esa-sceva/satcom-chunk-collection huggingface dataset:
# Flow Sensing-Based Congestion Detection for D2D Streaming on a 5G gNB
## 3 System Model
### Detecting Link Flow Errors
#### 3.2.2 Detecting Link Overflow Errors  
The caching rate is detected to prevent flow error due to mismatched decoding rates. To detect whether \(DUE_{buffer}(x)\) suffers from a link overflow error, the flow rate at the decoder buffer is assigned first. The congestion due to link overflow error occurs if the uplinked flow rate is larger than the actual caching rate in the buffer cache. Equation (7) defines \(LC_{overflow}(x)\) to prevent the link congestion due to link overflow error.  
\[LC_{overflow}(x)=\sum_{x=0}^{k}\left[\left\{\frac{f(x-t+s(x)}{CBW(x-1)} \right\}+\left\{D_{r}(k)-Burst(x)\right.\right] \tag{7}\]  
where \(D_{r}(k)\) is the decoding rate for \(DUE_{buffer}(x)\).  
We can observe that a link overflow error occurs if \(E_{r}(k)>D_{r}(k)\), whereas a link underflow error occurs if \(E_{r}(k)<D_{r}(k)\). Detecting and controlling link flow errors with source rates is difficult.
## 5 Conclusions  
1. The transverse cofferdam of Xiangjiaba hydropower station was divided into six regions based on its structural features, which consisted of masonry, PC, and RCC. The maximum explosives charge weight per delay in the masonry structure, PC, and RCC above water were 26.4 kg, 28 kg, and 28.5 kg. The \(PPV\) reached 8.741 cm/s, 4.479 cm/s, and 3.878 cm/s, respectively.
2. The attenuation law of \(PPV\) land blasting was revealed in different structures through the blast-induced vibration data, and then the prediction formulas of explosives charge weight per delay in PC and RCC structures were put forward combining [PERSON]'s third law.
3. Considering permissible blast-induced vibration values of adjacent structures in Xiangjiaba hydropower station, the maximum permissible explosives charge weight per delay in PC and RCC underwater structures were calculated by the prediction formulas, which were 67 kg and 27 kg, respectively.
4. The actual explosives per delay in PC and RCC underwater structures were 54 kg and 23.4 kg, respectively, where the \(PPV\) was 7.65 cm/s and 4.21 cm/s. Both of them were less than the maximum allowable values. Simultaneously, the prediction formulas were proven to meet the blast requirements by the analysis of MAPE. Consequently, the \(PPV\) underwater blasting could be predicted by analyzing the \(PPV\) of land blasting in same structure, which provided the basis for the design of underwater blasting parameters. A reliable method for cofferdam demolition in hydropower stations was proposed, which provided a reference for similar projects.  
Conceptualization, C.L.; methodology, Y.M.; formal analysis, Y.M. and X.Z.; resources, Y.M.; data curation, J.Z.; writing--review and editing, Y.M.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51804114.  
The authors declare no conflict of interest.
## 2 Methods
### Optimization Method
#### 2.4.2 Velocity Filtering  
After the normal velocities have been extrapolated into the entire design domain, a filter step is then performed. The filter is applied separately for the two normal velocity fields. The aim of filtering is to smooth the velocity field in order to stabilize the optimization process. The filter used is the one described in [34], which is based on the Helmholtz-PDE and is well known in the field of homogenization-based topology optimization. The filter equation (Helmholtz PDE) is [34]:  
\[-r_{f}^{2}\
abla^{2}\vartheta^{n}+\vartheta^{n}=v^{n}. \tag{15}\]  
\(\tilde{\vartheta}^{n}\) is the filtered normal velocity field that results as the solution of Equation (15). The filter parameter \(r_{f}\) is a length parameter that describes the size of the filter mask but not its explicit radius [34].  
To solve Equation (15), its weak form is generated. The building blocks from the evaluation of the system response are used for the evaluation of the weak form. The weak form is solved on the LS mesh with homogeneous Neumann boundary conditions \(\frac{\partial\vartheta^{n}}{\partial n}=0\) and the unfiltered normal velocity field \(v^{n}\) as the right-hand side [34].  
The advantages of this filter approach over explicit filters are the reuse of the building blocks, including their parallelization and the automatic consideration of non-convex design spaces (holes, undercuts) [34].
# Enhancing Saga Pattern for Distributed Transactions within a Microservices Architecture
## 3 Proposed Approach: Design and Components
### Components of the Proposed Approach
#### 3.2.3 Quota Cache  
A cache in computing is generally a hardware or a software component that stores data. It is known as a high-speed data storage layer. A cache increases the performance of data retrieval by reducing main memory access. The quota cache (see Figure 5) is the quota of a specified resource from the main database. Quota is a feature that estimates the available bytes (i.e., the amount of available space) to store contents.  
To resolve the read-isolation property in the saga pattern, in-memory data caching is utilized. A quota from the main database will be allocated to the memory cache server. The CRUD (create, read, update, and delete) tasks will be handled via quota cache. This in turn will never cause a wrong commit to the main database. In addition, the microservices which apply the quota cache will also benefit from the in-memory operations to ensure low latency and high throughput and thus achieve better performance in comparison to the baseline standard saga.  
As shown in Figure 3, microservices that require validation apply the quota cache as exceptions may occur when the validation fails. When a microservice fails to be completed, the other microservices will run compensation transactions to rollback the changes. Assuming that an error occurs from the Warehouse-Service when fetching the goods, in the standard saga pattern, initial good fetching will cause updating of the main database; thus, a customer could see the created order. However, when the compensation transaction is run to rollback the changes, the order will be removed in the next few seconds. In contrast, with the enhanced version, the CRUD operations are moved to the cache level instead of the main database level. This will not cause a wrong database commit or update. The compensation request will be sent to the corresponding message queue middleware in the case of an error. Several message queue middleware were utilized to handle different events, including: the buy events, the completion events, and the failure events. For instance, the request will send to the warehouse-compensate queue if an error occurs in either the Warehouse-Service or the Billing-Service. Moreover, the request will be sent to the payment-related queue if an error occurs in the Billing-Service. As a result, with the enhanced saga pattern, database commit will only be performed when the transaction is completed successfully.  
Redis [22] was utilized as our memory cache server. Redis is an open-source database, with an in-memory data store. Redis is suitable for building low latency in-memory cache, and also can increase the throughput. Several well-known companies, such as Twitter, GitHub, and StackOverflow, have applied Redis.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Supervised Methods
#### 3.1.5 Gradient Boosting  
The gradient boosting (GB) method is an ensemble method like RF that uses DTs. [91]. Unlike RF, which trains multiple trees in parallel, GB trains one tree at a time, where each tree learns from the mistakes of its predecessor, so that the next tree adjusts what the previous one failed to capture [59; 91; 92]. The main disadvantage of this method is its susceptibility to overfitting, while its main advantage is its robustness against imbalanced data [91].
## 4 Stress Strength Reliability and Information Measures
### Information Measures  
Entropy measures the presence of uncertainty or variability in a random variable. The higher the entropy number, the more uncertain the data are. This section focuses on determining the UEHLD expression for various entropy measurements. The Re entropy of a random variable \(T\) is mathematically specified by:  
\[\mathbb{R}(\zeta)=\left(1-\zeta\right)^{-1}\log\left(\int\limits_{0}^{\infty} (g(t))^{\zeta}dt\right),\zeta\
eq 1,\zeta>0. \tag{13}\]  
Substituting PDF (3) in (13) and using binomial expansion, then the Re entropy of the UEHLD is  
\[\mathbb{R}(\zeta)=\left(1-\zeta\right)^{-1}\log\left(\sum\limits_{q=0}^{ \infty}\left(-1\right)^{q}\binom{\zeta(\varphi+1)+q}{q}(2\varphi\delta)^{ \zeta}\int\limits_{0}^{1}t^{\zeta(\delta-1)+\delta q}(1-t^{\delta})^{\zeta( \varphi-1)}dt\right).\]Suppose that \(y=t^{\delta}\Rightarrow dy=\delta t^{\delta-1}dt\), then \(\mathbb{R}(\zeta)\) obtains the form:  
\[\mathbb{R}(\zeta)=(1-\zeta)^{-1}\log\Biggl{(}\sum_{q=0}^{\infty} (-1)^{q}\binom{\zeta(\varphi+1)+q}{q}(2\varphi)^{\zeta}\delta\zeta^{-1}\mathbb{ B}\biggl{(}\zeta-\frac{\zeta}{\delta}+q+\frac{1}{\delta},\zeta(\varphi-1)+1 \biggr{)}\Biggr{)}.\]  
The Havrda and Charvat entropy measure of the UEHLD is given by:  
\[\begin{array}{ll}\hbar(\zeta)&=\frac{1}{2^{1-\zeta}-1}\biggl{[} \Bigl{(}\int_{0}^{1}(g(t))^{\zeta}dt\Bigr{)}^{\frac{1}{\zeta}}-1\biggr{]}, \zeta\
eq 1,\zeta>0\\ &=\frac{1}{2^{1-\zeta}-1}\Biggl{[}\Biggl{(}\sum_{q=0}^{\infty} (-1)^{q}\binom{\zeta(\varphi+1)+q}{q}\biggl{)}(2\varphi)^{\zeta}\delta\zeta^{ -1}\mathbb{B}\Bigl{(}\zeta-\frac{\zeta}{\delta}+q+\frac{1}{\delta},\zeta( \varphi-1)\Bigr{)}\Biggr{)}^{\frac{1}{\zeta}}-1\Biggr{]}.\end{array}\]  
The Tsallis entropy of the UEHLD is calculated as follows:  
\[\begin{array}{ll}\aleph(\zeta)&=\frac{1}{\zeta-1}\Bigl{[}1-\int_{0}^{1}(g(t ))^{\zeta}dt\Bigr{]},\zeta\
eq 1,\zeta>0\\ &=\frac{1}{\zeta-1}\Biggl{[}1-\Biggl{(}\sum_{q=0}^{\infty} (-1)^{q}\binom{\zeta(\varphi+1)+q}{q}\biggl{)}(2\varphi)^{\zeta}\delta\zeta^{ -1}\mathbb{B}\Bigl{(}\zeta-\frac{\zeta}{\delta}+q+\frac{1}{\delta},\zeta( \varphi-1)\Bigr{)}\Biggr{)}\Biggr{]}.\end{array}\]  
Table 2 gives some numerical values of \(\mathbb{R}(\zeta),\hbar(\zeta),\) and \(\aleph(\zeta)\) for some selected parameter values.  
We conclude from Table 2 that, when the value of \(\zeta\) increases, all entropy measures decrease, resulting in greater information. When the value of \(\delta\) rises, for the same value of \(\varphi\), we conclude that the \(\mathbb{R}(\zeta),\)\(\hbar(\zeta),\) and \(\aleph(\zeta)\) measures decrease, implying that there is less variability. Furthermore, we infer that the \(\mathbb{R}(\zeta),\)\(\hbar(\zeta),\) and \(\aleph(\zeta)\) measurements decrease as the value of \(\varphi\) rises, for the same value of \(\delta\), implying decreased variability.
# Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron
## 6 Simulation Setup, Evaluation Criteria, Dataset, Results and Discussion
### Results and Discussion  
Using Equations (4)-(6), we performed the comparison of different classification models by means of ROC (receiver operating characteristics) curve using Weka tool version 3.9.3. The performance confusion matrix of the proposed MLP classification model is shown in Figure 11 and ROC curves of proposed MLP classification model is depicted in Figure 12. A clear comparison of different classifiers with our proposed MLP classifier is shown in Figure 13a-i. We calculated the accuracy of our proposed MLP classification model with Naive Bayes, Decision Stump, Logistic Model Tree, Naive Bayes Updateable, Naive Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier Optimizer, and OneR, as shown in Figure 14.  
Figure 14: Accuracy comparison of proposed MLP classifier with other classification models.  
Figure 13: ROC curves showing comparison of proposed MLP classifier with counterparts.  
It is clear from the results shown in Table 8 that MLP classifier outperforms all other classification models with an efficiency of 98.99%. Using our proposed MLP classifier, we can quickly recognise DDoS assaults at the application level. We are able to identify between legitimate clients and attackers using the proposed MLP classifier. Some of the presumed IP addresses, meanwhile, do not fit the mould of a typical client or an attacker. In this study, we tested the effectiveness of our proposed technique by using it to identify attacks in real-world DDoS attack datasets including CTU-13 (2011), our company's web logs (2019), and our own dataset. The detection accuracy analysis of ten classifiers is shown in Figure 14.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 4 Proposed System
### System Workflow  
The system involves two categories of users: the image owner and the image consumer. The proposed system facilitates interactions between these two user categories to enable the secure sharing of facial data in images. The image owner is responsible for encrypting images, granting access to selected consumers, and revoking previously granted access. The image consumer can request access to protected facial regions and view them upon the image owner's approval.  
Furthermore, the system supports multiple image consumers (e.g., [PERSON], [PERSON], and [PERSON]) requesting access to the same image. The image owner (e.g., [PERSON]) retains full control over access permissions and can manage each relationship independently. For instance, [PERSON] may grant access to [PERSON] and [PERSON] while denying it to [PERSON]. Additionally, [PERSON] can revoke access previously granted to [PERSON] while maintaining [PERSON]'s access. This granular control ensures that the system restricts unauthorized users to non-facial areas of the image while permitting only authorized users to view protected facial regions.  
These interactions are made possible through the collaboration of the system's primary components, as previously described: the client application, the server, and the CSP. Together, these components ensure the secure sharing of facial data by managing user interactions, processing requests, and performing cryptographic operations.  
The following sections provide a detailed description of these interactions, as outlined in the system workflow for key operations: user registration, face encryption, face decryption by owner, key splitting, and face decryption by consumer. Each operation is illustrated in a step-by-step process, detailing the interactions between the system components and the roles of the image owner and image consumer.
## 3 Results and Discussion
### The Impact of Amendments on Soil Physicochemical Properties and the Bioavailability of Cu
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multirow{2}{*}{**pH**} & **EC** & **SOM** & **CEC** & **Eh** \\ \cline{3-5}  & & **(mS cm\({}^{-1}\))** & **(g kg\({}^{-1}\))** & **(mol kg\({}^{-1}\))** & **(mv)** \\ \hline CK & 3.78 \(\pm\) 0.45 c & 858 \(\pm\) 36.1 c & 5.85 \(\pm\) 0.05 d & 17.2 \(\pm\) 0.45 d & 509 \(\pm\) 6.06 a \\ L1 & 5.17 \(\pm\) 0.17 b & 1005 \(\pm\) 73.4 bc & 14.5 \(\pm\) 0.27 a & 30.6 \(\pm\) 0.82 a & 492 \(\pm\) 3.38 b \\ L2 & 6.76 \(\pm\) 0.04 a & 1241 \(\pm\) 47.2 a & 9.90 \(\pm\) 0.01 c & 28.2 \(\pm\) 0.26 b & 417 \(\pm\) 1.70 c \\ L3 & 6.86 \(\pm\) 0.05 a & 1150 \(\pm\) 34.9 ab & 12.5 \(\pm\) 0.45 b & 23.2 \(\pm\) 0.51 c & 403 \(\pm\) 2.80 d \\ \hline \hline \end{tabular} Note: different letters indicate obvious differences between treatment groups for the same index (\(p<\) 0.05). The data are mean \(\pm\) SD, n = 3. EC, electrical conductivity; SOM, soil organic matter; CEC, cation exchange capacity; Eh, redox potential. CK: the control soil devoid of any amendment. L1: vermicomopost amendment; L2: steel slag amendment; L3: a combination of vermicomopost and steel slag amendment.  
\end{table} 
Table 2: Basic physicochemical properties of soils under different treatments. Figure 3: The total Cu concentration, available Cu concentration, and bioavailability of Cu in soil under different treatments. Note: The bars sharing the different letters suggest significant differences (\(p<\) 0.05). The data are mean \(\pm\) SD, n = 3. CK: the control soil devoid of any amendment. L1: vermicomopost amendment; L2: steel slag amendment; L3: a combination of vermicomopost and steel slag amendment. The same as below.
# Carousel Greedy Algorithms for Feature Selection in Linear Regression
## 1 Introduction  
The carousel greedy algorithm (CG) is a generalized greedy algorithm that seeks to overcome the traditional weaknesses of greedy approaches. A generalized greedy algorithm uses a greedy algorithm as a subroutine in order to search a more expansive set of solutions with a small and predictable increase in computational effort. To be more specific, greedy algorithms often make poor choices early on and these cannot be undone. CG, on the other hand, allows the heuristic to correct early mistakes. The difference is often significant.  
In the original paper, [PERSON] et al. (2017) [1] applied CG to several combinatorial optimization problems such as the minimum label spanning tree problem, the minimum vertex cover problem, the maximum independent set problem, and the minimum weight vertex cover problem. Its performance was very encouraging. More recently, it has been applied to a variety of other problems; see Table 1 for details.  
CG is conceptually simple and easy to implement. Furthermore, it can be applied to many greedy algorithms. In this paper, we will focus on using CG to solve the well-known linear regression problem with a cardinality constraint. In other words, we seek to identify the \(k\) most important variables, predictors, or features out of a total of \(p\). The motivation is quite straightforward. Stepwise regression is a widely used greedy heuristic to solve this problem. However, the results are not as near-optimal as we would like. CG can generate better solutions within a reasonable amount of computing time.  
In addition to the combinatorial optimization problems studied in [PERSON] et al. (2017) [1], the authors also began to study stepwise linear regression. Their experiments were modest and preliminary. A pseudo-code description of their CG stepwise linear regression approach is provided in Algorithm 1. For the problem to be interesting, we assume that the number of explanatory variables to begin with (say, \(p\)) is large and the number of these variables that we want in the model (say, \(k\)) is much smaller.  
The experiments were limited, but promising. The purpose of this article is to present a more complete study of the application of CG to linear regression with a constraint on the number of explanatory variables. In all of the experiments in this paper, we assume a cardinality constraint. Furthermore, in the initial paper on CG, [PERSON] et al. (2017) [1] worked with two datasets for linear regression and also assumed a target number of explanatory/predictor variables. This is an important variant of linear regression and it ensures that different subsets of variables can be conveniently compared using RSS.
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 2 Method
### Filtering of Yield Data  
Before generating surfaces and yield estimation models, the yield data were filtered for each area to eliminate outliers. A previously described method based on the Density-Based Spatial Clustering of Applications with Noise algorithm [18] was used for filtering. In addition, null yield data, plot border points, transition lines from one treatment to another, and locations where yield was within a confidence interval of \(+/-\) three standard deviations from the average yield of the area were excluded. The means, variation coefficients, minimum and maximum values, and grain yield asymmetries were determined to verify the normality of the measurements using Minitab version 19.2020 statistical software.
# Fabric Defect Detection Based on Improved Lightweight YOLOv8n
## 4 Conclusions  
Based on the multi-scale characteristics of fabric defects, the dataset was preprocessed to enhance the model's generalization ability. Building on the YOLOv8 model, we applied the concept of Ghost convolutions to improve the YOLOv8 backbone network. Specifically,  
Figure 9: (**a**) Precision–Recall curves of GSL-YOLOv8; (**b**) Precision–Recall curves of YOLOv8; (**c**) Recall–Confidence curves of GSL-YOLOv8; (**d**) Recall–Confidence curves of YOLOv8.  
we replaced the standard convolutions (Conv) in YOLOv8 with GhostConv and modified the C2f module to C2 fGhost. This approach maintained model accuracy while reducing its complexity. Additionally, we integrated the parameter-free attention mechanism SimAM at the end of the backbone network, which enhanced the model's ability to integrate multi-scale information.  
The YOLOv8 detection head was upgraded to the newly designed LSCDH, which utilized the concept of shared convolutions to merge and adjust channel numbers of features. This combination of low-level detail and high-level semantic information boosts the model's feature extraction capabilities while ensuring its lightweight nature, making it suitable for deployment on mobile devices. Ablation experiments validated the effectiveness of each module in our proposed GSL-YOLOv8n algorithm. Compared to the YOLOv8n algorithm, the improved GSL-YOLOv8n model reduced model size, computational complexity, and parameter count by 66.7%, 58.0%, and 67.4%, respectively. This resulted in a lightweight model with enhanced small target defect feature extraction and anti-interference capabilities while maintaining detection speed, achieving an [EMAIL_ADDRESS] of 98.29%.  
In the future, we plan to integrate this model into resource-constrained embedded devices, exploring the application of lightweight models for fabric defect detection in environments with limited computing resources.  
Conceptualization, S.M. and Y.L.; methodology, S.M. and Y.L.; software, Y.L.; validation, Y.L.; formal analysis, Y.L.; investigation, Y.L.; resources, S.M.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, S.M.; visualization, Y.L.; supervision, S.M.; project administration, Y.Z.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Program of the National Natural Science Foundation of China (62103309).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# Design Considerations of Multi-Phase Buck DC-DC Converter
## 4 Analytical and Simulation Study of Power Losses in an 8-Phase Synchronous Buck Converter
### Simulation Study of Power Losses in 8-Phase Synchronous Buck Converter  
To verify the considered design methodology, a simulation using _PSpice for TI_ of a multi-phase synchronous buck converter has been performed with the following input data: \(Ulin=12\) V, \(U_{0}=3.3\) V, \(f_{SW}=200\) kHz, \(I_{0}=200\) A, \(t_{d}=100\) ns. Based on the data presented in Table 6, a configuration circuit with \(N=8\) has been selected. The MOSFET CSD17581Q3A (\(Q1,Q2\)) built into the simulator libraries has been used to perform the simulations.  
The comparison results between numerical calculations and those obtained from simulations are summarized in Table 7.  
By analyzing the results from Table 7, it can be concluded that the relative error \(\delta\) between analytical and simulation results does not exceed 4%.