esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bipolar pqr-Spherical Fuzzy Aggregation Operators
### Bipolar pqr-Spherical Fuzzy-Weighted Average Operators  
In this subsection, the weighted average operator for aggregating \(\mathrm{B_{pqr}SFNs}\) is introduced, and its key characteristics are examined.  
**Definition 12**.: _Let \(\check{\tau}_{j}=(\theta_{\bar{\tau}_{j}}^{+},\eta_{\bar{\tau}_{j}}^{+}, \lambda_{\bar{\tau}_{j}}^{+},\theta_{\bar{\tau}_{j}}^{-},\eta_{\bar{\tau}_{j}}^ {-},\lambda_{\bar{\tau}_{j}}^{-})\), for \(j=1,2,\ldots,l\), be a collection of \(B_{pqr}SFNs\), then the formulation of the bipolar pqr-spherical fuzzy-weighted average (\(B_{pqr}SFWA\)) operator is expressed as follows:_  
\[B_{pqr}SFWA(\check{\tau}_{1},\check{\tau}_{2},\ldots,\check{\tau}_{l})=\bigoplus _{j=1}^{l}\ \Big{(}\epsilon_{j}\cdot\bar{\tau}_{j}\Big{)},\]  
_whereas \(\epsilon=(\epsilon_{1},\epsilon_{2},\ldots,\epsilon_{l})\) is meant to the weight of \(\check{\tau}_{j}\) for \(j=1,2,\ldots,l\) such that \(\sum_{j=1}^{l}\epsilon_{j}=1\) and each \(\epsilon_{j}>0\)._  
The following theorems are derived from the \(\mathrm{B_{pqr}SFNs}\) operating rules.  
**Theorem 3**.: _The \(B_{pqr}SFWA\) operator is defined as follows:_\[B_{pqr}SFWA(\tau_{1},\tau_{2},\ldots,\tau_{l})= \Bigg{(}\sqrt[2]{1-\prod_{j=1}^{l}(1-(\theta_{\tau}^{+})^{2p})^{ \epsilon_{j}}},\prod_{j=1}^{l}(\eta_{\tau}^{+})^{\epsilon_{j}},\prod_{j=1}^{l}( \lambda_{\tau}^{+})^{\epsilon_{j}},\ -\prod_{j=1}^{l}(-\theta_{\tau}^{-})^{ \epsilon_{j}}, \tag{2}\] \[-\sqrt[2]{1-\prod_{j=1}^{l}(1-(\eta_{\tau}^{-})^{2q})^{\epsilon_{ j}}},-\sqrt[2]{1-\prod_{j=1}^{l}(1-(\lambda_{\tau}^{-})^{2r})^{\epsilon_{j}}} \Bigg{)}.\]  
Proof.: See Appendix A (4).  
**Theorem 4** (Idempotency).: _Let \(\tau_{j}=(\theta_{\tau_{j}}^{+},\eta_{\tau_{j}}^{+},\lambda_{\tau_{j}}^{+}, \theta_{\tau_{j}}^{-},\eta_{\tau_{j}}^{-},\lambda_{\tau_{j}}^{-})\) denote a family of \(B_{pqr}SFNs\), for \(j\in\{1,2,\ldots,l\}\). If \(\tau_{j}=\tau=(\theta_{\tau}^{+},\eta_{\tau}^{+},\lambda_{\tau}^{+},\theta_{ \tau}^{-},\eta_{\tau}^{-},\lambda_{\tau}^{-})\) for all \(j\), then_  
\[B_{pqr}SFWA(\tau_{1},\tau_{2},\ldots,\tau_{l})=\tau.\]  
Proof.: See Appendix A (5).  
**Theorem 5** (Monotonicity).: _If \(\tau_{j}=(\theta_{\tau_{j}}^{+},\eta_{\tau_{j}}^{+},\lambda_{\tau_{j}}^{+}, \theta_{\tau_{j}}^{-},\eta_{\tau_{j}}^{-},\lambda_{\tau_{j}}^{-})\) and \(\tilde{t}_{j}=(\theta_{\tilde{t}_{j}}^{+},\eta_{\tilde{t}_{j}}^{+},\lambda_{ \tilde{t}_{j}}^{+},\theta_{\tilde{t}_{j}}^{-},\eta_{\tilde{t}_{j}}^{-},\lambda _{\tilde{t}_{j}}^{-})\) are two families of \(B_{pqr}SFNs\) indexed by \(j=1,2,\ldots,l\), and if \(\tau_{j}\leq\tilde{t}_{j}\) for all \(j\), then_  
\[B_{pqr}SFWA(\tilde{\tau}_{1},\tau_{2},\ldots,\tilde{t}_{l})\leq B_{pqr}SFWA( \tilde{t}_{1},\tilde{t}_{2},\ldots,\tilde{t}_{l}).\]  
Proof.: See Appendix A (7).
## 4 Empirical Study
### Data Preprocessing  
DFSS facilitates the collection and preprocessing of crucial data for the chosen sector, enabling their integration into the prediction models. The experiments were executed across ten sectors, adhering to the FnGuide industry classification standard (FICS). The sectors selected encompass energy, materials, industrials, consumer discretionary, consumer staples, healthcare, financials, information technology (IT), communication services, and utilities. Data collection was tailored to amass features that provided comprehensive information, thereby allowing the prediction model to forecast the direction of the index accurately.  
For each sector, data encompassing six primary classes--price indicators, technical indicators, economic indicators, financial indicators, fundamental indicators, and market sentiment indicators--were meticulously gathered, incorporating a total of 67 features. A detailed list of these variables is presented in Table 1. The news sentiment index was computed using the Koelectra-base-finetuned model, which quantifies news data for the respective sector on a scale from 0 to 1.  
The proposed DFSS analyzes data from the last three years to determine the optimal feature combination at each point, considering both accurate performance and stability. The empirical study utilized data from 1 March 2020 to 31 March 2022.
# Adaptive Modem Based on LSTM-AutoEncoder with Vector Quantization
###### Abstract  
Recently, researchers have achieved the goal of using unified architecture to achieve multiple modulation modes under specific conditions. However, existing research still suffers from the problem of large resource and time overhead. This paper proposes an adaptive modem based on a vector quantization (VQ) long short-term memory autoencoder (LSTM-AE), designed to implement modulation and demodulation of signals from the second to thirty-second order in a lightweight way. By leveraging the memory capacity of the LSTM module and the compression capability of the autoencoder, the model is able to support multi-order modulation methods. This study used the Adam optimizer for training and testing on a simple dataset extended by adding AWGN noise only and made modifications based on the MSE loss function to balance the accuracy and training speed of each part of the model. Experiments demonstrate that the proposed method not only achieves comparable modem performance to existing frameworks for second to thirty-second order signals, but also significantly reduces the number of parameters and training time. Experimental results indicate that the proposed methodology not only matches the performance of existing frameworks for signals ranging from the second to the thirty-second order, but also employs merely 79.6% of the average parameter count and a mere 7.4% of the average training duration. This represents a substantial reduction in resource expenditure.  
adaptive modem; autoencoder; vector quantization +
Footnote †: journal: Electronics
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 6 Results and Discussion  
The proposed system is designed with a motivation to provide a various error protection scheme based on data partitioning technique of H.264 while utilizing the innate unequal error protection capability of the employed channel codes. Furthermore, space-time signal construction or SP modulation aided DSTS technique has been incorporated to provide the video quality to mobile handset systems on time-varying correlated Rayleigh fading.
## 4 Pool-Experiments Verification and Discussion
### The Joint Control Pool-Experiments of the Underwater Manipulator
#### 4.1.2 Experiments of Joint Continuous Trajectory Tracking  
In the experiments of joint continuous trajectory tracking, consistent with the method in [17], the sinusoid was selected as the target trajectory. The target sinusoidal trajectory of the (neck, shoulder, elbow, wrist) joints is as shown in the following Equation (32).  
\[\eta=A\sin(\frac{2\pi}{100}t)+B \tag{32}\]  
where, \(\eta=(\eta_{1},\,\eta_{2},\,\eta_{3},\,\eta_{4})^{\rm T}\), \(A=(50^{\circ},\,30^{\circ},\,50^{\circ},\,30^{\circ})^{\rm T}\), \(B=(90^{\circ},\,90^{\circ},\,120^{\circ},\,150^{\circ})^{\rm T}\).  
The pool-experiments results of the proposed method and the method in [17] are shown in Figure 4. Moreover, and the relevant indicator data in Figure 4 are summarized into Table 4 for a quantitative analysis. For the experimental analysis, the \"Maximum absolute tracking error (MATE), Standard deviation of error (SDE)\" was used as the evaluation index. At the same time, to illustrate the effect of the proposed method on reducing the joint lag, increase the \"motion lag time\" as the evaluation index.  
Figure 4: _Cont._  
From Table 4, in comparison with the method in [17], the results confirm that the proposed method can reduce the trajectory tracking error, and reduce the influence of joint lag on the joint control of underwater manipulator. The results verified the effectiveness of the proposed method.
## 4 Conclusions  
This study investigated the influence of different preparation methods on the critical performance and microstructure of SBS-modified asphalt. Different variables, including shearing rate, shearing temperature, shearing time, development time, and sulfur addition were considered. The LAS test and MSCR test were conducted to evaluate the asphalt's high-temperature resistance and fatigue resistance. Moreover, the Burgers model was employed for viscoelasticity analysis. The detailed conclusions are as follows.  
* The recommended preparation method is 2 h of shearing at 180 \({}^{\circ}\)C, 4000 r/min, with sulfur addition and developing for 6 h. Sulfur addition and the shear rate have the most significant impact on the performance of SBS-modified asphalt;
* The microstructure captured by the fluorescence image shows that an intermediate shearing temperature, high shearing rate, and extended shearing time render the distribution of SBS polymer more even;
* Sulfurized SBS-modified asphalt shows optimal performance after 6 h of development (25% decrease in Jnr and 28.7% increase in N\({}_{\text{J}}\)), and extended development hinders its  
Figure 12: Cumulative proportions of the three deformations at the end of loading.  
performance (137.5% increase in Jnr and 6% decrease in N\({}_{l}\)), which is due to the heat sensitivity of the C-S and S-S bonds;
* Without sulfur, SBS-modified asphalt shows monotonously improving performance with extended development time (48.7% decrease in Jnr and 36.7% increase in N\({}_{l}\)). However, after 18 h of development, it still demonstrates much lower high-temperature performance and slightly lower fatigue performance than sulfurized SBS-modified asphalt.  
Conceptualization, Y.W. and H.Y.; methodology, P.L.; software, C.C.; validation, Y.W., H.Y. and S.Z.; formal analysis, Y.W.; investigation, Y.W.; resources, Y.W.; data curation, H.Y.; writing--original draft preparation, C.Y.; writing--review and editing, H.Y.; visualization, P.L.; supervision, C.C.; project administration, S.Z.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No.52008353, No.51908426); Sichuan Youth Science and Technology Innovation Research Team (No.2021)DTD0023, No.2022 JDTD0015); and Chengdu Technological Innovation R&D Project (2021-YF05-01175-SN).  
Not applicable.  
The authors gratefully acknowledge the financial support by the National Natural Science Foundation of China (52008353, 51908426), the Sichuan Youth Science and Technology Innovation Research Team (2021)DTD0023, 2022 JDTD0015), and the Chengdu Technological Innovation R&D Project (2021-YF05-01175-SN).  
The authors declare no conflict of interest.
## 4 Results and Discussion  
Experiments were performed with a pair of piezocomposite transducers in a 3-mm-thick, 800-mm-long and 100-mm-wide aluminum plate where transmitter and receiver were placed close to each end of the plate, separated by 700 mm. The plate is mounted over a bridge structure where variable tensile stress along the plate's length can be applied, as schematically shown in Figure 3, following the setup used in [27]. Piezocomposite transducers were used due to their wider bandwidth, compared to conventional piezoelectric ceramics. They were prepared following the standard dice and fill procedure [43] with a 0.65 mm thick PZ37 ceramic from Ferroperm [44] and widths of element and polymer of 0.1 and 0.04 mm, respectively, and 0.14 mm pitch. One ultrasonic transducer was used as transmitter and the opposite as receiver.  
Figure 2: Schematic of the filtering procedure used to obtain the new \(\phi\)-sensitive reference signal based on the impulse response at some known strain \(\tilde{\varepsilon}\): the overall procedure (**a**); and a detailed diagram of the filter block (**b**).  
An initial broadband pulse excites the transmitter generating several wave modes in the plate which propagate until being received by the opposite transducer. Here, reflections from the plate's ends were received, due to the transducer positioning, which, to some extent, increase the number of wave packets that arrive at the receiver. Figure 4 shows the reference signal, \(h(0,t)\), acquired due to the first propagating step, its Fourier and short-time Fourier transforms. The complex wave propagating phenomenon can be verified in Figure 4a, as multiple wave packets arrive at different time instants, with different frequency components. The cross-correlated focused signal under null strain, \(y(t)\), obtained through Equation (3), is shown in Figure 5. As can be seen, it has a high main peak, which concentrates a significant amount of the signal's energy.
## 3 OMA of Linear Time-Varying Structure Based on MWLPP
### The Applicable Scope of the Method  
In this paper, the application scope of MWLPP for OMA of linear slow-time-varying structure is,  
1. The method is only suitable for linear slow-time-varying structures with small damping. If the damping is too high, the modals will be complex. Reference [16] show that the damping ratio reaches 10%, and the operational modal parameters can also be identified by manifold learning. However, the lower the damping ratio, the better the identification of modal parameters effect. Only for linear slow-time-varying structures, based on the \"time-freezing\" theory, Equation (12) can be expressed as Equation (13).
2. The number of vibration response sensors \(n\) should be greater than or equal to the \(d\)-order modal identified by the method. According to Equation (2), the displacement response signal in modal coordinates is approximately represented by \(d\)-order modal (\(d\leq n\)). In addition, MWLPP can identify modal natural frequency, modal shape, and damping ratio of one mode with only 1 sensor.
3. The excitation vector to the structure should be approximately stationary Gaussian white noise.
4. The method can identify time-varying transient modal shapes, modal frequencies, and modal damping ratios from non-stationary vibration response signals. However, for linear slow-time-varying structures, mass reduction or motion will generate additional damping [41,42]. Therefore, the damping ratio identified by MWLPP cannot be directly compared with real values.  
Figure 4: OMA of linear time-varying structure based on moving window locality preserving projections (MWLPP) algorithm.
## 1 Introduction
The percentage distribution of UCS reduction for different rock types is shown in Figure 1. Evidently, the water-weakening extent on rock strength is highly dependent on the rock type, and likely reflects the great differences in lithology and texture. The UCS losses in the vast majority of rocks fall in the range from 0% to 60%. Compared to metamorphic and igneous rocks, sedimentary rocks generally suffer greater reductions in UCS. However, rock strength is susceptible to the test conditions [7; 24; 31; 32] such as strain rate, loading apparatus and ambient temperature. Test conditions are usually different between experiments. This results in the low degree of comparability of tested data. Hence, there is a need to research the UCS sensitivities to water of varied rock types under the same test conditions. To date, many mechanisms have been proposed to explain the water-induced strength reduction, such as fracture energy reduction [6; 33; 34], chemical and physical deterioration [35; 36; 37; 38], pore pressure increase [39; 40], capillary tension decrease [41] and frictional reduction [42; 43; 44; 45; 46]. Several mechanisms may act concurrently to control the deformation of wet rocks. However, none of these mechanisms can be uniquely determined for the great variety of rock types, but one or some are likely more significant than others for certain rock types and test conditions [7; 42; 47]. In this paper, to explore the water effects on the mechanical behavior of various rocks, three types of rock, namely sandstone, granite and marble, were selected as test materials. Uniaxial compression tests were performed on three rock types under dry and saturated conditions. Mechanical parameters such as UCS, elastic modulus and critical strain were obtained and then compared. The damage evolution within rocks during deformation was monitored by the acoustic emission (AE) technique. The different UCS sensitivities to water of different rock types were explained in terms of the differences in the minerology and texture.
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 5 Performance Analysis
### Overhead Analysis
#### 5.2.1 Communication Overhead
Note, when the adversary launches the report disruption attacks, DSEDA will incur an additional message overhead for compensating the lack of secret shares, and PKAEF will incur an additional message overhead for performing the substitution scheme or selecting a new CH. However, the additional communication overhead in DSEDA or PKAEF is acceptable, since it is associated with great advantages in data availability and security strength. The communication overhead for forwarding a report is affected by the length of the signed report message. The longer the length of the signed report message, the higher the communication overhead for forwarding a report at each hop. Suppose the format of the original event report in PDF is the same as that in PKAEF, \(M\) = {_CID_, \(ID_{dt}\), \(E\), \(L_{e}\), \(t_{e}\)}, and each original event report \(M\) is divided into \(C\) = {_ID_, \(E\), \(L_{e}\)} and \(V\) = {_CID_, \(ID_{ch}\), \(L_{e}\), \(t_{e}\)}. Suppose that the lengths of _CID_, \(ID_{ch}\), \(E\), \(L_{e}\), and \(t_{e}\) are 4 bits, 8 bits, 4 bits, 20 bits, and 32 bits respectively, then the size of \(M\) is 9 bytes. The 32-bit RC5 algorithm is adopted as the symmetric-key encryption algorithm in DSEDA, PDF, and PKAEF. Then, the length of a signed report message in PDF and PKAEF is 39 bytes (including an extra 3-byte header). _CID_ is not necessary in DSEDA, thus the format of the event report in DSEDA could be \(S\) = {_ID_, \(E\), \(L_{e}\), \(t_{e}\)}, and the size of \(S\) is about 8 bytes. DSEDA utilizes the bloom filter to reduce the size of IDs of the \(T\) participating nodes to 3 bytes and adopts the partial message recovery algorithm to reduce the signature overhead to 26 bytes. Suppose that the length of secret share \(S_{u}\) is 8 bytes and (\(T\), \(t\)) = (6, 5). Then, the length of a signed report message in DSEDA is 80 bytes (including an extra 3-byte header). Letting (\(T\), \(t\)) = (5, 4), then the length of a signed report message in DSEDA is 72 bytes (including an extra 3-byte header). It can be seen that the length of a signed report message in DSEDA is longer than that in PDF or PKAEF, which will result in higher communication overhead for forwarding a report at each hop.
## 3 Results  
### Spatiotemporal Dynamics of LDD from 1985 to 2015  
Using the Sen-MK method, the spatiotemporal dynamics of LDD in China from 1985 to 2015 were explored. Overall, China has experienced land development from 1982 to 2015 (Figure 2). The significant and extremely significant land development area spread across the country, such as the Loess Plateau area, the North China Plain, southwest China, the south Tibetan area and the north area of Xinjiang Uygur Autonomous Region, accounting for about 45% of the total area of China. Meanwhile, about 13% of China's land experienced degradation processes, and these were mainly distributed in the Yangtze River Delta, the Pearl River Delta and the Daxinganling Mountain area of northeast China and some parts of the northeast area of the Xinjiang Uygur Autonomous Region. The remaining 32% of China did not undergo significant change, which was mainly distributed in northwest China.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Slope of Residual** & **Significance Level** & **LDD Types** \\ \hline Slope \(\leq-0.0005\) & \(p<0.01\) & Significant decrease \\ Slope \(\leq-0.0005\) & \(0.01\leq p<0.05\) & Slight decrease \\ \(-0.0005<\text{Slope}<0.0005\) & \(p\geq 0.05\) & Nonsignificant change \\ Slope \(\geq 0.0005\) & \(p<0.01\) & Slight increase \\ Slope \(\geq 0.0005\) & \(0.01\leq p<0.05\) & Significant increase \\ \hline \hline \end{tabular}
\end{table}
Table 2: The change in effects of human activities on LDD.  
Figure 2: LDD processes in China from 1985 to 2015.  
The hot-spot analysis further showed the areas where LDD took place during the study period in China. As we can see in Figure 3, the hot spots representing the land development areas were mainly located in the Loess Plateau areas such as east Gansu Province, north Shaanxi Province and Shanxi Province, the North China Plain including south Hebei Province, the whole Henan Province, north Anhui Province and west Shandong Province, and southwest China embracing Yunnan Province, Guizhou Province and west Guangxi Zhuang Autonomous Region. In addition, there were hot spots distributed in the northwest Xinjiang Uygur Autonomous Region, Bohai Bay Basin and Sanjiang Plain in Jilin Province. The cold spots were mainly distributed in Guangdong Province, south Jiangsu Province, Shanghai, north Zhejiang Province and the east Inner Mongolia Autonomous Region. Additionally, some cold spots were also found in east Xinjiang Uygur Autonomous Region.
# Study on Vertical Uplift Resistance Characteristics of Pre-Drilled Planted Piles in Boulder Strata
###### Abstract  
Pre-drilled composite planted piles are a commonly used construction method, but there is currently limited research on the load-bearing characteristics of piles penetrating boulders. Therefore, a new type of pre-drilled composite planted pile suitable for sites with isolated boulders has been developed. Using transparent soil technology, vertical uplift bearing capacity tests were conducted on pre-drilled piles to investigate the uplift capacity and load transfer mechanism of piles with boulders. Additionally, a discrete-continuous coupled 3D numerical model was employed to analyze the influence of boulder position on the pile's uplift bearing capacity. The results indicate that the ultimate uplift bearing capacity of piles with boulders in the pile body is approximately twice that of piles without boulders, and this difference decreases with the increase of the distance between boulder and the pile end; under ultimate uplift loading, the pile end of a pile containing boulders contributes approximately 32% of the uplift bearing capacity, whereas the pile end without boulders contributes almost no uplift capacity; the presence of boulders increases the cross-sectional diameter of the pile, providing additional vertical support from the overlying soil, thereby significantly enhancing the ultimate uplift bearing capacity of the pile. Additionally, the boulder also increases the pathways for load transfer to the surrounding soil, further expanding the impact area on the soil surrounding the piles. Parameter analysis of the position of the boulder in the pile body reveals that under various conditions (boulder distances from the pile tip of 0, 50, 100, and 150 mm), both the ultimate uplift bearing capacity and the angle of the sliding failure surface decrease gradually as the depth of boulder decreases. This study provides a novel solution for pile foundation construction in similar boulder strata.  
pre-drilled planted piles; boulder; model testing; numerical simulation +
Footnote †: journal: Applied Sciences