esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 3 Theoretical Calculation of Ultimate Bearing Capacity
### Confinement Effect Coefficient  
In order to analyze the influence of the circular steel tube and CFRP, the confinement effect coefficient of the circular steel tube \(\xi_{s}\), the confinement effect coefficient of CFRP \(\xi_{cf}\), and the synthetical confinement effect coefficient \(\xi\) are adopted as follows:  
\[\xi_{s}=A_{s}f_{sy}/A_{c}f_{c} \tag{2}\]  
\[\xi_{cf}=A_{cf}f_{fu}/A_{c}f_{c} \tag{3}\]  
\[\xi=\xi_{s}+\xi_{cf} \tag{4}\]  
where \(A_{s}\), \(A_{c}\), and \(A_{cf}\) are the cross-sectional areas of the circular steel tube, concrete, and CFRP, respectively. \(f_{sy}\), \(f_{c}\), and \(f_{fu}\) are the yield strength of the circular steel tube, the concrete compression strength, and the ultimate tensile strength of CFRP, respectively.  
It can be seen from the formula that the influencing factors of the restraint effect coefficient of concrete-filled circular CFRP-steel middle long columns are the type of steel, the wall thickness of the steel tube, the number of layers of CFRP, the tensile strength of CFRP, and the strength grades of concrete.
## 3 Results and Discussion
### Effect of Coexisting Ions in Solution  
Due to the impact of production and other wastewater treatment processes, some industrial wastewater may contain lots of ions, e.g., Ca\({}^{2+}\), Na\({}^{+}\) and Cl\({}^{-}\)[29]. Therefore, ionic strength is also an important factor affecting the removal of Cr(VI). As shown in Figure 7, with an increase in the concentration of Ca\({}^{2+}\), Na\({}^{+}\) and Cl\({}^{-}\), the removal efficiency decreased from 90% to 60%. However, when the concentration was higher than 0.25 mol\(\cdot\)L\({}^{-1}\), the efficiency decreased slowly. In terms of Cl\({}^{-}\), these may be associated with the fact that the competitive adsorption of Cl\({}^{-}\) or ions produced by its hydrolysis on the active sites of FeBC interfered with the adsorption of Cr(VI) on FeBC. In terms of Ca\({}^{2+}\) and Na\({}^{+}\), the main reason may be the electrostatic interaction weakened between the surface charge of FeBC and Cr(VI) ions in the presence of cations [30].
## 3 Performance Analysis
### OP of the Secondary Network  
This sub-section calculates \(\text{OP}_{\text{SS}_{2}}\) and \(\text{OP}_{\text{SS}_{1}}\) as in Propositions 1 and 2 below. At first, plugging (10), (12), and (18)-(21) together, we can rewrite \(\text{OP}_{\text{SS}_{2}}\) and \(\text{OP}_{\text{SS}_{1}}\), respectively, as  
\[\text{OP}_{\text{SS}_{2}}=1-\underbrace{\Pr(X_{1}\geq\alpha_{1}X_{2}+\alpha_ {2}U+\alpha_{0},X_{2}\geq\alpha_{6}V_{2}+\alpha_{5})}_{\mathcal{I}_{2}}, \tag{36}\]  
\[\text{OP}_{\text{SS}_{1}}=1-\underbrace{\Pr(X_{1}\geq\alpha_{1}X_{2}+\alpha_ {2}U+\alpha_{0},X_{2}\geq\alpha_{4}U+\alpha_{3},X_{1}\geq\alpha_{6}V_{1}+\alpha _{5})}_{\mathcal{I}_{3}}, \tag{37}\]  
where \(X_{1}=g_{\text{SS}_{1}\text{SR}_{2}}=g_{\text{SR}_{2}\text{SS}_{1}}\), \(X_{2}=g_{\text{SS}_{2}\text{SR}_{2}}=g_{\text{SR}_{2}\text{SS}_{2}}\), \(U=g_{\text{PT}_{1}\text{SR}_{2}}\), \(V_{1}=g_{\text{PT}_{2}\text{SS}_{1}}\), \(V_{2}=g_{\text{PT}_{2}\text{SS}_{2}}\), and  
\[\alpha_{0}=\frac{\theta_{\text{Sth}}}{P_{\text{SS}_{1}}},\alpha_{1}=\frac{ \theta_{\text{Sth}}P_{\text{SS}_{2}}}{P_{\text{SS}_{1}}},\alpha_{2}=\frac{ \theta_{\text{Sth}}P_{\text{PT}}}{P_{\text{SS}_{1}}},\alpha_{3}=\frac{\theta_ {\text{Sth}}}{P_{\text{SS}_{2}}},\alpha_{4}=\frac{P_{\text{PT}}\theta_{\text{ Sth}}}{P_{\text{SS}_{2}}},\alpha_{5}=\frac{\theta_{\text{Sth}}}{P_{\text{SR}_{ 2}}},\alpha_{6}=\frac{P_{\text{PT}}\theta_{\text{Sth}}}{P_{\text{SR}_{2}}}.\]  
**Proposition 1**. : \(OP_{\text{SS}_{2}}\) _can be expressed by an exact closed-form expression as_  
\[\text{OP}_{\text{SS}_{2}}=1-\frac{\Omega_{\text{PTSR}}}{\Omega_{ \text{PTSR}}+\Omega_{\text{SS}_{1}\text{SR}}\alpha_{2}}\exp\big{(}-\Omega_{ \text{SS}_{1}\text{SR}}\alpha_{0}\big{)}\] \[\qquad\qquad\times\sum_{p=0}^{M-1}(-1)^{p}\frac{C_{M-1}^{p}M \Omega_{\text{SS}_{2}\text{SR}}}{(p+1)\Omega_{\text{SS}_{2}\text{SR}}+\Omega_{ \text{SS}_{1}\text{SR}}\alpha_{1}}\frac{\Omega_{\text{PTSS}_{2}}}{\Omega_{ \text{PTSS}_{2}}+\big{(}(p+1)\Omega_{\text{SS}_{2}\text{SR}}+\Omega_{\text{SS }_{1}\text{SR}}\alpha_{1}\big{)}\alpha_{6}}\] \[\qquad\qquad\times\exp\big{(}-\big{(}(p+1)\Omega_{\text{SS}_{2} \text{SR}}+\Omega_{\text{SS}_{1}\text{SR}}\alpha_{1}\big{)}\alpha_{5}\big{)}. \tag{38}\]  
Proof.
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
###### Abstract  
The intelligent damage detection of catenary insulators is one of the key steps in maintaining the safe and stable operation of railway traction power supply systems. However, traditional deep learning algorithms need to train a large number of images with damage features, which are hard to obtain; and feature-matching algorithms have limitations in anti-complex background interference, affecting the accuracy of damage detection. The current work proposes a method that combines deep learning and Zernike moment algorithms. The Mask R-CNN algorithm is firstly used to identify the catenary insulators to realize the region proposal of the insulators. After image preprocessing, the Zernike moment algorithm is used to replace the existing Hu moment algorithm to extract more detailed insulator contour features, then the similarity value and its standard deviation are further calculated, so as to complete the damage detection of the catenary insulator. The experimental results show that the mean average precision of insulator identification can reach 96.4%, and the Zernike moment algorithm has an accuracy of 93.36% in judging the damage of insulators. Compared with the existing Hu moment algorithm, the accuracy is increased by 10.94%, which provides a new method for the automatic detection of damaged insulators in catenary and even other scenarios.  
c 2022 12 5004 (T.L.); [EMAIL_ADDRESS]  
**Keywords:** catenary insulator; damage detection; deep learning; Mask R-CNN; Zernike moment
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 3 Reputation-Based Honest Blockchain Pool Mining
### Reputation Mechanism  
Reputation is defined as a comprehensive judgment on the behaviors of miners, which relates directly to the benefits/profits offered to miners. For crowdsourcing tasks in one chain, reputation mainly comes from local subjective and objective evaluation. Similarly, for complex task execution systems, in addition to local reputation, the evaluation integration of all main- and side-chains is also included.  
There are \(m(m\in\mathcal{M})\) miners joining in \(l(l\in\mathcal{L})\) mining pools. \(\mathcal{M}\) is the set of all miners, while \(\mathcal{L}\) represents the set of all pools. In blockchain, the PoW consensus mechanism rewards the first node to successfully publish the solution. The miners can then receive a reward based on the proportion of their volunteered computational resources. In the process of \(n(n\in N)\) times of pool mining rounds, rational miners take profit-driven actions to maximize their utility. To encourage honest actions, we suggest a reputation-based method by exploring historical behaviors and interactions. The interaction refers to the process of sending and receiving information, mainly including information plaintext, communication channel, receiver, transmission, etc. With regard to the blockchain network, it mainly refers to the process of obtaining blockchain broadcasts, publishing calculation results, accepting verification results, profit acquisition, etc.  
Honest behavior means that rational miners conduct mining independently according to protocols such as smart contracts and mining pool management rules. When the optimal participation decision/strategy is obtained through continuous strategy iteration optimization, it will not seek to actively change its behavior strategy in fear of compromising its own benefits. Dishonest behavior mainly refers to selfish malicious miners adopting malicious measures/attacking (e.g., block withholding attack, selfish mining, eclipse attack and stubborn mining) to destroy mining pools, in order to fraudulently obtain benefits beyond their contributions.For the honesty attribute, the behavior strategy/decision of miners is denoted as \(j\) is \(C_{j}=\{H,D\}(j\in\mathcal{M})\), where \(H\) and \(D\) represent the honest and dishonest behaviors, respectively. That is, while \(C_{j}=H\), the action of miner \(j\) would be judged and recorded as honest, and the reputation and survive times will be increased; otherwise, they will be updated accordingly. Let \(\mathcal{H}\) and \(\mathcal{D}\) be the sets of honest and dishonest miners, respectively.  
Next, the definition of reputation is given. Note that comprehensive reputation is mainly applicable to cross-chain mining, while local reputation mainly refers to the reputation obtained by pool mining on the current chain.
## 3 Problem Formulation & Terminology
### Threat Model & Assumptions  
We assume that aggressors can unleash network attacks that require the establishing and maintaining of _long-lived connections_ with the target system. Note that the term _connection_ is used loosely, not necessarily corresponding to TCP connections only, but rather it might include any type of network flows. Such attacks may aim at injecting spurious data or exfiltrating any piece of information from the target system, or simply DoS. An example of the latter is a low-rate attack that can cripple powerful servers by sending merely KB (if not less) of data. Henceforth, LDoS are to be treated as the main exemplar. In this context, works such as the ones in [1,22] studied LDoS extensively and attempted to provide a basic model of this family of attacks. Notice that although LDoS incidents may vary in terms of duration \(\mathcal{L}\) and volume intensity/transmission rate \(\mathcal{R}\), in practice all attempts tend to unfold in bursts, i.e., the malicious packets follow a normal distribution. Moreover, even at its peak volume, the malicious traffic corresponding to an LDoS is insignificant compared to the volume of the normal traffic. Figure 1 provides an example of the volume of malicious traffic across time corresponding to an LDoS attack. In real-life scenarios, any user interacts directly with a centralized component, i.e., the LB or a reverse proxy and not the actual servers. For this reason, we assume that the attacker has no visibility into the internal structure of the network and they have no means of monitoring the traffic inside the data center.  
Finally, a basic assumption of our approach is that the defenders have some _indicator of compromise_ of the system or pointers that some type of attack is unfolding. Particularly  
Figure 1: Model of traffic conditions during an LDoS attack.  
for LDoS such an indicator, might be that the application OS is unresponsive or that the communications are unusually slow. The reader should notice that the term _indicator_ is kept intentionally vague, as it typically is a highly empirical process that may in some cases rely on manual human input. For this reason, we assume that the simple action of checking is comparatively one of the most time-consuming processes and should be performed sporadically.
# Low-Complexity High-Throughput QC-LDPC Decoder for 5G New Radio Wireless Communication
## 2 5G New Radio LDPC Codes  
### Introduction  
According to ITU-R, there are three primary 5G NR use cases defined by the 3 GPP as part of its Study on New Services and Markets Technology Enablers (SMARTER) project. The three sets of use cases are [27]: eMBB, Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC).  
The initial phase of 5G deployments focuses on the eMBB use case. The eMBB traffic can be regarded as a direct extension from the 4G broadband service. The eMBB scenario is required to support a wider range of code rates, various code lengths, and modulation orders compared to the 4G Long Term Evolution (LTE). The eMBB offers peak data rates of 20 Gbps and provided user data rates of 100 Mbps to numerous users. Recently, LDPC codes have been selected as the coding scheme for the 5G eMBB data channel [14]. The NR access technology marks a great transition in channel coding for the 3 GPP cellular technologies [28]. This section summarizes the basic features of standard 5G QC-LDPCcodes. Furthermore, the construction procedures of the parity-check matrix of the target LDPC codes are also presented.
## 2 Methods
### Improved Calculation Method
#### 2.4.2 Joint Depth Metrics and Transformation Learning  
First, we complete the proximal explanation. We need to identify an equivalent and profound solution for \(M_{D}\).  
\(M_{D}\) could be solved exactly by a proximal operator, and it is related to transform learning with a metric \(Q\):  
\[M_{D}(x)=prox_{\lambda\
eq\emptyset}^{Q}(Fx-c) \tag{7}\]  
\[Q=D^{T}D+aI,F=Q^{-1}D^{T},c=Q^{-1}d \tag{8}\]  
Indeed, for every \(D\in R^{m*k},a\in R^{k},x\in R^{m}\); we have the following equation:  
\[L^{R}(D,a,x)=\frac{1}{2}\Big{(}\|x\|^{2}-2x^{T}Da+a^{T}\Big{(}D^{T}D+aI\Big{)} a\Big{)}+\lambda\psi(a)+d^{T}a=\]  
\[L^{R}(D,a,x)+\frac{1}{2}\Big{(}\|x\|^{2}-\|Fx\|_{Q}^{2}-\|c\|_{Q}^{2}\Big{)}+x ^{T}Dc \tag{9}\]Obviously, computing the proximity operator is equivalent to determining the optimal sparse representation of \(x\in R^{m}\).  
\[M_{D}(X)=\underset{a\in R^{k}}{\text{argmin}}L^{R}(D,a,x)=prox_{\lambda\psi}^{Q}( Fx-c) \tag{10}\]  
Next, we proceed to the multi-layer representation. Deep dictionary learning models are able to represent more comprehensively and concisely:  
\[\hat{y}=\phi\circ A^{s+1}\circ prox_{\lambda\psi_{s}}^{Q^{(s)}}\circ A^{(s)} \circ prox_{\lambda\psi_{s-1}}^{Q^{(s-1)}}\circ\ldots\circ prox_{\lambda\psi_ {1}}^{Q^{(1)}}\circ A^{(1)}\Big{(}x^{(0)}\Big{)} \tag{11}\]  
The affine operators \(A^{(r)}\) mapping \(x^{r-1}\in R^{k_{r-1}}\) to \(z^{(r)}\in R^{k}\), \(1\leq r\leq s\), by a shift term \(c^{(r)}\), by an analysis transform \(W^{(r)}\), it has the following representation:  
\[\forall r\in\{1,\ldots,s\},A^{(r)}:R^{k_{r-1}}\to R^{k_{r}},z^{(r-1)}\mapsto W ^{(r)}z^{(r)}-c^{(r)} \tag{12}\]  
With \(k_{0}=m_{1},W^{(1)}=F^{(1)},\forall r\in\{2,\ldots,s\}\),  
\[W^{(r)}=F^{(r)}p^{(r-1)},\] \[W^{(s+1)}=Cp^{(s)}\] \[\forall r\in\{1,\ldots s\},\] \[Q^{(r)}=\Big{(}D^{(r)}\Big{)}^{T}D^{(r)}+aI,\] \[F^{(r)}=\Big{(}Q^{(r)}\Big{)}^{-1}\Big{(}D^{(r)}\Big{)}^{T},\] \[c^{(r)}=\Big{(}Q^{(r)}\Big{)}^{-1}d^{(r)}. \tag{13}\]  
Equations (11) and (12) show that our model is feasible and widely used. This effect can be achieved because we introduce some quantitative and qualitatively different measures for these operators. In FNN, the activation function is called the proximity operator of a convex function [76]. Next, it is time to adopt a method for efficient learning.
## 3 Results and Discussion
The estimated amount of fresh water produced during eight hours of operation using the algorithm established for the regulation of the third configuration of the HD solar desalination system is shown in Figure 13. The same figure shows the amount of fresh water obtained under the same working conditions, but without regulation. The difference between the two estimated quantities to be collected is apparent, suchthat the gain derived using regulation is around 33%. Concerning the daily fresh water production amounts for the managed system when compared with other HD solar systems [20; 21; 22], their volume run from 6 to 15.2 L/day, is a promising rate.
## 5 Application to General Examples
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Example**} & \multirow{2}{*}{**State Function**} & \multicolumn{2}{c}{**Probability of**} & \multirow{2}{*}{**MCS \({}^{*}\)**} & \multirow{2}{*}{**Subset Simulation**} & \multirow{2}{*}{**KDE**} & \multirow{2}{*}{**Combined KDE and GPD**} \\  & & & **Failure (\(p_{f}\))/Reliability** & & & & \\  & & **Index (\(\beta\))** & & & & \\ \hline I & \(g(\)X\()=X_{1}+2\) & \(p_{f}\) & 0.0227 & 0.0229 & 0.0229 & 0.0229 \\  & \(X_{1}\sim N(0,1)\) & \(\beta\) & 2.00 & 2.00 & 2.00 & 2.00 \\ \hline II & \(g(\)X\()=\(\frac{X_{1}+X_{2}}{\sqrt{2}})+3\) & \(p_{f}\) & 0.0014 & 0.0014 & 0.0014 & 0.0015 \\  & \(X_{1}\sim N(0,1)\) & \(\beta\) & 2.99 & 2.99 & 2.99 & 2.97 \\ \hline III & \(g(\)X\()=X_{1}\cdot X_{2}+7\) & \(p_{f}\) & \(1.43\times 10^{-4}\) & \(1.33\times 10^{-4}\) & \(1.43\times 10^{-4}\) & \(1.28\times 10^{-4}\) \\  & \(X_{1}\sim N(0,1)\) & \(\beta\) & 3.63 & 3.65 & 3.63 & 3.66 \\ \hline \hline \end{tabular} \({}^{*}\)Number of samples for MCS = \(10^{6}\).  
\end{table} 
Table 1: Probability of failure (\(p_{f}\)) and reliability index (\(\beta\)) when Monte Carlo simulation (MCS), subset simulation, kernel density estimation (KDE), and the combination of KDE and generalized Pareto distribution (GPD) are applied for examples I, II and III. approximately equal to 3.0. In addition, the \(p_{f}\) and \(\beta\) computed using the four methods were not significantly different in example III, as shown in Table 1.
## 3 Public Data and Modeling Experiments  
### Public Data Description  
The dataset is obtained from Kaggle (accessible at [[https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset](https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset)]([https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset](https://www.kaggle.com/datasets/drganeshkumars/flank-wear-of-cnc-lathe-tool-insert-dataset)), accessed on 17 January 2025), documenting the flank wear of CNC lathe tool inserts. The preprocessed dataset can also be directly accessed via this URL (accessed on 10 May 2025): [[https://github.com/567](https://github.com/567) ZYC/Data-Processing-and-Explanation-for-Flank-Wear-of-CNC-Lathe-Tool-Insert-Dataset-]([https://github.com/567](https://github.com/567) ZYC/Data-Processing-and-Explanation-for-Flank-Wear-of-CNC-Lathe-Tool-Insert-Dataset-).  
Flink wear, a detrimental phenomenon in single-point cutting processes, arises from adhesion and abrasion when the cutting tool contacts the workpiece. It is measured by distinguishing geometric relationships in rake face images between new and worn tools.  
Flink wear of lathe tools impacts machining quality: it degrades surface finish, increases tool replacement frequency, reduces machining efficiency, and may cause dimensional errors affecting overall precision. Tool wear is primarily influenced by the feed rate, depth of cut, and cutting speed during turning, as summarized in Table 3. Establishing mathematical relationships between these physical variables and flank wear serves as the foundation for intelligent optimization of cutting parameters.  
The dataset contains 2001 samples without missing values. However, 45 samples were incorrectly recorded as negative or excessively large values (physically meaningless for flank wear measurement) and were thus removed, leaving 1956 samples for experiments. The training and test sets were divided at a 1:1 ratio.
## 4 Discussion
### Future Directions
#### 4.5.4 Speech-to-Text and Interactive Innovations  
Future enhancements also include speech-to-text (STT) capabilities, enabling practitioners to record and process doctor-patient interactions more interactively. Coupled with chain-of-thought features, STT integration will support the automatic generation of structured summaries or patient notes, further optimizing clinical workflows.
## 3 Experimental Results and Discussion
### Comparison of Our Proposed Method with the Existing Method
Our proposed SFFS with RF (SFFS-RF) model produced higher accuracy compared to SFFS with SVM (SFFS-SVM) to classify adult and child based on their handwritten text and handwritten pattern.
## 3 Material and Methods  
Using a geospatial analysis technique, the geomorphic signatures over the study area basin between Al Faraah and Heelan along the eastern flank of the Red Sea were assigned.  
Figure 3: (**a**) Digital elevation model and (**b**) slope maps showing the general elevations and slopes of the study basin landscape.  
This method is applied by using digital elevation models (DEM) with a 30 m resolution obtained from a Shuttle Radar Topography Mission (STRM). The basin's drainage systems were extracted using the hydrology tools in ArcGIS 10.4 software, which allowed for the classification of the basin into 42 sub-basins with various geometries. These 42 sub-basins were automatically delineated using ArcGIS software's hydrology tools and watershed extraction method, considering the drainage systems that cover the entire area of the study basin. Figure 4 illustrates the different procedures and processes that the technique goes through. This study inferred geomorphological signatures using a variety of geomorphic indices to provide a model that suggests the relative tectonic activity of the study basin. Whereas some geomorphic indices are determined by analyzing the mountain fronts, others are calculated based on drainage analysis [7; 19]. Geomorphic analysis studies may record different anomalies along mountain fronts or drainage streams [1; 3; 19; 25]. These anomalies can be observed by local variations in relative tectonic activity that result in uplift or subsidence. In this study, several indices were applied to various sub-basins and based on the ranges of values for each indicator, and the sub-basins were classified into three distinct tectonic classes or levels. Following that, these indices were computed, averaged, and split arbitrary into three groups, each of which represented a different degree of relative tectonic activity (Rta). At the conclusion of the Methodology Section, a detailed explanation of the calculation of the Rta index is provided.  
Figure 4: A flowchart that shows the methodology that was applied.
## 4 User Testing Methodology
### Testing Environment  
A simulated operational setting was utilized to test the VA, designed to mimic its intended deployment within a university's student office. Participants used a client application developed with the Next.js framework in this environment, providing an intuitive interface for submitting queries and viewing responses. Each query was handled by the client application as an independent interaction, ensuring that no residual data from previous queries influenced subsequent responses. The setup was designed to enable the VA to autonomously address each question, focusing on the current input and maintaining a \"stateless\" approach to maximize response clarity.  
The backend of the VA system was powered by a server application based on Flask, which was responsible for managing query processing and facilitating communication with the VERTEX AI model. This server handled the RAG method, with relevant contextual data being retrieved from a PostgreSQL database that was enhanced with pgvector for vector-based querying. The VA leveraged the RAG methodology to integrate contextual information when necessary, resulting in more accurate and relevant responses. The testing environment was designed to assess the performance of the VA under realistic operational conditions, examining its ability to manage user queries and deliver coherent responses seamlessly.