esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Local Cyclic Loading on Direct Shear Strength Characteristics of Shear-Zone Soil
## 6 Conclusions  
In this paper, the stress-strain behaviors of shear-zone soil obtained from the Huang-tupo landslide in the TGR area were characterized through monotonic and local cyclic direct shear tests under different normal stress, cyclic periods, and numbers of cycles. Based on the detailed analysis and interpretation of the cyclic shear test results, the following conclusions can be drawn:  
1. The shear strength ratio \(f_{\rm cm}\) of shear-zone soil from monotonic and local cyclic direct shear testing depends on the normal stress and cyclic shear stress. As the normal stress increases, the shear strength ratio \(f_{\rm cm}\) grows almost linearly with different cyclic periods. The shear strength of shear-zone soil after applying local cyclic loading is smaller than that of monotonic loading because of ratcheting behavior and fatigue deterioration.
2. In a local cyclic loading direct shear test, the shear failure stress of shear-zone soil decreases with an increase in the number of cycles and a decrease in the cyclic periods. In a local load-controlled cyclic loading test (stage II), the shear displacement of specimens in each cycle initially increases dramatically, and then increases slightly to steady stage, exhibiting a ratcheting effect. If loading persists after cyclic loading, the shear-zone soil hardens continuously, resulting in an elastic hysteresis loop with a larger number of cycles.
3. Compared with monotonic loading, an increase in the cyclic period of cyclic loading on a specimen causes a decrease in the shear strength ratio \(f_{\rm cm}\). The shear stress-displacement curves of the shear-zone soil with different numbers of cycles are quite similar, showing two individual parts: initially a plastic displacement stage and then elastic displacement stage.
4. For slow-moving reservoir landslides in the TGR, the horizontal displacements are more sensitive than vertical ones after suffering local cyclic loadings.  
During the dynamic evolution of the Huangtupo landslide, the shear strength of the shear-zone soil will gradually decrease with cyclic reservoir water fluctuations. Therefore, there is a need to monitor the deformation, especially the horizontal displacement of the widespread reservoir landslide in the Three Gorges Reservoir area, and pertinent prevention measures should be taken to avert serious fatigue deterioration of the shear-zone soil, which may even induce a catastrophic reservoir landslide disaster.  
Conceptualization, Q.C. and Q.L.; methodology, X.T.; validation, X.T.; formal analysis, Q.C. and D.C.; writing--original draft preparation, D.C.; writing--review and editing, Q.C.; funding acquisition, D.C. 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 (NSFC), funding number 42277171, 41972298 and 41772304.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Dueling DQN-Based Routing Protocol
### The Proposed Routing Protocol
#### 4.3.1 Neighboring Table Establishment and Maintenance  
Each node broadcast hello packets periodically at predefined intervals \(t\) to exchange useful state information that contained its geographical location, velocity, probability value (\(d_{r}\)), packet total waiting time, and node ID. Figure 4a displays the format of a hello packet. When a node received a hello packet from its neighboring node, it established entry in its neighboring table \(NT\) and recorded related information from the hello packet.  
After the node received the hello packet, the receiving node replied with an ACK packet to the current forwarding node to confirm the link had been established. Fur  
Figure 3: The framework for our proposed routing protocol.  
thermore, an ACK packet included node ID and location, ACK confirmation, and the SNR and probability values (\(d_{f}\)). Furthermore, the neighboring table also recorded useful information to assist in routing decisions. This is shown in Figure 4b.  
Additional information needed to be recorded in the neighboring table during the process of data-packet forwarding to assist in routing decisions. The data-packet format is shown in Figure 4c. The data packets included node ID and the location of the destination and source nodes, the nodes that the packet had already visited (HVN), which was used to avoid routing loops, a unique sequence number to decide the freshness of the packet, the last node's ID, and the data packet content.
## 1 Introduction
Therefore, we focused on optimized implementations of scalar multiplication using curve Ted127-glv4 on 8-bit ATxmega256A3, 16-bit MSP430 FR5969, and 32-bit ARM Cortex-M4 processors, respectively. Our main contributions can be summarized as follows:  
* We present efficient implementations at each level of the implementation hierarchy of four-dimensional GLV-GLS scalar multiplication considering the features of 8-bit AVR, 16-bit MSP430, and 32-bit ARM Cortex-M4 processors. To improve the performance of scalar multiplication, we carefully selected the internal algorithms at each level of the implementation hierarchy. These implementations also run in constant time to resist timing and cache-timing attacks [26; 27]. * We demonstrate that the efficiently computable endomorphisms can accelerate the performance of four-dimensional GLV-GLS scalar multiplication. For this purpose, we analyze the operation counts of two elliptic curves \"Ted127-glv4\" and \"Four\(\mathbb{Q}\)\", which support the four-dimensional GLV-GLS scalar multiplication. The GLV-GLS curve Ted127-glv4 requires fewer number of field arithmetic operations than Four\(\mathbb{Q}\)-based implementation to compute a single variable-base scalar multiplication. However, because Four\(\mathbb{Q}\) uses a Mersenne prime \(p=2^{127}-1\) and the curve Ted127-glv4 uses a Mersenne-like prime \(p=2^{127}-5997\), Four\(\mathbb{Q}\) has a computational advantage of faster field arithmetic operations. By using the computational advantage of endomorphisms, we overcome the computational disadvantage of curve Ted127-glv4 at field arithmetic level. * We present the first constant-time implementations of four-dimensional GLV-GLS scalar multiplication using curve Ted127-glv4 on three target platforms, which have not been considered in previous works. The proposed implementations on AVR, MSP430, and ARM processors require 6,856,026, 4,158,453, and 447,836 cycles to compute a single variable-base scalar multiplication, respectively. Compared to Four\(\mathbb{Q}\)-based implementations [24], which have provided the fastest results to date, our results are 4.49% slower on AVR, but 2.85% and 4.61% faster on MSP430 and ARM, respectively. Our MSP430 and ARM implementations set new speed records for variable-base scalar multiplication. The remainder of this paper is organized as follows. Section 2 describes preliminaries regarding ECC and its speed-up techniques, including the GLV and GLS methods. Section 3 presents a review of four-dimensional GLV-GLS scalar multiplication and its implementation hierarchy. Section 4 describes the implementation details of field arithmetic and optimization methods for the target platforms. Section 5 describes optimization methods for ECC in terms of point arithmetic and scalar multiplication. Experimental results and a comparison of our work to previous ECC implementationson AVR, MSP430, and ARM processors are presented in Section 6. Finally, we conclude this paper in Section 7.
## 3 Results
### Cannabinoids in Hemp Oil
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**S1**} & \multicolumn{3}{c}{**S2**} & \multicolumn{3}{c}{**S3**} & \multicolumn{3}{c}{**S4**} & \multicolumn{3}{c}{**S5**} & \multicolumn{3}{c}{**S6**} & \multicolumn{3}{c}{**S7**} & \multicolumn{3}{c}{**S8**} & \multicolumn{3}{c}{**S9**} \\  & **HE + HSO** & **HE + HSO** & **INF + HO** & **CBD + T + HSO** & **HE + HSO** & **HE + HSO** & **SCO2** & **CBD + HSO** & **CBD + MCT** \\ \hline
**Analyte** & **Rt** & **C** & **Rt** & **C** & **Rt** & **C** & **Rt** & **C** & **Rt** & **C** & **Rt** & **C** & **Rt** & **C** & **Rt** & **C** & **Rt** & **C** \\ \hline CBDV & 1.84 & \(<\)LOQ & 1.89 & 14.70 & N/A & \(<\)LOD & 1.96 & \(<\)LOQ & 2.09 & \(<\)LOQ & N/A & \(<\)LOD & 1.81 & 2.52 & 1.94 & \(<\)LOQ & 2.03 & \(<\)LOQ \\ CDQA & 2.38 & 50.46 & 2.38 & 18.77 & N/A & \(<\)LOD & 2.38 & 1.62 & 2.54 & 17.81 & 2.54 & 14.65 & 2.38 & 6.99 & 2.34 & \(<\)LOQ & N/A & \(<\)LOD \\ CDGA & 2.48 & 1.19 & N/A & \(<\)LOD & N/A & \(<\)LOD & 1.71 & \(<\)LOD & 2.64 & 0.98 & 2.64 & \(<\)LOQ & 2.47 & \(<\)LOQ & N/A & \(<\)LOD & N/A & \(<\)LOD \\ CBG & 2.61 & \(<\)LOQ\(<\)L & 2.61 & 2.13 & N/A & \(<\)LOD & N/A & \(<\)LOD & 2.78 & \(<\)LOQ & N/A & \(<\)LOD & 2.60 & \(<\)LOQ & N/A & \(<\)LOD & N/A & \(<\)LOD \\ CBD & 2.73 & 2.34 & 2.73 & 0.090 & 2.73 & 2.53 & 2.73 & \(<\)LOQ & 2.90 & 4.37 & 2.90 & 1.42 & 2.73 & 166.32 & 2.80 & 89.88 & 29.0 & 50.67 \\ CBN & N/A & \(<\)LOD & 3.55 & \(<\)LOQ & 3.52 & \(<\)LOQ & 3.62 & \(<\)LOQ & N/A & \(<\)LOD & N/A & \(<\)LOD & 3.54 & 4.57 & N/A & \(<\)LOD & N/A & \(<\)LOD \\
39-THC & 4.07 & \(<\)LOQ & 4.07 & \(<\)LOQ & 4.06 & \(<\)LOQ & N/A & \(<\)LOD & 4.22 & 1.35 & 4.22 & \(<\)LOQ & 4.06 & 1.06 & N/A & \(<\)LOD & N/A & \(<\)LOD \\
38-THC & N/A & \(<\)LOD & N/A & \(<\)LOD & N/A & \(<\)LOD & 4.11 & \(<\)LOQ & N/A & \(<\)LOD & N/A & \(<\)LOD & 4.16 & \(<\)LOD & N/A & \(<\)LOD & N/A & \(<\)LOD \\ CBC & 4.55 & \(<\)LOQ & 4.55 & \(<\)LOQ & 4.55 & \(<\)LOQ & 4.55 & \(<\)LOQ & 4.69 & \(<\)LOQ & 4.70 & \(<\)LOQ & 4.55 & \(<\)LOD & N/A & \(<\)LOD & N/A & \(<\)LOD \\ THCA & 4.66 & 2.01 & 4.65 & \(<\)LOQ & N/A & \(<\)LOQ & 4.67 & \(<\)LOQ & 4.79 & 2.75 & 4.79 & 1.80 & 4.65 & \(<\)LOD & N/A & \(<\)LOD & N/A & \(<\)LOD \\ \hline \end{tabular}  
* Rt: retesting time in six: S1-S10 sample 1 \(<\)-sample 10: C: concentration in mg/g \(<\)LOQ: peak detected but too low to be quantified: \(<\)LOD: no peak detected; N/A \(<\)not applicable.  
\end{table} 
Table 6: Results for here oils, analyzed with the proposed method and regression equations used for quantification.
## 3 Effect of Deformation Parameters on Microstructure Evolution  
It is well known that the microstructure evolution of MDFed alloys, such as the grains, second phases, micro-texture and dislocations, is related to the deformation temperatures, strain rates and accumulative strain. Table 1 summarizes the effect of the deformation parameters on the grain sizes and second phases of Mg alloys p  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Parameters** & **Grain Sizes** & **Second Phases** & **Reference** \\ \hline Pure Mg & Decreasing from 340 \({}^{\circ}\)C to 320 \({}^{\circ}\)C & The grain size decreases & - & [31] \\  & with total strain of 1.2 & from 81.3 \(\upmu\)m to 8.3 \(\upmu\)m & & \\ \hline Mg-3 Al-1 Zn & Decreasing from 350 \({}^{\circ}\)C to 150 \({}^{\circ}\)C & The grain size is 0.5 \(\upmu\)m & - & [32] \\  & with total strain of 4.8 & & & \\ \hline Mg-8.8 Al-0.47 Zn & Deformation temperature of 300 \({}^{\circ}\)C with true strain of 0.9 & The grain size is 1.5 \(\upmu\)m & Fine Mg\({}_{54}\)Ag\({}_{17}\) & [33] \\ \hline Mg-8.5 Gd-4.5Y-0.7 Zn- 0.4 Zr & Deformation temperature of 500 \({}^{\circ}\)C with total strain of 3.0 & The grain size is 1.49 \(\upmu\)m & Broken LPSO phases & [34] \\ \hline Mg-13 Gd-4Y-2 Zn- 0.5 Zr & Decreasing from 480 \({}^{\circ}\)C to 420 \({}^{\circ}\)C & The grain size is 4 \(\upmu\)m & Thin-plated LPSO and broken block-shaped LPSO & [24] \\ \hline Mg-8 Al & Room temperature with  cumulative strain of 2.0 at strain  rate of 3 \(\times\) 10\({}^{-3}\) s\({}^{-1}\) & The grain size is 0.3 \(\upmu\)m & - & [35] \\ \hline Mg-2.36Y-0.98 Zn & Deformation temperature of 500 \({}^{\circ}\)C with strain of 3.8 & The grain size is 8.0 \(\upmu\)m & Severe kinking and totally fragmentation of LPSO. The size less than 1 \(\upmu\)m & [36] \\ \hline Mg-3 Al-1 Zn & Deformation temperature of 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C with strain at the center and edge regions in range of 7.44–10.00 and 3.14–5.44. & The grain size ranges of 3.0–3.9 \(\upmu\)m & - & [37] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Microstructure evolution of MDFed Mg alloys.
# Deep Learning-Based Signal Detection for Underwater Acoustic OTFS Communication
## 6 Conclusions  
In this paper, a DL-based signal detection neural network is proposed for UWA OTFS communication, named SC-CNN-BiLSTM. In SC-CNN-BiLSTM, we cascade skip-connected CNN with BiLSTM for UWA OTFS signal detection, which can extract signal features and effective information to recover distorted received symbols. The numerical results show that the proposed method had better BER performance than the ZF, LMMSE, MP, FC-DNN and 2D-CNN signal detection methods in UWA OTFS. This was because the proposed SC-CNN-BiLSTM-based OTFS signal detection method could use the CNN to extract the signal features and learn the hidden relationship between its input and output. In addition, the SC structure provided the information fusion from the interaction of the current layer and previous layers. Furthermore, BiLSTM in the network can continuously memorize and extract effective information with sequentially transmitted symbols from the past to the future, which can mitigate the ICI and ISI. For UWA OTFS, the proposed SC-CNN-BiLSTM signal detection method had better performance and low complexity in multiple communication channels with different scales of multi-path and Doppler effects.  
Conceptualization, X.S.; data curation, S.Z.; formal analysis, Y.Z.; funding acquisition, B.W.; investigation, Y.Z.; methodology, S.Z., B.W. and W.B.; project administration, Y.Z. and B.W.; resources, W.B. and X.S.; software, S.Z.; supervision, Y.Z. and B.W.; validation, W.B. and Y.L.; visualization, S.Z.; writing--original draft, S.Z.; writing--review and editing, Y.Z. and Y.L. 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 under grant no. 61801372, U19B2015, 62001360, 62031021 and 61801371 and the Scientific Research Program of Shaanxi Education Department. under grant No. 22 JK0454.  
Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found here: [[https://www.ffi.no/forskning/prosjekter/watermark](https://www.ffi.no/forskning/prosjekter/watermark)]([https://www.ffi.no/forskning/prosjekter/watermark](https://www.ffi.no/forskning/prosjekter/watermark)), accessed on 30 June 2022.
## 3 The MISP Structure
### Adaptive Notch Filter  
The traditional IIR notch filter with a constant frequency is designed as  
\[H(z)=\frac{1+az^{-1}+z^{-2}}{1+a\rho z^{-1}+\rho^{2}z^{-2}} \tag{32}\]where \(\alpha\) and \(\rho\) are the frequency and bandwidth parameters, respectively. The relationship between \(a\) and notch filter frequency is given by  
\[\omega_{n}=\frac{1}{T_{s}}\cos^{-1}\left(-\frac{\alpha}{2}\right) \tag{33}\]  
where \(T_{s}\) is the sampling time.  
Because the conventional filter cannot adjust the frequency parameter online, an adaptive frequency notch filter is proposed here, and its structure diagram is shown in Figure 6.  
\(\Delta\omega(k)\) is the tilting speed error of the motor and gimbal frame; \(y_{\omega}(k)\) is the output after passing through the notch filter; \(d(k)\) is the expected output, which is generally zero. The improved RLS algorithm proposed in the previous section was used to estimate the resonant frequency, and then the frequency-related parameter in the notch filter was updated.  
The gradient of \(y_{\omega}(k)\) with respect to \(a\) is deduced as:  
\[\varphi(k)=\frac{[\Delta\omega(k)-\rho y_{\omega}(k)]z^{-1}}{1+a\rho z^{-1}+ \rho^{2}z^{-2}} \tag{34}\]  
Combined with the improved RLS algorithm, the complete adaptive iterative filtering algorithm can be summarized as follows:  
Step 1: initialize \(\widehat{a}\left(0\right)=a_{0}P(k)=\delta I\sigma_{v}^{2}(0)=0\);  
Step 2: update \(e(k)=d(k)-y_{\omega}(k)\);  
Step 3: update \(\widehat{\sigma_{v}^{2}}(k)=\alpha\widehat{\sigma_{e}^{2}}(k-1)+(1-\alpha)e^ {2}(k)\), \(\widehat{\sigma_{v}^{2}}(k)=\beta\widehat{\sigma_{v}^{2}}(k-1)+(1-\beta)e^{2 }(k)\);  
Step 4: update \(\lambda(k)\) according to Equation (31);  
Step 5: update \(K(k)=\frac{P(k-1)\varphi(k)}{\lambda(k)+\varphi(k)P(k-1)\varphi(k)}\) and \(P(k)=[(1-K(k))\varphi(k)]P(k-1)\);  
Step 6: update \(\lambda(k)=\lambda(k-1)+K(k)e(k)\).  
\(\delta\) is a small constant in Step 1, and \(a_{0}\) is converted from the parameter \(\omega_{p}\) of the initial structure resonant frequency using Equation (33).
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 3 Thermoplastic Resin Toughening Method
### Advancements in Thermoplastic Resin Toughening
#### 3.4.1 Polyphenylene Sulfone  
Polyphenylene sulfone (PSF) is an amorphous, transparent polymer characterized by its high strength and exceptional thermal stability. It exhibits excellent mechanical properties and dimensional stability. Furthermore, the structural similarity between PSF and bisphenol-A-type epoxy resins results in good compatibility between the two materials.  
[PERSON] et al. [64] incorporated carboxylated carbon nanotubes (CNT-COOHs) into an epoxy resin (EP)/polyphenylene sulfide (PSF) blend to control the phase separation behavior of the ternary composite materials. The study demonstrated that the addition of CNT-COOHs enhanced the complex viscosity and curing reaction rate of the EP/PSF blend, significantly suppressing the early termination of the phase separation process. Additionally, the presence of CNT-COOHs improved the fracture toughness, mechanical properties, and thermal performance of the EP/PSF/CNT-COOH composites.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 3 Computational Results and Discussion
### Optimisation of an LSS System (System with Load Consumption)
#### 3.2.2 Electricity Price  
At the beginning of the system (first year), the RTP of Spain in 2023 was considered for the selling electricity prices. Figure 10 shows the hourly selling electricity price in the first year, with an average of 0.087 EUR/kWh, a maximum of 0.22 EUR/kWh, a minimum 0 EUR/kWh, and a standard deviation of 0.041 EUR/kWh.  
The daily difference between the maximum and minimum selling electricity prices should be known, as the profitability of arbitrage depends on these differences. The values for the first year are shown in Figure 11, with an average of 0.0733 EUR/kWh, maximum of 0.19 EUR/kWh, minimum of 0.00433 EUR/kWh, and a standard deviation of 0.0315 EUR/kWh. Figure 12 shows the probability density of the daily difference between the maximum and minimum selling prices for the first year, in which the Gaussian curve is the best fit.  
Figure 11: Daily maximum minus minimum sell price difference (EUR/kWh) for the first year.  
Figure 10: Hourly sell electricity price for the first year.  
The hourly purchase price was assumed to be the same as the RTP selling price. A fixed access charge of 0.02 EUR/kWh was added to the cost of electricity purchased from the grid.  
The prices for the next 25 years were obtained using \(F_{PV}\) = 0.3 and \(F_{wind}\) = 0, considering the uncertainty in the annual inflation rate for the electricity price with an average value of \(Inf_{P\_{E\_ave}}\) = 1% and a standard deviation of \(\sigma_{PrE}\) = 0.5% (inflation for each year is shown in Figure 13, which also shows the cumulative inflation during the years, highlighting cumulative inflation for year 12). Figure 14 shows the daily average hourly electricity sale price for different years, all referring to the beginning of the system (year 0). We can see the 'duck curve' effect with the years.  
Figure 12: Probability density function of the daily maximum minus minimum sell price difference (EUR/kWh) for the first year.  
Figure 13: Annual and cumulated inflation for the electricity price (%).
## 7 Unbalanced PSI-CA Protocol Based on Dual-Cloud Assistance
### Protocol Process
#### 7.2.3 Intersection  
1. \(H_{1}\) and \(H_{2}\) send data: \(H_{1}\) and \(H_{2}\) each send their respective collections \(C_{1}\) and \(C_{2}\) to the large medical institution.
2. Large medical institution processes data: Upon receiving the data, the large medical institution uses its private key \(\alpha\) to compute \(C_{1,j}^{\prime}=C_{1,j}^{\alpha}=E_{pk_{c}}(r_{1}j_{i}\omega_{j})^{\alpha}\) and sends the results back to \(H_{1}\). It also processes \(C_{2,j}^{\prime}=C_{2,j}^{\alpha}=E_{pk_{c}}(r_{2}j_{i}\beta_{j})^{\alpha}\) and sends the results back to \(H_{2}\).
3. \(H_{1}\) processes data: After receiving data from the large medical institution, \(H_{1}\) uses the random number \(\omega_{j}\) to calculate \(C_{1,j}^{\prime\prime}=C_{1,j}^{\prime 1/\omega_{j}}=E_{pk_{c}}(r_{1}j_{i} \omega_{j})^{a*1/\omega_{j}}\) and sends the results to \(H_{2}\).
4. \(H_{2}\) processes data: Upon receiving data from \(H_{1}\) and the large medical institution, \(H_{2}\) calculates \(C_{2,j}^{\prime\prime}=C_{2,j}^{\prime 1/\beta_{j}}=E_{pk_{c}}(r_{2}j_{i} \beta_{j})^{a*1/\beta_{j}}\). \(H_{2}\) checks if \(item_{j}=C_{1,j}^{\prime\prime}*C_{2,j}^{\prime\prime}\) exists in \(CF\). If \(item_{j}\) exists in \(CF\), then sum is incremented by 1 (initial value of sum is 0).
5. Obtaining the intersection cardinality: The small health app developer obtains the cardinality of the intersection by calculating \(sum-k\), where \(k\) is the cardinality of the set \(Y^{\prime}\).