esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 6 Conclusions  
In the present work, an experimental study upon the effect of corrosion on the hysteretic behavior of steel reinforcing bars was conducted, from which non-linear hysteretic models of steel bars were extracted. Based on these, an FEM analysis of the cyclic behavior of non-corroded and corroded RC columns was performed in order to validate them. The main outcomes of the manuscript are as follows:  
* Pitting corrosion can cause a significant reduction in the actual residual cross-section of steel bars, since deviation between the mean reduced diameter (d\({}_{\text{red}}\)) and the effective diameter (d\({}_{\text{eff}}\)) was depicted. Due to the reduction in the cross-section and stress concentration development in the area around the pits, rapid failure and change of the failure mechanism occurred under the dynamic loading of the corroded steel bars;
* Due to the combined action of the loading history and the buckling phenomena, the hysteretic behavior of the non-corroded steel reinforcing bar gradually degraded and differed from the classic bilinear \(\sigma\)-\(\varepsilon\) model, which is taken into account by the current international regulations. In the case of corrosion, the envelope curve of the hysteretic model was further degraded due to corrosion damage.
* For corroded steel reinforcing bars, the envelope curve of the hysteretic model was further degraded; in the case of uniform corrosion, a slight reduction was presented depending on the percentage mass loss, whereas in the case of pitting corrosion a significant drop was denoted, both in terms of strength but mainly of ductility;
* An attempt was made to quantify the corrosive factor in the hysteretic behavior of steel reinforcement. The proposed models simulating the cyclic behavior of the non-corroded and corroded RC column, especially in the case of steel reinforcement suffering from pitting corrosion, were in good agreement with the experimental results, both in terms of bearing capacity and useful lifetime.  
Methodology, M.B., K.K. and C.A.; software, M.B. and K.K.; validation, M.B.; investigation, M.B.; writing--original draft preparation, M.B. and K.K.; writing--review and editing, C.A.; supervision, C.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Permute-Scatter-Gather Implementation
### Implementation  
The Permute-SG is augmented with ease to the OpenSSL's scatter-gather implementation (i.e., the version of 1.0.2f). The procedure of the Permute-SG for the modular exponentiation is performed through the following steps:  
1. Permute step: In this step, a permutation P is randomly generated from \(\mathcal{P}\), the set of all permutations. The generation process is conducted along with the precomputation phase of modular exponentiation algorithm (Algorithm 1).
2. Scatter step: This step is the same as the scatter procedure in the OpenSSL, except that the scattering location of a multiplier with an index idx is determined by P(idx).  
Gather step: This step is the same as the gather procedure in the OpenSSL, except that the gathering location of a multiplier with an index idx is determined by P(idx).
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 4 Model Performance and Results Analysis
### Performance of Multiplicative Models  
Multiplicative models, such as TWD-IRT and ART-IRT, generally underperformed, with lower AUC scores and higher AIC/ BIC values. The poor performance of multiplicative models highlights the challenges of incorporating response time effects in a way that does not distort ability estimates. The key reason for this poor performance lies in how multiplicative models handle response time variability. Multiplicative transformations amplify small variations in response time, leading to disproportionately large effects on estimated ability levels. This issue becomes particularly problematic for students who either respond extremely fast or slow, leading to unreliable and erratic ability estimates.
# Compact 50 W All-Solid-State Picosecond Laser System at 1 kHz
## 2 Experiment and Results
### Side-Pumped Amplifier
\(r\) and \(\theta\) are the waist radius and divergent angle respectively. The ABCD matrix of the beams passing through the optics in the Figure 6 can be expressed as:  
\[\left( \begin{array}{cc}A&B\\ C&D\end{array} \right)=\left( \begin{array}{cc}1&L\\ 0&1\end{array} \right)\left( \begin{array}{cc}1&0\\ -\frac{1}{f}&1\end{array} \right)\left( \begin{array}{cc}1&l\\ 0&1\end{array} \right)\left( \begin{array}{cc}-1&0\\ 0&-1\end{array} \right)\left( \begin{array}{cc}1&l\\ 0&1\end{array} \right)\left( \begin{array}{cc}1&0\\ -\frac{1}{f}&1\end{array} \right)\left( \begin{array}{cc}1&L\\ 0&1\end{array} \right). \tag{10}\]  
The length of \(l\) can be obtained according to Figure 6:  
\[l=\frac{f(\theta L+r)}{r+\theta L-f\theta}. \tag{11}\]  
\(l\) is a dependent variable, affected by variables \(\theta\) and \(r\). Therefore, the \(l\) can vary with different pump powers of the side-pumped amplifier. The matrix where the beam returns to the crystal surface again can be expressed as:  
\[\left( \begin{array}{c}r^{\prime}\\ \theta^{\prime}\end{array} \right)=\left( \begin{array}{cc}A&B\\ C&D\end{array} \right)\left( \begin{array}{c}r\\ \theta\end{array} \right)=\left( \begin{array}{c}r\\ -\theta\end{array} \right). \tag{12}\]  
FR was a 45\({}^{\circ}\) Faraday Rotator, and a roundtrip in a 45\({}^{\circ}\) FR was equivalent to a single pass in a 90\({}^{\circ}\) rotator. With this structure, the tangential and radial components of polarization can exchange phase retardation, and the divergent angle changed from \(\theta\) to \(-\theta\), while the diameter of the beam was invariant. This is an ideal structure for birefringence compensation in a single-rod amplifier. Finally, the 10.8 W picosecond pulses from amplifiers presented above were input to the side-pumped double-pass amplifier. The side-pumped module consisted of a \(\varphi 6\times 160\) mm\({}^{3}\), 0.5 at % Nd-doped YAG crystal side-pumped by QCW laser diode. The maximum output power (at 110 A) of the side-pumped module was 1008 W with the pulse duration of 180 \(\upmu\)s at the repetition of 1 kHz. In this experiment, by using the depolarization compensation technique described above, the side-pumped module working at a current of 70 A and output power of 50 W can be obtained. Figure 7 shows the power curve and beam image of the laser system. Figure 6: The structure for depolarization compensation in a single-rod amplifier.
# An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background
## 2 Materials and Methods
### Wavelet Transform and Curvelet Transform
#### 2.1.5 Analysis of Wavelet Transform Denoising  
The wavelet transform method solves the correlation values of the spatial image in a set of established anisotropic bases. The method of wavelet transform denoising is as follows: first, the noisy image is decomposed into subbands of different scales, and the wavelet transform coefficients corresponding to each subband are obtained through the Plancherel operation; then, the obtained wavelet transform coefficients are processed by the hard threshold method (abandoning the smaller transform coefficients and retaining the larger transform coefficients) to filter out noise in the image and retain the edge characteristics of the image. The expression of the hard threshold in the wavelet transform denoising is as follows:  
\[C_{0}(i,h)=\left\{\begin{array}{c}C(i,h),|C(i,h)|\geq T_{i}\\ 0,|C(i,h)|<T_{i}\end{array}\right. \tag{15}\]  
where \(C(i,h)\) is the wavelet coefficient at scale \(i\) and direction \(h\), \(T_{i}\) is the threshold corresponding to different scales, and \(C_{0}(i,h)\) is the wavelet transform coefficient after hard threshold denoising; the expression for selecting the threshold \(T_{i}\) is as follows:  
\[T_{i}=k_{i},\sigma\cdot\sigma_{i} \tag{16}\]  
where \(\sigma\) is the noise standard deviation, \(\sigma_{i}\) is the noise standard deviation corresponding to the subband after transformation, and \(k_{i}\) is the adaptive constant corresponding to each subband. The Monte Carlo algorithm with strong adaptability is used to estimate the noise standard deviation in this method.
## 4 Experiment Setup
### Analysis of Experimental Results of Classified Datasets with Different Feature Numbers
On the explanatory variance ratio chart of the dataset, the FSNT method outperforms the other three methods at k = 15, maintaining a unique variance ratio exceeding 90% and retaining the most information. On the whole, the CSFFS, XGBFS, RFHFS, and FSNT algorithms demonstrate their respective advantages in classifying different datasets, although the RFHFS algorithm exhibits slightly inferior performance. However, the CSFFS algorithm solely selects features based on high correlation using the chi-square value, without considering feature differences or redundancy, resulting in poorer performance compared to information theory-based feature selection algorithms such as XGBFS, RFHFS, and FSNT. Based on the three evaluation criteria (F1, precision, and accuracy) for the three datasets and the explained variance ratio of the datasets, the classification accuracy of the FSNT method surpasses that of the CSFFS, XGBFS, and RFHFS methods. This indicates that the FSNT method is capable of generating feature subsets with stronger classification abilities. Figure 4: Performance of different feature selection methods on Student-mat dataset. (**a**) F1; (**b**) precision; (**c**) accuracy; (**d**) dataset variance.
# Gaussian-UDSR: Real-Time Unbounded Dynamic Scene Reconstruction with 3D Gaussian Splitting
## 3 Method
### Gaussian Model
At the same time, we add optimizable parameters \(\Delta T_{t}\), \(\Delta R_{t}\) which describe minor variations of the object's positional attitude, aiming to reduce motion estimation errors and tracker noise:  
\[T_{t}^{\prime}=T_{t}+\Delta T_{t},R_{t}^{\prime}=R_{t}\cdot\Delta R_{t} \tag{7}\]  
The following equation gives the dynamic object's positional representation in the world coordinate system:  
\[\left\{\mu_{d,t},R_{d,t}\right\}_{t=0}^{T}=\left\{R_{t}\Delta R_{t}\mu_{0}+T_{ t}+\Delta T_{t},R_{0}\Delta R_{t}^{T}R_{t}^{T}\right\}_{t=0}^{T} \tag{8}\]  
where \(\mu_{0}\) and \(R_{0}\) are dynamic objects defined in the object's local coordinate system in terms of its covariance matrix:  
\[\left\{\Sigma_{d,t}\right\}_{t=0}^{T}=\left\{R_{d,t}S_{d}^{T}R_{d,t}^{T}\right\} _{t=0}^{T} \tag{9}\]  
The computational complexity of NeRF mainly comes from two aspects: the evaluation of the neural network for each query point in the volume rendering process and the construction of the neural network itself. Let's assume that in a traditional NeRF-based method, the neural network has \(N\) layers with \(M_{i}\) neurons in the \(i\)-th layer (\(i=1,2,\cdots,N\)). For a single query point, the forward-pass computation in the neural network has a complexity of \(O\Big{(}\sum_{i=1}^{N-1}M_{i}\times M_{i+1}\Big{)}\). In the volume rendering process, if we consider a scene with \(V\) volume elements (voxels) and \(R\) rays for rendering, the overall computational complexity is approximately \(O\Big{(}V\times R\times\sum_{i=1}^{N-1}M_{i}\times M_{i+1}\Big{)}\). The Gaussian splatting reduces the number of elements that need to be processed compared to the voxel-based approach in NeRF. Specifically, we represent the scene with \(G\) Gaussian primitives, where \(G\ll V\). The evaluation of the Gaussian-based model for a single ray has a complexity of \(O(G)\). Moreover, our deep learning network is designed in a more lightweight way. Suppose our network has \(N^{\prime}\) layers with \(M^{\prime}_{i}\) neurons in the \(i\)-th layer (\(i=1,2,\cdots,N^{\prime}\)), and \(N^{\prime}\leq N\), \(M^{\prime}_{i}\leq M_{i}\) for most \(i\). The forward-pass computation for a single query point in our network has a complexity of \(O\Big{(}\sum_{i=1}^{N^{\prime}-1}M^{\prime}_{i}\times M^{\prime}_{i+1}\Big{)}\). Considering the same number of rays \(R\) for rendering, the overall computational complexity of our method is approximately \(O\Big{(}G\times R\times\sum_{i=1}^{N^{\prime}-1}M^{\prime}_{i}\times M^{ \prime}_{i+1}\Big{)}\). By comparison, it is evident that our method significantly reduces the computational complexity. In practical scenarios, we have observed that the reduction in the number of elements (\(G\ll V\)) and the lightweight design of the neural network lead to a decrease in computational complexity by at least several times compared to traditional NeRF-based methods, thus achieving higher efficiency in dynamic scene reconstruction for autonomous driving.
## 4 Simulation Result Comparisons of the N-TWSRIM
### Copper Plating Model  
A composite structure solid rotor is applied to enhance the performance of N-TWSRIM. This composite rotor consists of steel and copper layers. The radial magnetic field and the induced electromotive force of the rotor increase because the relative magnetic permeability of the steel layer is much larger than that of the copper layer. Hence, the output torque and power factor of the N-TWSRIM will be improved. When the thickness of the copper layer is greater than 0, a 2D finite element model is established and its flux distribution is given in Figure 10. The analytical results are compared with the FEM results to verify composite multilayer theory.  
Figure 10 shows that the depth of penetration of the magnetic lines increases when the copper plating rotor is used. This phenomenon is also consistent with the analysis results. The electromagnetic properties of the copper plating model, including output torque, stator current, and power factor, are calculated with ECM and FEM. Figure 11 and Table 5 show the comparison results.  
Figure 11: Copper plating model comparative results of ECM and FEMt (**a**) Torque against slip, (**b**) power factor against slip rates, (**c**) stator current against slip.  
Figure 10: 2D copper plating model of the N-TWSRIM.  
According to Figure 14 and Table 7 of the revised manuscript, it can be seen that the ECM and test error are within 15% and the error at the large slip is less than 5%, meeting the accuracy requirements. The causes of the error are as follows.  
1. When the prototype is tested, the power supply contains harmonic components, so the stator current obtained by the experiment is slightly larger than ECM and FEM.
2. In the calculation process of composite multilayer theory, the influence of harmonics is ignored and the obtained resistance and reactance value will cause errors.
3. The selection of some empirical coefficients in the ECM calculation process will produce errors.
## 2 Materials and Methods
### Start-up and Operation Scheme  
The sludge was inoculated from the sludge storage tank after concentration tank in a wastewater treatment plant. After deposition for a week, the supernatant was removed from the sludge. The inoculated sludge was evenly mixed, and then placed to the bottom of the HABR. The inoculated sludge occupied about 1/3 volume in each compartment. The start-up condition was: hydraulic retention time (HRT) 60 h, volatile suspended solid (VSS) 20-35 g/L, influent organic loading rate (OLR) 0.02-0.03 kg COD/(m\({}^{3}\)-d), influent pH 7.0, water temperature 15 \({}^{\circ}\)C (in April). HRT is a key factor to affect the extent of fluid mixing and the treatment performance of the reactor. In this study, HRT was gradually reduced with the influent water quality fixed, which was beneficial to the sludge retention and thus the stability of the reactor operation. After the HABR was started at HRT of 60 h, the flow rate was continuously increased by regulating the valve until the HRT reached designed retention time 12 h during the first 72 days. On average, HRT was decreased by 2 h every 3 days. During 73-131 days, HRT was maintained at 12 h. Under influent OLR of 1.6-2.4 kg COD/(m\({}^{3}\)-d), the COD removal effect of HABR was investigated. The pH was controlled below 9.0.
## 3 Model Formulation
### Problem Definition  
Graph theory offers a strong modeling framework for urban street networks, treating streets as nodes and distances as edges to capture spatial relationships and the urban structure. This approach facilitates the application of graph algorithms to optimize patrol routes,reduce transition times, and efficiently allocate resources, which is especially advantageous for PPOP due to its dynamic and scalable nature.  
The PPOP can be formally defined as follows: Consider a network of \(m\) streets, \(G\), within a given jurisdiction, where \(n\) officer units, \(P=\{p_{1},p_{2},...,p_{n}\}\) (typically deployed in pairs), are responsible for executing daily patrol operations over a specified time horizon, \(T\) (e.g., from 08:00 to 23:00, comprising 16 one-hour intervals). Each officer unit is required to patrol distinct streets during consecutive time intervals, necessitating transitions between street assignments at each time step.  
To enhance temporal resolution in patrol scheduling, we introduce a fine-tuning parameter \(\delta=\{1,2,3\}\) that governs the granularity of time intervals. When \(\delta=1\) (default setting), the total time horizon \(T=\{1,2,...,16\}\), with each interval representing a 60 min patrol duration. For \(\delta=2\), \(T=\{1,2,...,32\}\), corresponding to 30 min patrol intervals. When \(\delta=3\), \(T=\{1,2,...,48\}\), resulting in 20 min patrol intervals. For computational simplicity, the duration of each patrol interval inherently includes the transition time between consecutive street assignments, where transition time optimization constitutes a key objective for future research.  
The street network within the district is formally represented as \(G=(S,D,L,R)\), where:  
\(S=\{s_{1},s_{2},...,s_{m}\}\) denotes the set of \(m\) streets in the network.  
\(D\) represents the weight matrix of the network, where \(d_{ij}\) corresponds to the traveling distance between any pair of streets  
\(L=\{l_{1},l_{2},...,l_{m}\}\) denotes the length of each street.  
\(R=\{r_{1}^{t},r_{2}^{t},...,r_{m}^{t}\}\) represents the risk of 110 call incidents on each street at hour \(t\), which is derived from a deep spatio-temporal graph attention network [44]. It is important to note that the street risk values are hour-based due to the temporal granularity of the data. Consequently, when the fine-tuning parameter \(\delta\) is set to \(\{2,3\}\), the risk value remains constant over the corresponding two or three consecutive patrol intervals.  
The binary decision variable \(x\in B^{m\
eq t}\) is defined over the Cartesian product of \(m\) streets and \(t\) patrol time intervals, where \(x_{i}^{t}\) indicates the patrol assignment for street \(i\) at time interval \(t\). Specifically, \(x_{i}^{t}=1\) if street \(i\) is selected for patrol during time interval \(t\), and \(x_{i}^{t}=0\) otherwise.
## 4 Performance Evaluation
### Energy Consumption  
The model described in [26] and known as first order radio communication model is adopted in our evaluation. Eelec is supposed to be the radio energy consumption for transmitting or receiving 1 bit of data, energy dissipation for transmitting 1 bit of data from source to destination node at the distance d can be estimated by the following equation:  
\[E_{tx}=l\cdot E_{elec}+l\cdot E_{amp}\cdot d^{2}{}_{\text{toBS}} \tag{4}\]  
The total energy dissipation by the transmission by a device of \(M\) messages:  
\[E_{tx\_tot}=\sum_{i=1}^{M}{l_{i}\cdot E_{elec}+l_{i}\cdot E_{amp}\cdot{d_{j}^{ 2}}_{\text{toBS}}} \tag{5}\]  
While the energy dissipation by a device to receive l bit data is formulated as:  
\[E_{rx}=l\cdot E_{elec} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Device CC (DCC)** & **DCC in Seconds** & **Total CC (TCC)** & **TCC in Seconds** \\ \hline Proposed & 2 T (h) + 2 T (sym) + T (eca) + T (eccn) & 0.0563 s & 5 T (h) + 4 T (sym) + T (eca) + 2 T (eccn) & 0.113 s \\
[15] & 3 T(ecm) + 1 T (h) + 2 T (eca) & 0.0604 s & 10 T (eccm) + 7 T (h) + 4 T (eca) & 0.190 s \\
[17] & 4 T (ecm) + 3 T (h) & 0.0693 s & 14 T (ecm) + 12 T (h) & 0.243 s \\
[18] & 4 T (h) + 2 T (sym) & 0.0354 s & 3 T (ecm) + 19 T (h) + 8 T (sym) & 0.194 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational cost (CC) comparison in seconds.  
Figure 11: Computational cost comparison.  
The total energy dissipation by a device for the reception of \(M^{\prime}\) messages is:  
\[E_{rx\_tot}=\sum_{i=1}^{M^{\prime}}l_{i}\cdot E_{elec} \tag{7}\]  
The total energy overhead consumed by the authentication process of \(N\) devices in our case, can be estimated by the following Equation (6):  
\[E_{tot}=\sum_{j=1}^{N}\left(\sum_{i=1}^{M}l_{i}\cdot E_{elec}+l_{i}\cdot E_{ amp}\cdot{d_{j}}^{2}_{\text{toBS}}+\sum_{i=1}^{M^{\prime}}l_{i}\cdot E_{elec}\right) \tag{8}\]  
where:  
\(l_{i}\): The total size in bits of transmitted data by the device.  
\(E_{elec}\): the dissipation of radio energy.  
\(E_{amp}\) is transmission amplifier energy dissipation.  
\(N\): The number of devices in the network.  
The model is adequate for low distance. For longer distances (\(d>d0\)), the term \(d^{2}\) is replaced by \(d^{4}\) in Equations (4), (5) and (8). \(d0\) is the crossover distance as defined in the radio communication model [26].
## 2 Non-Linear Finite Element Analysis
### Structural Assessment  
#### 2.2.1 Ductility Ratio of the Steel Frame  
The level of structural performance can be measured by quantifying its stresses, strains, displacements, ductility, functionality, and similar parameters. Like seismic design, blast resistant structural design also considers inelastic response under dynamic load. In terms of the structural response, the impulsive blast load yields higher response frequency than the response due to seismic load [38] and the inter-story drifts due to the blast loading on a building are generally higher than the drifts due to earthquakes [39]. In the case of blast loading, the design is based on the member's plastic behaviour and the structural component is governed by its ductility and rotation [40]. The ductility is defined as the structural capability of plastic deformation without fracturing. The maximum plastic displacement of a structure can be measured by the ductility ratio (\(\mu\)) relative to first yield [41] as given in Equation (8).  
\[\mu=\frac{\delta_{u}}{\delta_{y}} \tag{8}\]  
where \(\delta_{u}\) is the maximum plastic displacement, and \(\delta_{y}\) is the displacement at first yield. The displacement at first yield is not obvious for most material. It can be determined by using graphical methods with several alternative definitions [41]. The method based on initial yielding, also called the Tangent Method, is usually implemented to approximate the yield point which is defined as the intersection between the tangential lines of elastic and plastic behaviour [42].  
In design practice, ductility ratio values and rotation criteria are used for designing structure particularly topside offshore structures. The ductility ratio and the plastic strain are related, and the failure of the structure is assumed to occur when the plastic strain approaches the material failure strain [43]. The allowable range of the structural member's plastic deformations is measured based on the calculated ductility ratio, whereas the limit on the support rotation makes sure that tension membrane action, that may develop in a member, will be in a safe range where no connection failures occur.