esa-sceva/satcom-chunk-collection huggingface dataset:
# Limit Equilibrium Analysis of Landfill Instability Based on Actual Failure Surface
## 5 Conclusions  
In this paper, a landfill instability analysis method based on the actual failure surface is proposed. Compared with the traditional wedge model, the safety of landfill slopes with different failure modes can be more accurately evaluated. The specific conclusions are as follows:  
1. The phenomena of previous centrifugal model tests show that the penetrating crack of the instability failure of the landfill under a high water level is not vertical to the horizontal plane, as in the traditional wedge model; so, the limit equilibrium analysis method was improved based on the actual failure surface angle. The interface between the active wedge and the passive wedge was changed to the failure surface in the process of slope slip, and the results of the centrifugal experiment were verified, which has important implications for predicting the stability of the landfill slope;
2. The proposed model was checked according to the results of the previous centrifuge tests. The wedges were divided according to the failure surface in the process of model instability and slip, and the limit equilibrium analysis was carried out. The obtained \(FS\) is closer to 1.0, which means close to the unstable state;
3. In the two-wedge model, the factor of safety hardly changes under the condition of a particular value of water level ratio. As the failure surface angle of the slope continues to increase and the water level rises, the safety factor of the slope decreases significantly. The friction angle of the passive wedge in the wedge model significantly affects the stability of the entire slope;
4. After analyzing the parameter-sensitivity of the three-wedge model based on the actual failure surface, it was found that with the increase in failure surface angle, the \(FS\) of the slope gradually increases. The soil properties and the parameters of the retaining dam significantly affect the stability of the slope.  
J.L.: supervision and writing--review and editing. R.C.: conceptualization, methodology, formal analysis, and writing--original draft. H.L.: supervision and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work presented in this paper was jointly supported by the Basic Public Welfare Research Program of Zhejiang Province (Grant No. LGF21E080013) and the Basic Science Center Program for Multiphase Media Evolution in Hypergravity of the National Natural Science Foundation of China (Grant No. 51988101).  
Not applicable.  
Data Availability Statement: Data can be acquired upon reasonable request.  
The authors also would like to gratefully acknowledge the laboratory support personnel at the Center for Hypergravity Experimental and Interdisciplinary Research and MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Five-System Integrated PPP Time Transfer Model  
In this contribution, the dual-frequency code and phase observations from GPS, GLONASS, Galileo, BDS (BDS-2 and BDS-3), and QZSS are used to form the ionospheric-free (IF) combination to eliminate the influence of first-order ionospheric delay, which can be expressed as:  
\[\left\{ \begin{array}{l}P_{IF}^{s,Q}=\rho_{r}^{s,Q}+cdt_{r}^{Q}-cds^{s,Q}+T_{ r}^{s,Q}+b_{IF}^{Q}+b_{IF}^{s,Q}+\varepsilon(P_{IF}^{s,Q})\\ L_{IF,r}^{s,Q}=\rho_{r}^{s,Q}+cdt_{r}^{Q}-cdt^{s,Q}+T_{r}^{s,Q}+N_{IF,r}^{s,Q}+B_ {IF}^{Q}+B_{IF}^{s,Q}+\varepsilon(L_{IF,r}^{s,Q})\end{array} \right. \tag{1}\]  
where \(s\) and \(r\) denote a GNSS (or RNSS) satellite and a receiver, respectively, \(Q\) is the navigation satellite system corresponding to the used satellite in the specific equation, the subscript \(IF\) denotes the dual-frequency IF combination, \(P\) and \(L\) are code and phase observations in meters, respectively, \(\rho\) is the geometric distance between satellite and receiver, \(cdt_{r}\) and \(cdt^{s}\) represent the receiver clock offset and satellite clock offset, respectively, \(T\) denotes the slant tropospheric delay, \(N\) is the phase ambiguity in meters, \(b_{r}\) and \(b^{s}\) represent the receiver-dependent and satellite-dependent code hardware delays, respectively, \(B_{r}\) and \(B^{s}\) represent the phase hardware delays at the receiver and satellite, respectively, and \(\varepsilon\) denotes the measurement noise including multipath error. The dual-frequency IF combined observables are usually used for precise clock estimation (PCE). As the frequency-independent physical clock errors of the satellite cannot be estimated in an unbiased manner because of rank deficiency, the satellite clock estimates can be formulated as:  
\[cdt_{IF}^{s,Q}=cdt^{s,Q}-b_{IF}^{s,Q}+dD^{AC,Q} \tag{2}\]  
where \(dD^{AC}\) is the satellite clock datum introduced by the respective analysis center (AC) during PCE. In view that the time scales as well as the satellite clock datums of each GNSS (or RNSS) are defined differently, and the corresponding receiver-dependent code hardware delay differs from each other, it is necessary to introduce ISB parameters to compensate for the inconsistency. Besides, it is generally believed that the ISB between BDS-3 and BDS-2 should be considered due to the different quality of observations. This contribution takes the receiver clock offset of GPS as the datum in the five-system combined PPP time transfer model, and introduces ISB parameters into the observation equations for other satellite systems. Similarly, in the BDS-only observation model, the receiver clock offset of BDS-3 is used as the datum, and the ISB parameters are introduced into the observation equations of BDS-2. After applying the precise satellite orbit and clock offset products and using a priori model to correct the tropospheric dry delay, the linearized five-system PPP time transfer model can be described as follows:  
\[\left\{
# Gaussian Processes for Signal Processing and Representation in Control Engineering
## 3 Computational Methods of Gaussian Process
### Approximation of Gaussian Process
GP is approximated with the use of a linear model, by the use of basis function expansion. This way, the whole computation and inference can be done significantly quicker. Linear structure has advantages like simplifying approximation computation and making it easier to use GP as a latent function, when we approach non-Gaussian models, which gives more overall flexibility. The Laplace eigenfunctions computation is independent from the choice of kernel (and its hyperparameters). It also can be performed analytically. The idea is to create the covariance operator of a stationary covariance function as a pseudo-differential operator constructed as a series of Laplace operators. This operator \(\mathcal{K}\) (over function \(f(x)\) ) is formulated as in Formula (22):  
\[\mathcal{K}f(\mathbf{x})=\int k\big{(}\mathbf{x},\mathbf{x}^{\prime}\big{)}f\big{(}\mathbf{x} ^{\prime}\big{)}\mathrm{d}\mathbf{x}^{\prime}, \tag{22}\]  
where \(k(\mathbf{x},\mathbf{x}^{\prime})\) is of course covariance function. This operator is approximated with Hilbert space methods on a compact subset, subject to boundary conditions. This is required to the the approximation of a stationary covariance function as a series expansion of eigenvalues and eigenfunctions of the Laplacian operator. Mathematical details provided by [PERSON] and [PERSON] in [18] give a concrete approximation of covariance, featured in Formula (23):  
\[k\big{(}\mathbf{x},\mathbf{x}^{\prime}\big{)}=\sum_{j}S\Big{(}\sqrt{\lambda_{j}}\Big{)} \phi_{j}(\mathbf{x})\phi_{j}\big{(}\mathbf{x}^{\prime}\big{)}, \tag{23}\]  
where \(S(\cdot)\) is spectral density of the covariance function, \(\lambda_{y}\) is is the \(j\)th eigenvalue and \(\phi(\cdot)\) is the eigenfunction of the Laplace operator. Calculation of such formula is relatively easy, because of it consist of evaluating the spectral density at the square roots of the eigenvalues and multiplying them with the eigenfunctions of the Laplace operator [18]. Such approximated GP is then called HSGP. The authors of [9] have created a whole framework, which allows us to work with HSGP with, e.g., Stan and the MCMC sampling method. Moreover, they added methods for GP with periodic covariance functions.
## 2 Problem Description  
### System Model  
For the remainder of this paper, we consider the case of a BS operating in a single cell environment, with our focus being on the specific issue of downlink transmission. For these purposes, the cell in question is comprised of a BS and \(n\) user terminals, with each user terminal being indexed as user-\(k\) (including both RT users and NRT users). In addition, we assume for the sake of simplicity that there are \(c\) adjacent CCs available to be taken for aggregation in the BS within the same frequency band. Next, assume that all the CCs have equal bandwidth and also that the \(i\)th (\(i=1,2,\ldots,c\)) CC has \(b_{i}\) resource blocks (RBs), where a single RB is herein designed to consist of the smallest allocation unit to be used in resource scheduling. As such, given the aggregation of multiple CCs for transmission, there are a total of \(b_{\text{total}}=\sum_{i=1}^{c}b_{i}\) RBs that can be utilized for packet transmissions. From a system-level perspective, the design of the conceptual operation of our proposed system model is illustrated in Figure 1.  
Figure 1: The proposed system model, which consists of a classifier, a real-time (RT) queue, a non-RT (NRT) queue, a transmission queue, a scheduler, and \(c\) component carriers (CCs) with the \(i\)th CC containing \(b_{i}\) resource blocks (RBs), where \(i=1,2,\ldots,c\), for downlink transmission.  
In this system, the first step consists of the classification of all of the arriving packets by the classifier as either RT packets or NRT packets. The packets are then sent on, according to their classification, to either the RT queue or the NRT queue on a first-come-first-served basis. It is then that the proposed packet scheduling algorithm (which will be detailed in Section 3) employed in the scheduler comes into play, with the packets in both the RT queue and the NRT queue being scheduled as appropriate for simultaneous parallel transmission from CCs according to the requirements of the algorithm. As noted above in Section 1, the classic PF criterion [27], which is itself detailed in Section 3, is the foundation upon which the proposed packet scheduling algorithm is based. In addition, the buffer size is assumed to be infinite for each queue shown in Figure 1.  
Because of its high robustness against multi-path fading, as well as its high spectral efficiency and bandwidth scalability, orthogonal frequency division multiplexing access (OFDMA) radio technology [28] is utilized as the underlying downlink radio access scheme in the proposed system model. Furthermore, we define the duration of a given OFDMA downlink frame, which is denoted as \(t_{\text{OFDMA}}\), to consist of a round for scheduling packets, where, it should be noted, the current round at a given time is identified by the notation \(s\).
## 1 Introduction  
Modern radiocommunication systems, both civil and military, adopt complex modulation schemes with wideband signals so that the real-time emulation of the propagation of such signals has become a complex task to accomplish. A channel emulator, usually unable to get access the transceiver baseband, must process the RF signal over a wideband spectrum that sometimes is much wider than that of the basic waveform due to the presence of frequency hopping or other diversity techniques. Moreover, the signal processing has to be performed with a constant and predictable latency in order to carry out the emulation correctly. Such conditions give advantage to the emulator to operate with no knowledge of the waveform characteristics, which can be unknown or confidential, but it must introduce all the impairments on the signal while limiting the introduction of spurious components due to its operation as much as possible.The propagation of a signal through a channel is affected by well known phenomena [1] and several statistical models are currently available in order to give system engineers powerful tools to take into account fading effects on communication systems. Unfortunately, when the instantaneous bandwidth of the signal is wide, the mobile platform is a high or very high speed one and the emulation of the propagation must be performed independently from the waveform working directly on a wideband spectrum, most of the available models become useless. When the trajectory of the mobile platform is quite predictable, such as UAVs (Unmanned Aerial Vehicle), it is possible to use DEM models to estimate the number of excited paths during the motion, reflection coefficients, angles of arrivals, path distance evolution laws and several other parameters needed for the emulation of the propagation. In this paper, the obtainable spectral performance when the Doppler effect is implemented through a time expansion in a wideband RF channel emulator is investigated.
# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### Patterns Obtained Using Other Sensors
Each affective state was described using three parameters: arousal, valence, and stance (discussed in the level of interest section in this paper). A weight W, which was calculated by summing the contributions of each parameter, was assigned to each expression and then multiplied by the intensity (I) of that expression to compute the level of interest (L) using L = W \(\times\) I. The proposed algorithm for facial expression recognition achieved an average accuracy of 90.9% using a Cohn-Kanade database. The algorithm had significantly higher accuracy in recognizing happiness, sadness, anger, and surprise when compared to fear and disgust. This study suggests that a two-step classification system is more efficient than a direct classification system, as the authors obtained better results when using both K-nearest neighbors and discrete HMM algorithms compared to continuous HMM. To validate their analysis of expressions and level of interest, the authors conducted an experiment with 21 subjects watching movie clips. The data were labeled with six emotions by three individuals who did not know the content of the videos. The recognition accuracy of the proposed algorithm was 82.1%. Furthermore, in order to use real data instead of laboratory data, the authors collected 108 sequences from television broadcasts, including different facial expressions used worldwide. Three individuals labeled these data. The accuracy achieved was 72% due to the misclassification of surprise, fear, anger, and disgust, in addition to the unbalanced distribution of subject sex, age, and ethnicity between the test and training sets. The algorithm used had difficulty classifying the emotions correctly when the subject was talking. It also tended to misclassify expressions with similar patterns, such as fear and surprise, and sadness and disgust. The above studies discuss the feasibility of using sensors that depend on the pose or the dynamic of the subject's posture to detect interest. There was a need to use more than one sensor at a time to improve classification accuracy. Compared to the EEG, ECG, and eye-tracking, these sensors had less accuracy and required more time and resources to handle and synchronize the data. However, it provided an insight into the effect of interest on physiological measures. Moreover, the subjective measures used especially by the academic staff had increased the reliability of the physiological measure. That could be a significant contribution to improve the educational environment by strengthening the student-educator relationship.
###### Abstract  
**Background/Objectives:** This study aimed to investigate the associations of anthropometric characteristics with performance and potential biomarker changes resulting from a continuous 10 h ultra-marathon swimming effort in regional-level swimmers. **Methods:** Nine adult male swimmers (age: 43 \(\pm\) 6 years) participated in a 10 h swim in a 50 m outdoor pool, self-managing their nutrition and hydration breaks. Pre- and post-swim measurements included body weight (BW), body fat percentage (BF%), limb lengths (LL), circumferences (C), lean mass (LM), body mass index (BMI), skinfold thicknesses, heart rate (HR) and blood pressure (BP). **Results:** A significant reduction was observed in bicep skinfold thickness (Pb) (\(p\) = 0.022), while both HR and systolic BP increased post-effort (\(p\) = 0.030 and \(p\) = 0.045, respectively). Also, most anthropometric parameters, such as BMI, LM, and some C, remained unchanged (\(p\)\(\geq\) 0.05). A statistically significant negative correlation was found between post-swim hip circumference (Ph) and total swimming distance (\(r\) = -0.682, \(p\) = 0.043). **Conclusions:** While most anthropometric traits remained stable and unrelated to performance, isolated changes in specific biomarkers indicate a physiological response to prolonged exertion. Although pacing and nutritional strategies were not directly examined, observational data--such as consistent swimming rhythm, time allocation for active recovery (AR), and structured carbohydrate intake--suggest these factors may have contributed to performance maintenance and probably the lack of body composition differences after the ultra-marathon effort. These insights are interpretive and align with the existing literature, highlighting the need for future studies with targeted experimental designs.  
**Keywords:** sports performance analysis; anthropometric characteristics; biomarkers changes; ultra-marathon swimming; endurance performance; regional swimmers +
Footnote †: 2020: _Applied sciences_
## 4 Results and Discussion
### Time Complexity and Order of the Proposed Method
It is observed from the analysis that deep learning models used in [30; 31] have more time complexity, training epochs, and model parameters as compared to the proposed model. The author used handcrafted features with KNN as a classifier, which is less computationally expensive than the proposed algorithm. Still, the inference time of prediction is longer, and our methodology has a higher accuracy rate. The spatial feature learning network in the paper [33] has implemented a lightweight network by merging features from two convolutional layers with multiscale filters. This model has 3.8 million trainable parameters and an average inference time of 2.23 s, which is comparatively more than the proposed model. The vision transformer in [34] used eight layers of the encoder with four heads with seven million parameters, which is computationally expensive. The authors in [21] have used ensemble network architecture employing ResNet50 with an attention module with more training parameters and epochs. The proposed method performs superiorly to the other existing methods on all benchmark datasets due to the following reasons: (i) The model combines the handcrafted features from the HOG feature descriptor with the CBAM attention-based CNN features that gather meaningful information from two different streams for better representation of gesture images. (ii) The model acquired good recognition accuracy with fewer model parameters and built an appropriate balance between accuracy and efficiency. (iii) The  
Figure 11: Confusion matrix for classification of ISL database. framework recognized all the sign gestures accurately with two misclassifications in both datasets, which is comparatively less compared to other existing methods.
## 3 Results
### The LAI Values between Different Experimental Treatments  
The LAI values of different treatments are shown in Table 4. Compared with CK, the LAI of T1 and T2 increased significantly. For instance, the LAI of T1 increased by 4.58%, 3.30%, and 3.86% at DAH 14d, DAH 21d, and DAH 28d, respectively. Furthermore, the average values of LAI for T1 and T2 in different periods were 5.03 and 5.42, respectively. Compared with T1, the LAI of T2 increased by 7.62%, 7.39%, and 8.06% at DAH 14d, DAH 21d, and DAH 28d, respectively. It was also found that the distribution of LAI in 2017 was consistent with 2016. The behavior of the change in LAI under the shading treatment indicates that an increase in diffuse radiation fraction led to an increase in rice LAI.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Years**} & **Days after** & \multirow{2}{*}{**Treatments**} & \multirow{2}{*}{**SPAD**} & \multirow{2}{*}{**Years**} & **Days after** & \multirow{2}{*}{**Treatments**} & \multirow{2}{*}{**SPAD**} \\  & **Heading** & & & & & & \\ \hline \multirow{8}{*}{2016} & \multirow{4}{*}{DAH 14d} & CK & \(43.64\pm 1.47\) a & & & CK & \(44.56\pm 0.61\) a \\  & & T1 & \(45.35\pm 1.02\) b & & DAH 14d & T1 & \(46.10\pm 1.09\) b \\  & & T2 & \(46.40\pm 0.61\) c & & & T2 & \(47.13\pm 0.09\) c \\  & & CK & \(40.79\pm 1.00\) a & & & CK & \(39.73\pm 1.05\) a \\  & & T1 & \(41.65\pm 0.88\) b & 2017 & DAH 21d & T1 & \(40.81\pm 0.92\) b \\  & & T2 & \(42.56\pm 1.32\) c & & & T2 & \(41.54\pm 1.62\) c \\  & & CK & \(31.83\pm 0.36\) a & & & CK & \(33.11\pm 0.36\) a \\  & & T1 & \(32.21\pm 0.13\) b & & DAH 28d & T1 & \(33.71\pm 1.12\) b \\  & & T2 & \(32.63\pm 0.40\) b & & & T2 & \(34.03\pm 1.13\) b \\ \hline \hline \end{tabular}
\end{table}
Table 3: The SPAD of rice under different treatments at different periods after heading in 2016–2017.