esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Vibration Characteristics of Multi-Beam Structures with Stick and Slip at Joints
## 3 Result Analysis and Discussion
### Effect of Excitation  
The connection pressure, friction coefficient, and connection stiffness collectively represent the inherent characteristics of the joint structure, while, besides the above characteristic parameters, changes in the connection state are also related to the magnitude of external excitation. In this section, the influence of excitation on the structural nonlinear vibration response is analyzed. Figure 13 illustrates the structural frequency response curve under different excitation amplitudes, where the connection stiffness and friction coefficient are consistent with Section 3.2, and the connection preload \(F_{N}=500\) N.  
When the excitation amplitude is small, the resonance peaks in the frequency response curve at both ends of the connection joint are sharp, corresponding to the linear system characteristic of smaller excitations being unable to cause slipp  
Figure 12: The relationship between equivalent damping and excitation frequency under different levels of connection stiffness. **(a)** Fixed support boundary; **(b)** simply supported boundary.  
Figure 13: Frequency response curves under different excitation amplitudes. **(a)** Fixed support boundary, \(L_{1}\) connection point; **(b)** fixed support boundary, \(L_{2}\) connection point; **(c)** simply supported boundary, \(L_{1}\) connection point; **(d)** simply supported boundary, \(L_{2}\) connection point.  
As the excitation amplitude increases, the resonance peaks become broader, and peak 'clipping' occurs, with the highest peak frequency shifting to the left. Under fixed boundary conditions, the resonance frequency range is wider, the 'clipping' phenomenon is more obvious, and there is a relatively flat frequency response line. Conversely, under simply supported boundary conditions, there is a more significant difference in equivalent stiffness and damping (as shown in Figures 14 and 15) with changes in excitation, so the peak changes in the frequency response curve are more complex. As the excitation increases, the nonlinear characteristics of the system's response become more apparent.
# Respiration Monitoring for Premature Neonates in NICU
## 3 Methods
### Motion Matrix Calculation
#### Conventional Optical Flow  
The optical flow should satisfy Equation (1):  
\[\frac{\partial\mathbf{I}}{\partial x}V_{x}+\frac{\partial\mathbf{I}}{\partial y }V_{y}+\frac{\partial\mathbf{I}}{\partial t}=0, \tag{1}\]  
where \(\mathbf{I}\) is the intensity matrix of a frame in a video and \(V_{x}\) are \(V_{y}\) are the actual optical flow parameters. We deployed the classic dense optical flow algorithm proposed by [PERSON] et al. [18], where second-order differential equations based on the _Hessian_ matrix are used to constrain two-dimensional (2D) velocity. The [PERSON] method creates flow fields with 100% density. However, the conventional optical flow method is limited at estimating motion in poorly textured areas, which lack gradient variation.
# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 3 The Proposed Algorithm  
In SLAM, accumulated sensor errors, measurement noise, complex environments, and abrupt changes in robot motion states not only introduce system nonlinearities but also often result in non-Gaussian noise distributions. Conventional nonlinear optimization algorithms, including the aforementioned SRUKF-SLAM approach, universally adopt the MMSE optimization criterion. As evident from Equation (20), their cost functions incorporate only first-order (mean) and second-order (variance) statistics of the error signals, failing to exploit the higher-order statistical characteristics (e.g., skewness and kurtosis) of non-Gaussian noise. Consequently, MMSE-based estimators exhibit heightened sensitivity to outliers under non-Gaussian noise conditions, leading to significant performance degradation in state estimation. Research has demonstrated that replacing the MMSE criterion with the Maximum Correntropy (MC) Criterion [45; 46] in conventional nonlinear filtering algorithms can effectively suppress non-Gaussian noise interference. In order to improve the estimation accuracy of the SRUKF-SLAM algorithm in non-Gaussian noise, this paper proposes the Maximum Correntropy Square Root Unscented Kalman Filter SLAM (MCSRUKF-SLAM) algorithm. The algorithm introduces the MC criterion instead of the MMSE criterion to effectively improve its robustness in non-Gaussian noise. Combined with the SRUKF-SLAM algorithm, it constructs a new cost function, and uses Cholesky decomposition and orthogonal triangular decomposition to better ensure the positive definiteness of the covariance matrix, so as to ensure the stability of the filtering value.
## 4 Experimental Verification  
### Experimental Set-Up and Data Acquisition  
The experimental platform is built on the SQI-MFS comprehensive simulation platform for mechanical failure, on which the experiments are conducted as shown in Figure 5. The single-piston reciprocating compressor is driven by a motor with a rotation frequency of 15 Hz, which is driven through one shaft and one single-slot V-belt, with a transmission ratio of 3.17. The sampling frequency is set as 5120 Hz [31]. The maximum power of the compressor is 367 W, the diameter of the inner hole is 50.8 mm, the diameter of the stroke is 38.1 mm, and the maximum working pressure is 0.86 MPa. When the pressure is 0.8 MPa, the volume flow rate is 0.07 m\({}^{3}\)/s. Three different compressor states are set: normal valve, worn inlet valve, and failure of exhaust valve spring, as shown in Figure 6. The surface of the valve plate is smooth under the normal state of the valve, and the movement of the exhaust valve plate is not hindered concurrently; therefore, the spring could be telescopic normally. The wear state of the intake valve is artificially processed on the surface of the intake valve with a length of 14 mm and width of 2 mm wear. The spring failure is achieved by adding a rigid part to the exhaust valve limiter to reduce the exhaust valve up-stroke. The model of the signal acquisition box is Utech640U, and a DH1A110E acceleration sensor is adopted, whose sensitivity is 49.86 mV/g. The acceleration sensor is located on the upper end of the reciprocating compressor piston.  
In this experiment, 20 sets of samples are collected for each working condition. The sampling time of each group is 1 s. A total of 60 experiments are set up. Figure 7 presents samples of vibration signals corresponding to different types of reciprocating compressor faults. It can be seen from the time-domain signal that the time-domain amplitude of the intake valve under the normal working state is larger than that under the fault state. The impact on the seat is reduced due to the valve plate being less affected by the airflow than the normal state under the conditions of intake valve wear; in the state of spring failure, the extent of the exhaust valve opening up is limited which results in the impact on the limiter  
\begin{table}
\begin{tabular}{c c} \hline
**Methods** & **CPU Time (s)** \\ \hline ASO-RSSD & 4.4023 \\ RSSD & 4.5628 \\ VMD & 5.9989 \\ \hline \end{tabular}
\end{table}
Table 2: The computational time of each method.  
Figure 5: Fault diagnosis test bench for reciprocating compressor.  
also being reduced. However, the difference between these two faults is not as obvious as required. Consequently, it is necessary for the signals to be further processed.
High Refractive Index Electromagnetic Devices in Printed Technology Based on Glide-Symmetric Periodic Structures  
[PERSON]  
1 Division for Electromagnetic Engineering, KTH Royal Institute of Technology, 114-28 Stockholm, Sweden; [EMAIL_ADDRESS] (O.B.P.); [EMAIL_ADDRESS] (O.Z.); [EMAIL_ADDRESS] (F.G.); [EMAIL_ADDRESS] (O.Q.-T.)  
[PERSON]  
1 Division for Electromagnetic Engineering, KTH Royal Institute of Technology, 114-28 Stockholm, Sweden; [EMAIL_ADDRESS] (O.B.P.); [EMAIL_ADDRESS] (O.Z.); [EMAIL_ADDRESS] (F.G.); [EMAIL_ADDRESS] (O.Q.-T.)  
12 March 202021 April 2  
measurements of electromagnetic devices [42]. The attractive properties found in higher-symmetric structures have inspired several works on numerical methods for the analysis [16; 43; 44; 45; 46; 47; 48; 49; 50]. These methods also give valuable physical insight.  
Due to the forthcoming increase of the operation frequency to millimeter waves in communication systems [51], the requirements on transmitters and receivers are increasing. For instance, highly directive antennas are required to compensate for the increase in the free space path loss [52]. Graded index lenses are a viable option to achieve highly directive antennas at millimeter wave frequencies [52; 53; 54; 55; 56; 57; 58; 59]. One example of a graded index lens is the Maxwell fish-eye (MFE) lens. The MFE lens is a rotationally symmetric lens that converges electromagnetic waves passing through a point in space to its antipodal point [60], and it is an attractive solution for imaging systems. Due to the rotational symmetry, the focusing property of the MFE lens is maintained for all angles and there is an ongoing debate among scientists whether or not the MFE lens has the ability of perfect imaging [61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71]. Furthermore, the half MFE (HMFE) lens has received significant attention [53; 54; 55; 56; 57; 58] due to its ability to transform a spherical wave into a planar wave, similar to a Luneburg lens [26; 59; 72]. Contrary to the Luneburg lens, reflection occurs at output of the HMFE lens. Additionally, since the rotational symmetry is broken in the HMFE lens, the scanning capabilities are limited. However, the HMFE lens is half the size of the Luneburg lens and is thus useful for size-constrained applications where only a limited scanning is required.  
In this work, we study the effect of introducing glide symmetry into a 2D periodic structure. Specifically, we analyze a parallel plate waveguiding structure where one or both of the conductors are loaded with slots. It is demonstrated that a less dispersive and higher effective refractive index is obtained in a glide-symmetric structure, compared to its non-glide-symmetric counterparts. The large achievable range of effective refractive indices enables a variety of microwave devices to be realized. Here, we design a planar MFE lens in printed circuit board (PCB) technology using the analyzed glide-symmetric structure. In fact, without glide symmetry, the performance of the designed lens is severely limited. A prototype of the lens is constructed in order to corroborate the simulations.
# GC4 MRec: Generative-Contrastive for Multimodal Recommendation
## 4 Experiment
### Experimental Settings  
#### 4.1.1 Datasets  
To evaluate our proposed GC4 MRec in the top-N item recommendation task, we conduct extensive experiments on three categories of the widely used Amazon dataset: Sports, Baby, and Clothing. These datasets offer items from distinct categories and vary in the number of users, items, and interactions. The statistics of these datasets are presented in Table 1. Each dataset comprises both textual and visual modalities. Textual features are extracted as 384-dimensional embeddings using pre-trained sentence transformers. For the visual modality, we utilize publicly available 4,096-dimensional visual features.
## 4 Experiment and Analysis
### Performance Testing and Analysis  
#### 4.3.1 Performance Testing  
The experimental results of detection performance are shown in Tables 1 and 2, and the model loss curve is shown in Figure 5.  
Figure 4: Label distribution in the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** \\ \hline
**COPD** & 0.7799 & 0.7057 & 0.7403 \\
**HBOS** & 0.7968 & 0.7630 & 0.7792 \\
**IForest** & 0.8108 & 0.7043 & 0.7491 \\
**VAE** & 0.7988 & 0.6912 & 0.7378 \\
**ECOD** & 0.8219 & 0.6757 & 0.7323 \\
**LOF** & 0.8002 & 0.5627 & 0.6456 \\
**TSMASAM** & **0.9972** & **0.9996** & **9984** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative experiment between TSMASAM and the machine learning-based anomaly detection algorithm.  
Figure 5: The loss curve of the TSMASAM model.  
The abscissa represents the epoch number (a total of 200 epochs), and the ordinate represents the loss value of the model. It can be seen from the figure that the loss value of TSMASAM shows a small range of fluctuation in the early training period, and then quickly converges and maintains a low oven for a long time.  
The experimental results are shown in Tables 1 and 2. It can be seen from the table that TSMASAM has a precision of 99.72%, a recall of 99.96%, and an F1-score of 99.84%. (1) Comparing machine learning methods (COPOD, HBOS, IForest, VAE, ECOD, and LOF): on the [31] dataset, the machine learning method can achieve the highest accuracy of 82.19%; the machine learning algorithm can achieve the highest recall rate of 76.30%; the machine learning algorithm can achieve the highest F1 score of 77.92%. (2) Comparing the ensemble learning methods (XGBOD, LSCP, SUOD, and LODA): on the [31] dataset, the highest performance can reach 99.98%. The detection performance of the method proposed in this paper is better than most of the comparison algorithms but weaker than the XGBOD method.  
The experimental results of detection performance are analyzed as follows: the traditional machine learning algorithm has limited ability and poor generalization ability, which makes the model's learning of traffic characteristics too limited. The learning of datasets with long-term associations is not sufficient, so the performance is lower.  
Due to the large randomness of traffic, it is difficult to learn suitable feature information. The detection method based on ensemble learning ensures the diversity of weak classifiers and fully considers each base model in decision making so that the results obtained are better than traditional ones. The machine learning method works well.  
The method proposed in this paper learns the long-term dependencies between data samples through the self-attention mechanism and convolutional network and transfers them to the ensemble learning model in the form of sample embedding so that the ensemble learning model can more accurately model the process. Therefore, the detection mechanism proposed in this paper is more stable, and the detection effect is not weaker than other integrated learning methods.
# Morphometric Analysis and Three-Dimensional Computed Tomography Reconstruction of Thai Distal Femur
## 2 Materials and Methods
### Measurement of Morphometric Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Measurement** & **Definition** \\ \hline \multirow{2}{*}{TEA} & \multirow{2}{*}{Transepicondyl axis length} & A distance between the most prominent point of lateral and medial epicondyle (LE-ME) \\ \cline{2-3}  & & \\ \multirow{2}{*}{ML} & \multirow{2}{*}{Medolateral length} & A distance between the most prominent point of lateral and medial epicondyle (LLC–MLC) \\ \hline \multirow{2}{*}{AP} & \multirow{2}{*}{Anteroposterior width} & A distance between the most anterior cortex point (AC) and a line connecting LPC and MPC point \\ \hline \multirow{2}{*}{MAP} & \multirow{2}{*}{Medial anteroposterior width} & A distance between the most anterior and posterior point of medial condele (MAC–MPC) \\ \hline \multirow{2}{*}{LAP} & \multirow{2}{*}{Lateral anteroposterior width} & A distance between the most anterior and posterior point of lateral condele (LAC–LPC) \\ \hline \multirow{2}{*}{MCW} & \multirow{2}{*}{Medial condele width} & A width from most medial point to lateral point of medial condele (MMC–MLC) \\ \hline \multirow{2}{*}{LCW} & \multirow{2}{*}{Lateral condele width} & A width from most medial point to lateral point of lateral condele (LMC–LLC) \\ \hline \multirow{2}{*}{WIN} & \multirow{2}{*}{Intercondylar notch width} & A width of the intercondylar notch (LMC–MMC) \\ \hline \multirow{2}{*}{DIN} & \multirow{2}{*}{Intercondylar notch depth} & A distance from the line connecting LPC–MPC \\ \cline{2-3}  & & point to the apex of the intercondylar notch \\ \hline \multirow{2}{*}{MPC} & \multirow{2}{*}{Medial posterior condele height} & A distance between the most superior and inferior point of medial condele (HMC1–HMC2) \\ \hline \multirow{2}{*}{LPC} & \multirow{2}{*}{Lateral posterior condele height} & A distance between the most superior and inferior point of lateral condele (HLC1–HLC2) \\ \hline \multirow{2}{*}{ML/AP} & \multirow{2}{*}{Femoral aspect ratio} & A femoral aspect ratio on the middle condele \\ \hline \multirow{2}{*}{ML/LAP} & \multirow{2}{*}{Lateral femoral aspect ratio} & A femoral aspect ratio on the lateral condele \\ \hline \multirow{2}{*}{ML/MAP} & \multirow{2}{*}{Medial femoral aspect ratio} & A femoral aspect ratio on the medial condele \\ \end{tabular}
\end{table} 
Table 1: Abbreviation and definition of morphometric parameters of the distal femur. Figure 3: The anatomical landmarks on the distal femur: (**a**) in inferior view and (**b**) in posterior view.
# Low-Complexity Convolutional Neural Network for Channel Estimation
## 3 Traditional Channel Estimation Methods  
The most common channel estimation techniques are LS and MMSE. Both of these methods apply mathematical expressions to compute a closed form equation of the channel matrix using different cost functions [2]. The aim of the LS estimator is to minimize the square distance between the received and the reference signal, and uses Equation (3).  
\[J_{LS}\big{(}\hat{\mathbf{H}}\big{)}=\|Y-X\hat{\mathbf{H}}\|^{2} \tag{3}\]  
Figure 1: Example of DM-RS location in the resource grid.  
It leads to the multiplication of the pseudo-inverse of the transmitted signal, \(X\in\ \mathbb{C}^{M\times 1}\), by the received signal, \(Y\in\ \mathbb{C}^{K\times 1}\), as in Equation (4).  
\[\hat{\mathbf{H}}_{\mathbf{L}\mathbf{S}}=(X^{H}X)^{-1}X^{H}Y \tag{4}\]  
where the term \(X^{H}\) is the transposed transmitted signal. The outcome is the estimated channel for each subcarrier, \(\hat{\mathbf{H}}_{\mathbf{L}\mathbf{S}}\ \in\ \mathbb{C}^{K\times M}\).  
Once the channel for specific elements in the resource grid is calculated, the information is interpolated in both the frequency and time domains to gain the whole channel knowledge. This technique is the most convenient in terms of complexity, and for this reason, it is still widely used. However, the accuracy of this method is strongly related to the signal-to-noise ratio (SNR) at the receiver [2; 21]. In order to reduce the error and consider the degradation due to noise and spatial correlation, the MMSE includes the statistical information in the channel estimation.  
In particular, MMSE aims is to minimize the error between the channel and the LS estimation, expressed by Equation (5).  
\[J_{MMSE}\big{(}\hat{\mathbf{H}}\big{)}=E\Big{\{}\|\mathbf{H}-\hat{\mathbf{H}}_{\mathbf{L}\mathbf{S} }\|^{2}\Big{\}} \tag{5}\]  
This leads to the closed form Equation (6) for channel estimation.  
\[\hat{\mathbf{H}}_{\mathbf{M}\mathbf{S}\mathbf{S}}=\mathbf{R}_{\mathbf{H}\mathbf{H}}\bigg{(}\mathbf{R}_{\mathbf{H} \mathbf{H}}+\frac{\sigma_{n}^{2}}{\sigma_{x}^{2}}\ \mathbf{I}\bigg{)}^{-1}\hat{\mathbf{H}}_{\mathbf{L}\mathbf{S}} \tag{6}\]  
where \(\mathbf{R}_{\mathbf{H}\mathbf{H}}\in\ \mathbb{C}^{K\times K}\) is the cross-correlation matrix between the exact channel and the temporary channel estimated in the frequency domain, while the \(\mathbf{R}_{\mathbf{H}\mathbf{H}}\) is the autocorrelation of the exact channel. The terms \(\sigma_{n}^{2}\) and \(\sigma_{x}^{2}\) take into account the variance of the noise and the transmitted signal, respectively. The achievable performance is much higher than the LS technique. However, the second-order statistical parameters are highly demanding in terms of computational cost, and they are hardly accurate in a real system with a fast-varying environment [2; 21; 22].
## 3 Materials and Methods
### Standardized Gravity Model  
Although the gravity model, characterized by a power-law function, is extensively utilized in spatial accessibility analysis, it presents a notable issue: as the distance \(d\) approaches zero, the function \(f(d)\) tends to infinity, as illustrated in Figure 1b. This can result in significant discrepancies in the magnitude of \(f(d)\) at varying distances, leading to an overall abnormal distribution of accessibility values. To mitigate this problem, this paper introduces a standardized gravity model:  
\[f(d)=\left\{\begin{array}{c}\left(\frac{d}{d_{0}}+1\right)^{-\beta},d\leq d _{0}\\ 0,d>d_{0}\end{array}\right., \tag{8}\]  
where \(d_{0}\) denotes the search radius. Unlike the original gravity model, the standardized version is independent of the unit of \(d\), making it more appropriate for use as a decay function. Through standardization, the range of the distance decay function is confined to \(\left(2^{-\beta},1\right)\), establishing the upper and lower limits of the distance decay effect, as shown in Figure 1c. The standardized gravity model prevents extreme variations while preserving the ability to adjust the strength of the distance decay function via \(\beta\), thereby making it more suitable for the 2 SFCA method's application scenarios.  
The term \(\big{[}f(d_{ij})\big{]}^{2}\) in Equation (6) represents an enhancement of the supply-demand relationship using the standardized gravity model. [PERSON] [27] noted that, in both traditional and distance-decay-adjusted 2 SFCA methods, the sum of the product of accessibility and demand scale for each demand point equals the total facility scale of all supply points. This suggests that in this model, the resources provided by all facilities perfectly align with the demand at the demand points, which is an idealized scenario that deviates from reality. Therefore, introducing a certain loss to the accessibility of demand points, making the supply scale less than the demand scale, aligns better with real-world conditions.  
The MM-G2 SFCA method is compatible with the MM-2 SFCA method. When the search radius \(f\big{(}d_{ij}\big{)}\) is fixed at 1, \(M_{k}\) represents the number of residents at point \(k\), and \(M_{k,n}\) represents the number of residents traveling by the \(n\)-th mode of transportation at point \(k\), Equations (5) and (6) are equivalent to Equations (2) and (3).  
With advancements in information technology, spatiotemporal big data related to residents' travel, such as OD data, travel data, and passenger volume data, as shown in Table 1, are continuously being enriched. These data can reflect residents' choices of travel modes to varying degrees and hold potential for application in accessibility analysis, providing data support for the MM-G2 SFCA method beyond the residents' transportation population structure data [20].
# Reducing PAPR with Low Complexity Filtered NOMA Using Novel Algorithm
## 3 Simulation Results
### Power Spectral Density  
The PSD of the NOMA and F-NOMA with SLM is estimated and analyzed in this section. From Figure 6, it is seen that the spectrum emission of NOMA is \(-50\) dB and F-NOMA is \(-71\) dB. This indicates that the F-NOMA efficiently reduces the OOBE as equated with the NOMA. The OOBE of the NOMA and F-NOMA with SLM is estimated and given as \(-63\) dB and \(-80\) dB, respectively; therefore, it is determined that the spectral efficiency of the F-NOMA is efficiently enhanced and outperforms the conventional NOMA.