esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Different Base Materials on the Microstructure and Properties of Clad Rebar
## 4 Conclusions  
1. The minimum cladding thickness in the four types of rebars occurred at the root of the transverse rib. The 20 MnSiV/316L rebar had the smallest range and variance of cladding thickness, which were 1.34 and 0.11 mm, respectively. This was because 20 MnSiV and 316L have similar stress-strain curves.
2. As the carbon content in the base layer increased from 0.22% to 0.53%, the amount of chromium carbide (C15.58 Fe7.42 Cr6) at the stainless-steel side increased notably, and the morphology transformed from disconnected grains to continuous flakes. The diffusion distances of carbon and chromium increased from 35 and 25 um to 80 and 40 um, respectively.
3. The bonding strength of the rebar composite interface depended on not only the amount of chromium-rich carbides, but also on the grain size in all the microstructures of the composite interface. The lower the amount of chromium-rich carbides in the interface, the higher the bonding strength is. The larger the grain size in the interface, the lower the bonding strength in the interface is.
4. After the salt spray corrosion experiments, the 55#/316L rebar composite interface exhibited a corrosion depth that was three times that of the 20 MnSiV/316L rebar and a corrosion rate that was approximately twice that of the 20 MnSiV/316L rebar. This was because of the large amount of chromium-rich carbide precipitated at the composite interface of the 55#/316L rebar, which led to a reduced chromium content in the interface and lower corrosion resistance.
5. To ensure the clad layers in the rebars were uniformly distributed in thickness and had satisfactory mechanical properties as well as corrosion resistance, materials with a low carbon content (Cwt \(\leq\) 0.25%), high strength, and a stress-strain curve similar to that of the clad layer were chosen for the base layer of the clad rebar.  
Conceptualization, Z.L.; methodology, Z.L. and J.T.; software, Z.L.; validation, Z.L., L.Z. and J.T.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L. and J.T.; data curation, Z.L. and Z.Z.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L. and J.T.; visualization, Z.L.; supervision, Z.L.; project administration, Z.L., J.T., Y.X. and X.Q.; funding acquisition, J.T., Y.X. and X.Q. All authors have read and agreed to the published version of the manuscript. This research was funded by the key R & D project of Guangxi Zhuang Autonomous Region (2021 AB16016) and the science and technology plan project of Liuzhou City (2021 AA0101B001). Special thanks to Liuzhou Iron and Steel Group and Hunan Santai New Materials Co. The authors declare no conflict of interest.
# Research on Personalized Course Resource Recommendation Method Based on GEMRec
## 2 Related Work  
### Recommendation Models  
In the field of recommendation systems, traditional methods include collaborative filtering, content-based recommendations, and hybrid recommendations. Collaborative filtering [1], as one of the most common recommendation algorithms, relies on analyzing relationships between users and items, recommending based on similar users' behaviors. However, this method is limited by cold-start and data sparsity issues. Content-based recommendation systems [2] primarily recommend similar items by analyzing item features, but they struggle to capture users' latent interests. Although [PERSON] et al. [3] improved recommendation systems' content understanding in specific domains by incorporating multiple classifications and scoring mechanisms, limitations remain in broader applications. To overcome these limitations, hybrid recommendation systems [4] combine collaborative filtering with content-based methods to enhance recommendation accuracy. For instance, [PERSON] et al. [5] developed a hybrid recommendation system capable of dynamically adapting to changing user preferences, suitable for online learning platforms, while [PERSON] et al. [6] addressed cold-start and data sparsity issues through hybrid filtering algorithms.  
In recent years, deep learning has achieved significant advances in recommendation systems, particularly in neural collaborative filtering (NCF), attention mechanisms, and sequential recommendation models. NCF enhances recommendation accuracy by mining complex nonlinear interactions between users and items through deep neural networks. [PERSON] et al. [7] improved a music recommendation system by combining traditional collaborative filtering with NCF, significantly boosting recommendation accuracy. Furthermore, the application of attention mechanisms in recommendation systems has gained widespread recognition, as it dynamically adjusts weights between users and items to improve model precision. [PERSON] et al. [8] proposed an attention model based on graph neural networks (GNNs) that effectively captures complex user-item relationships, while [PERSON] et al. [9] introduced a variational self-attention mechanism for a new sequential recommendation model, addressing the issue of uncertainty in user preferences. Sequential recommendation models focus on the dynamic evolution of user interests; for instance, [PERSON] et al. [10] combined long-term and short-term preferences in their Long-Short Term Self-Attention Network (LSSA), significantly improving recommendation accuracy. [PERSON] et al. [11] proposed the Time Interval Aware Self-Attention Model (TiSASRec), further enhancing the real-time accuracy of recommendations.  
Although hybrid recommendation and deep learning methods have improved recommendation performance to some extent, the limitation of single-modality methods remains insufficient to fully capture users' multidimensional interests and needs. Thus, multimodal learning presents a promising approach to improving recommendation systems' performance in complex scenarios.
# Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework
## 5 Findings and Discussion
### The Framework: Key Components and Limitations in the Existing Literature
#### 5.2.4 Challenges and Recommendations
This encompasses the formulation of policies, the frequent training of staff members on cybersecurity protocols, and active involvement with the public to disseminate knowledge about local governments' cybersecurity and data protection strategies, as well as educating residents on protecting their own data as well as about cybersecurity [13; 62; 68; 76]. The general lack of knowledge among staff and decision-makers about cybersecurity and how to safeguard data exacerbates vulnerability to cyberthreats [55; 58; 64]. This knowledge gap is significant, manifesting not only in a general lack of cybersecurity awareness among non-IT staff but also in the specific technical knowledge necessary for IT professionals within local governments. Each data category has unique vulnerabilities, requiring different levels of safeguarding [135]. Understanding the different types of cyberthreats is crucial for IT professionals to prioritize their cybersecurity measures. Furthermore, obtaining specific cybersecurity certifications can be resource-intensive and beyond the reach of many local governments, making them ill-prepared to manage and respond to cyberthreats. To tackle technical and operational challenges, local governments can form partnerships, invest in targeted training programs, and use open-source or cost-effective cloud-based cybersecurity services [26; 35]. Regular risk assessments and audits can also help them identify vulnerabilities and prioritize resource allocation.
# A Review of Double-Walled and Triple-Walled Carbon Nanotube Synthesis and Applications
## 4 Pea Pod-Derived Method: Advantages and Disadvantages  
Pea pod-derived DWNTs and TWNTs have specific advantages and disadvantages over the DWNTs and TWNTs grown by the arc-discharge method and the CVD method [104; 130; 131; 134; 135; 136; 137; 138; 139; 140; 141; 142; 150]. One advantage is that the method of growing the multi-walled structures by thermally-treating pea pods makes it possible to obtain high-purity CNTs without contaminants, such as metallic impurities. Another advantage is that the diameter and chirality distributions of pea pod-derived DWNTs and TWNTs are narrower than the diameter and chirality distributions obtained via arc-discharge and CVD. This happens because the electronic structure of the constituent inner tubes is predominantly determined by the outer tubes, as well as by the high temperature observed during the thermal treatment. It is very interesting to note that the interlayer spacing in pea pod-derived structures strongly depends on the high temperatures achieved during the thermal treatment [104; 130; 131; 134; 135; 136; 137; 138; 139; 140; 141; 142; 150].  
Therefore, this method is highly promising to synthesize DWNTs and TWNTs with specific metallicity configurations (e.g., S@M, M@S, S@S and M@M in the case of DWNTs) using metallic or semiconducting SWNTs as host materials under optimized synthetic conditions. Eventually, it will be possible to synthesize chirality-specific DWNTs and TWNTs using chirality-specific SWNTs as host materials and optimized thermal treatment conditions (e.g., temperature and environment of the growth process) [130; 131; 134; 135; 136; 137; 138; 139; 140; 142; 150]. However, the pea pod-derived method presents the following disadvantages: (1) it suffers from the limited filling ratio of fullerenes inside SWNTs, which leads to the formation of very short or disconnected inner tubes; (2) it has limited production capacity; (3) it relies upon high-cost ultra-crystalline arc-discharge-derived SWNTs (in the case of the DWNT synthesis) to serve as host materials; and (4) the method also depends on high-cost material processing techniques, as for example, the technique used to encapsulate the fullerene molecules and the high temperatures required during the thermal treatment [130; 131; 134; 135; 136; 137; 138; 139; 140; 142; 150].  
This is likely why, intrinsically, the CVD method has been largely used to produce CNTs at an industrial scale. Indeed, the CVD method carries the disadvantage of producing lower quality samples, but on the other hand, it is a technique much cheaper to operate than the arc-discharge technique and the pea pod-derived technique. The commercially-available DWNTs are predominantly synthesized via the CVD method.
## 3 Simulation Analysis and Results
### Results and Discussion
#### 3.3.3 BER Performance  
By analyzing the BER results, we can evaluate the performance of the UOWC system based on different LED layouts. The BER curves of the three layouts in clean seawater, coastal seawater, and harbor seawater at different depths are shown in Figure 8. It can be seen that the BER of the lemniscate-compensated layout is lower than that of the other two traditional LED layouts. In clean seawater, the BER for the lemniscate-compensated layout at a 13 m depth is 1 \(\times\) 10\({}^{-8}\). The ellipse-compensated and ring-compensated layouts' BERs are 1.5 \(\times\) 10\({}^{-7}\) and 6.5 \(\times\) 10\({}^{-7}\), respectively. The transmission distance with the lemniscate-compensated LED layout is longer than the traditional layouts under the same BER. In coastal seawater, the BER of the three compensated layouts varies significantly with distance transmission. The BER of the lemniscate-compensated layout is 1 \(\times\) 10\({}^{-7}\), which is better than the BER of 3.5 \(\times\) 10\({}^{-6}\) and 1 \(\times\) 10\({}^{-4}\) under the other two traditional light source layouts at a depth of 9.5 m. In the harbor seawater, the BER of the lemniscate-compensated layout, ellipse-compensated layout, and ring-compensated layout is 2.5 \(\times\) 10\({}^{-5}\), 6 \(\times\) 10\({}^{-5}\), and 1.8 \(\times\) 10\({}^{-4}\) at a depth of 2 m. Compared with the traditional layouts, the BER performance of the proposed lemniscate-compensated layout is better under different seawater types at the same transmission depth.  
Figure 6: RMSE of the three LED layouts in (**a**) clean seawater; (**b**) coastal seawater; and (**c**) harbor seawater.  
Figure 7: Received optical power distribution in harbor seawater at a depth of 5 m.
# Learning with Errors: A Lattice-Based Keystone of Post-Quantum Cryptography
## 3 Lattices
### Basic Definitions
: _For any lattice \(\mathcal{L}=\mathcal{L}(\mathcal{B})\), we define the minimum distance of \(\mathcal{L}\) as the smallest distance between any two lattice points:_  
\[\lambda_{1}(\mathcal{L})=\inf\{\|x-y\|:x,y\in\mathcal{L},x\
eq y\}.\]  
Clearly, the minimum distance can equivalently be denoted as the length of the shortest nonzero lattice vector:  
\[\lambda_{1}(\mathcal{L})=\min\{\|v\|:v\in\mathcal{L}\setminus\{0\}\}.\]  
where \(||\cdot||\) denotes the Euclidean norm. Generalizing, the \(i\)th successive minimum \(\lambda_{i}(\mathcal{L})\) can be defined as the smallest \(r\) such that the lattice \(\mathcal{L}\) has \(i\) linear independent vectors of norm at most \(r\). The minimum length of a set of independent vectors of a lattice \(\mathcal{L}\) is denoted as \(\lambda_{n}(\mathcal{L})\), where the length of a set is defined as the length of the longest vector in the set. **Definition 15**. : _Let \(V\) be an arbitrary vector space over a lattice \(\mathcal{L}\). An inner product on \(V\) is a function \(\langle,\rangle:V\times V\to\mathcal{L}\), which satisfies_  
1. \(\langle x,x\rangle\geq 0\)__\(\forall x\in V\) _and_ \(\langle x,x\rangle=0\) _iff_ \(x=0\)__
2. \(\langle x+y,z\rangle=\langle x,z\rangle\vee\langle y,z\rangle\)__\(\forall x,y,z\in V\)__
3. \(\langle ax,y\rangle=a\langle x,y\rangle\)__\(\forall x,y\in V\) _and_ \(a\in\mathcal{L}\)__
4. \(\langle x,y\rangle=\langle y,x\rangle\)_,_ \(\forall x,y\in V\)_._  
**Definition 16**. : _Given a lattice \(\mathcal{L}\subset\mathbb{R}^{n}\), we define the dual of \(\mathcal{L}\) as the set_  
\[\mathcal{L}^{*}=\{u:\langle u,\mathcal{L}\rangle\subseteq\mathbb{Z}\}.\]  
It is obvious that the dual of a lattice is the set of points whose inner products with the vectors in the lattice \(\mathcal{L}\) are integer numbers. Furthermore, the dual of a lattice, \(\mathcal{L}^{*}\), is also a lattice.
###### Abstract  
Currently, effective crowd management based on the information provided by crowd monitoring systems is difficult as this information comes in at the moment adverse crowd movements are already occurring. Up to this moment, very little forecasting techniques have been developed that predict crowd flows a longer time period ahead. Moreover, most contemporary state estimation methods apply demanding pre-processing steps, such as map-matching. The objective of this paper is to design, train and benchmark a data-driven procedure to forecast crowd movements, which can in real-time predict crowd movement. This procedure entails two steps. The first step comprises of a cell sequence derivation method that allows the representation of spatially continuous GPS traces in terms of discrete cell sequences. The second step entails the training of a Recursive Neural Network (RNN) with a Gated Recurrent Unit (GRU) and six benchmark models to forecast the next location of pedestrians. The RNN-GRU is found to outperform the other tested models. Some additional tests of the ability of the RNN-GRU to forecast illustrate that the RNN-GRU preserves its predictive power when a limited amount of data is used from the first few hours of a multi-day event and temporal information is incorporated in the cell sequences.  
deep learning algorithms; forecasting; pedestrian crowd movements; GPS trajectories; Recursive Neural Network (RNN) with Gated Recurrent Unit (GRU) +
Footnote †: journal: Sensors
# Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings
###### Abstract  
Recycled concrete aggregates (RCAs) generated from construction and demolition activities have been recognized as a feasible alternative to natural aggregates (NAs). Naturally, the columns fabricated with reinforced recycled concrete (RRC) have been proposed and investigated to promote the structural use of recycled aggregate concrete (RAC). There is still, however, very limited modeling research available to reproduce, accurately and efficiently, the seismic response of RRC columns under lateral cyclic loading; proper evaluations are also lacking on addressing the columns' seismic behaviors. To fill some of those research gaps, a fiber-based numerical model is developed in this study and then validated with the experimental results published in the literature. Subsequently, the numerical model justified is applied to carry out a comprehensive parametric study to examine the effects of a range of variables on the hysteretic characteristics of RRC columns. Furthermore, a grey relational analysis is conducted to establish quantifiable evidence of key variable sensitivities. The evaluation results imply that the use of the additional water method (AWM) for manufacturing RAC is likely to reduce the lateral load-carrying capacity of the RRC columns (up to 10%), whereas the opposite would occur if a conventional mixing procedure is adopted. Moreover, compared with other factors such as steel area ratio, the content of RCA replacement has a less remarkable effect on the seismic performance of the RRC columns. In general, the RRC columns possess acceptable seismic-resistant properties, and they can be used in earthquake-prone regions with confidence.  
reinforced concrete; recycled aggregate concrete; columns; seismic performance; numerical analysis; variable sensitivity +
Footnote †: journal: Applied  
Article
# Feature-Model-Based In-Process Measurement of Machining Precision Using Computer Vision
## 2 Measurement Algorithm
### ROI-Based Canny Edge Detection
#### 2.4.3 Non-Maximum Suppression and Dual Threshold Processing  
Due to the convolution of filters sliding over the image within a window, lower gradient values, \(G_{p1}\) and \(G_{p2}\), appear on either side of the actual edge center. For detected edge pixel \(G_{p}\), the pixel gradient strength is compared with adjacent pixels along the positive gradient direction \(G_{p1}\) and negative gradient direction \(G_{p2}\). If this is the maximum value, this indicates that the pixel is at the edge center and is retained; otherwise, the pixel value is set to zero. By setting high and low thresholds, the gradient magnitude image categorizes pixels into three types: strong edge, weak edge, and non-edge. The high threshold determines strong edges, the low threshold determines non-edges, and weak edges require further analysis to determine if they are actual edges. The spatial connectivity of weak edges is analyzed to determine true edges. Components connected spatially to strong and weak edges are considered true edges, while unconnected components are excluded. Finally, weak edge pixels are connected to strong edge pixels based on paths connected to form complete edges. Algorithm 2 lists pseudocode based on the steps of the improved Canny edge detection algorithm.  
```
Input: Part image \(I\), ROI image \(I_{\text{ROI}}\) Output: Edge image \(I_{\text{out}}\)
1. Begin
2. \(I\leftarrow\) to compute Formulas (2) and (4) for \(I\)
3. for i = \(I_{\text{ROI}}(x)\)
4. for j = \(I_{\text{ROI}}(y)\)
5. \(I_{G(x,y)}\leftarrow\)to compute Formula (13) for \(I\).
6. \(G_{p}\leftarrow\) to compute Formula (16) for \(I_{G(x,y)}\).
7. if \(G_{p}\geq G_{p1}\) and \(G_{p}\geq G_{p2}\)
8. \(G_{p}\) may be an edge
9. else
10. \(G_{p}\) should be suppressed
11. end
12. if \(G_{p}\geq\) HighThreshold,
13. \(G_{p}\) is a strong edge
14. else if \(G_{p}>\) LowThreshold
15. \(G_{p}\) is a weak edge
16. else
17. \(G_{p}\) should be suppressed
18. end
19. if \(G_{p}=\) LowThreshold and \(G_{p}\) connected to a strong edge pixel
20. \(G_{p}\) is a strong edge
21. else
22. \(G_{p}\) should be suppressed
23. end
24. \(I_{\text{out}}\)(x,y) = \(G_{p}\)
25. end
26. end
```  
**Algorithm 2**Improved Canny Edge Detection
## 4 Results and Comparative Analysis
### Continuous biodegradability models  
The prediction accuracy results obtained by the continuous modeling techniques tested are reported in Table 1, which shows the MAPE obtained with continuous biodegradability models in terms of average and maximum values for the low and high biodegradability sets, and for the whole data set.  
For (Trained) MLR with the training set used by [PERSON] _et al._[9], the average MAPEs for the low, high and whole set are 30.88%, 27.59% and 29.96%, respectively. The maxima are 88.63%, 52.73% and 88.63% respectively. This shows that the Trained MLR model has similar predictive ability for the three sets, with a higher maximum error for the low set (88% _vs._ 52%). LOOCV MLR fared better than trained MLR, with average errors of 21.51%, 6.10% and 13.80%, and maxima of 70.12%, 21.26% and 70.12%, for the three sets respectively. Thus LOOCV MLR has a much higher predictive ability for the high end than trained MLR (6% _vs._ 27%). LOOCV K-Nearest Neighbors MLR performed similarly to LOOCV MLR with averages of 21.51%, 6.60% and 13.8%, and maxima of 79.2%, 25.73% and 79.2% respectively. LOOCV ANN had a little higher accuracy than Trained MLR for the high end, but not better than the previous methods. The averages were 22.98% for the low end, 7.11% high end,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Average**} & \multicolumn{3}{c}{**Max**} \\  & **Low-B** & **High-B** & **All** & **Low-B** & **High-B** & **All** \\ \hline
**Trained MLR** & 30.9 & 27.6 & 29.9 & 88.6 & 52.7 & 88.6 \\
**LOOCV MLR** & 21.5 & 6.1 & 13.8 & 70.1 & 21.2 & 70.1 \\
**LOOCV K-NN MLR** & 22.1 & 6.6 & 14.2 & 79.2 & 25.7 & 79.2 \\
**LOOCV ANN** & 22.9 & 7.1 & 16.1 & 87.2 & 27.0 & 87.2 \\
**LOOCV Continuous MBR** & 10.6 & 37.5 & 23.8 & 47.6 & 146.5 & 146.5 \\
**LOOCV CART** & 24.8 & 11.1 & 17.8 & 66.6 & 30.8 & 66.6 \\
**Basu-ANN** & 26.2 & 9.2 & 17.4 & 133.64 & 24.7 & 133.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: MAPE for continuous models.  
and 16.05% overall, while the maxima were 87.27%, 27.04%, and 87.27%. Continuous LOOCV MBR fared better than all of the previous methods on the low end with an average of 10.63% and a maximum of 47.62%. For the high and overall sets, MBR's average errors were 37.57% and 23.89%, and maxima of 146.57% and 66.67%, respectively. Although [PERSON] _et al._[2] used ANN with a different set of oils and different attributes, we were curious to compare their results to ours. The prediction accuracy obtained was inferior to all of our tested methods. Their methods had averages of 26.29%, 9, 2%, and 17%, and maxima of 133.64%, 24.76% and 133.64%, respectively. Compared to trained MLR [1], the errors are higher, especially in terms of maxima. Finally, CART had a little easier time predicting the high end, but was inferior to the other methods that favored the high end. CART averages were 24.82%, 11.15%, and 17.88%, with maxima at 66.67%, 30.8% and 66.67% for the low, high, and overall data sets respectively.