esa-sceva/satcom-chunk-collection huggingface dataset:
# OTFS-IM Modulation Based on Four-Dimensional Spherical Code in Air-to-Ground Communication
## 2 System Model
In order to obtain the received signal \(\mathbf{y}\) in the DD domain, the receiver performs in reverse compared with the transmitter, including in its shaping pulse, Wigner transform, Symplectic Fast Fourier Transform (SSFT), and column-wise operation:  
\[\mathbf{Y}_{\text{DD}}=\mathbf{F}_{M}^{H}(\mathbf{F}_{M}\mathbf{G}_{rx} \mathbf{R})\mathbf{F}_{N}=\mathbf{G}_{rx}\mathbf{R}\mathbf{F}_{N} \tag{8}\]  
\[\mathbf{y}=vec(\mathbf{Y}_{\text{DD}})=(\mathbf{F}_{N}\otimes\mathbf{G}_{rx} )\mathbf{r} \tag{9}\]  
where \(\mathbf{Y}_{\text{DD}},\mathbf{G}_{rx}\) represent the matrix form of the received signal in DD domain and the received shaping pulse matrix, respectively. Combined with Equations (5), (6) and (9), the input-output relationship in the DD domain can be expressed as:  
\[ \begin{array}{ll}\mathbf{y}&=(\mathbf{F}_{N}\otimes\mathbf{G}_{rx})\mathbf{ H}(\mathbf{F}_{N}^{H}\otimes\mathbf{G}_{tx})\mathbf{x}+(\mathbf{F}_{N} \otimes\mathbf{G}_{rx})\mathbf{n}\\ &=\mathbf{H}_{eff}\mathbf{x}+\mathbf{\tilde{n}}\end{array}  \tag{10}\]  
where \(\mathbf{H}_{eff}=\sum_{i=1}^{p}h_{i}[(\mathbf{F}_{N}\otimes\mathbf{G}_{rx}) \mathbf{\Pi}^{i}\mathbf{\Delta}^{(k_{i})}(\mathbf{F}_{N}^{H}\otimes\mathbf{G} _{tx})]\). It is assumed that the channel state information is well known at the receiver. After the MMSE equalization for \(\mathbf{Y}_{\text{DD}}\), the estimated signal \(\mathbf{\hat{X}}_{\text{DD}}\) in the DD domain is obtained. The MMSE equalization used in this paper is a basic MMSE approach that can be expressed as follows [9]:  
\[\mathbf{\hat{x}}_{\text{DD}}=\mathbf{H}_{eff}^{H}(\mathbf{H}_{eff}\mathbf{H}_ {eff}^{H}+\frac{1}{N_{0,\text{DD}}}\mathbf{I})^{-1}\mathbf{y}_{\text{DD}} \tag{11}\]  
where \(\mathbf{\hat{x}}_{\text{DD}}\) is the column-wise vector of \(\mathbf{\hat{X}}_{\text{DD}}\), \(\mathbf{y}_{\text{DD}}\) is the column-wise vector of \(\mathbf{Y}_{\text{DD}}\), and \(N_{0,\text{DD}}=N_{0}\) represents the variance in noise in the DD domain. The noise vectors in the time domain and the DD domain have the same statistical properties since they are related by unitary transformations. Then, the ML detector is used to demodulate the signals, sub-block by sub-block. The index information and the modulated symbols information in the \(\beta\)-th sub-block are detected as:  
\[[\mathbf{I}_{\beta},\mathbf{s}_{\beta}]=\underset{\mathbf{I}_{\beta},\mathbf{s} _{\beta}}{\arg\min}\left|\hat{\mathbf{x}}_{\beta}-\mathbf{X}_{\beta}\right|^{2} \tag{12}\]  
where \(\mathbf{X}_{\beta}\) is the potential cases of sub-blocks. For each sub-block, there are \((M_{c})^{k/2}\) possible options for the modulated symbols, and there are \(n/k\) different situations for index selection. Therefore, the number of potential cases for a sub-block is \(O((M_{c})^{k/2}n/k)\), which is also the complexity of detecting a sub-block using the ML detection algorithm.
# Influence of Spectral Filtration on Pulse Dynamics in Ring-Cavity Mamyshev Oscillator
## 5 Conclusions  
To conclude, we performed a detailed investigation of influence of spectral filtration on the output field dynamics of the ring-cavity MO. Using Floquet stability analysis, we examined self-starting possibilities of the MO and predicted repetition rates of the pulse trains generated due to Faraday instability. The results of the full NLSE-based numerical simulations are in good qualitative agreement with the theoretical predictions. We demonstrated that a gradual increase of the spectral filters separation with varying steps leads to rich transient dynamics between multi- and single-pulse generation regimes, including hysteresis phenomena and formation of soliton molecules. In further research, pump power should also be considered as a tunable parameter, leading to even more complicated dynamics. For this reason, it is important to develop intelligent methods for controlled tuning of the MO parameters to find the mode-locked regimes with required characteristics.  
Conceptualization, A.B. and A.K.; methodology, A.B.; software, A.B., E.K. and I.G.; validation, A.B., E.K. and A.K.; investigation, I.G., E.K. and A.B.; writing--original draft preparation, A.B. and A.K.; visualization, A.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Higher Education of the Russian Federation (agreement No. 075-15-2021-1009).  
Not applicable.  
The authors declare no conflict of interest.
# Application of the Random Decrement Technique in Damage Detection under Moving Load
## 3 Laboratory Models of Simply Supported Beam and Arch Cable Bridge
### Experimental Study of the Arch Cable Bridge  
A scaled arch bridge model was designed and assembled in the department of civil engineering, Zhejiang University, China [31]. The model is a half-through steel-tube arch bridge with a main span of 6 m and 14 cables in total. This arch cable bridge is shown in Figure 7. The material properties of the bridge, including [PERSON]'s modulus, density, and Poisson's ratio are assumed as 2.06 \(\times\) 10\({}^{6}\) N/mm\({}^{2}\), 7.698 \(\times\) 10\({}^{-5}\) N/mm\({}^{3}\), and 0.3, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **1** & **2** \\ \hline Crack depth to beam flange ratio & 40\% & 30\% \\ Location & at node 5 (mid-span) & at node 7 \\ Name & N5D40 & N7D30 \\ \hline \hline \end{tabular} Note: The scenarios are designated as N (damage location) D (delta). For example, N5D40 refers to a scenario where delta = 40\% at node 5.  
\end{table}
Table 2: Two damage scenarios considered in the laboratory model of the simply supported beam.  
Figure 5: A general view of the vehicle and accelerometers.  
Figure 6: A general view of the crack. The right photo shows the width of the crack (around 0.003 m), and the left photo shows the height of the crack for delta = 30% (0.072 \(\times\) 0.3 = 0.0216 m \(\approx\) 0.021).  
Figure 8 shows the location of accelerometers attached close to the cables on one side of the bridge. Each cable is composed of 7 twisted sub-cables. Figure 9 shows the twisted cable. The damage is applied to the model by cutting the small twisted cables. It enables us to simulate 14% to 100% damage in each cable. Cutting one of the twisted cables corresponds to 14% damage (\(\frac{1}{7}\cong 0.14\%\)), while removing the whole twisted cables equals 100% damage.  
Figure 8: Side view of the model showing (**a**) locations of accelerometers installed on one side of the bridge and (**b**) accelerometers attached close to the cables.  
Figure 7: The arch cable bridge lab model built at Zhejiang University Laboratory.  
Five different damage scenarios were considered in the laboratory model of arch cable bridge, as listed in Table 3.
## 4 Discussion
It is worth mentioning that GP_GAIN trains two classifiers when the fitness function is evaluated: an LR model with original features at the trees' input and another LR model with created features at the trees' output. Hence, a limitation is that the computation time can be higher than GP_AUC and GP_CORR, mainly when the number of depth levels increases: the higher the depth levels, the more numerous the terminal nodes (i.e., original features to train an LR model). Nevertheless, solutions using a few shallow trees obtained the best classification performance. Indeed, the best model used two trees with a maximum of three depth levels, shown in Figure 4, suggesting that shallow trees tend to construct more generalizable features. Moreover, these two constructed features combined indices from all the radiomic classes (i.e., shape, histogram-based, and texture features), thus highlighting the relevance of different sources in the final classification. This observation aligns with the most recent works that proposed radiomic signatures that enclose different radiomic features for the survival prediction of NSCLC patients using traditional ML methods [53; 54]. Another limitation of our work is that the two datasets (LUNG1 and LUNG2) used for training and testing present intrinsic differences in patients' characteristics, thus limiting the effectiveness of the comparison and the generalizability of our approach. Nonetheless, the availability of these two datasets, which have been widely adopted in the literature to train and test different approaches, makes it possible to compare directly with previous works. The proposed approach has several potential benefits for clinical practice. It offers improved predictive performance for two-year survival, better generalizability across different datasets, and greater robustness compared to traditional radiomics. Additionally, to make it easier for clinicians to understand and use, GP-based features can be utilized to create nomograms, which oncologists commonly use to make decisions. This approach has been widely employed in radiomics to predict survival in NSCLC [14]. Figure 5 shows a nomogram created using the best GP-based features and clinical and demographic information (such as age and T and N stages). Survival probability was estimated from the LR classifier. This approach enables NSCLC patients to receive information about clinical decision-making and personalized treatment options using traditional tools like nomograms enhanced by informative, independent, and reliable GP features. Figure 5: A nomogram for predicting two-year survival using the GP features from the internal validation and clinical and demographic information.
## 2 Methodology
### Direct Model
#### 2.2.1 Instrument Model  
There are two detection modes for lidar systems: photon-counting and analog detections [44]. The backscattered lidar signal \(S\) is expressed in volt or in number of photoelectrons in analog or photon-counting detections, respectively. The corresponding instrumental constant \(K(\lambda)\), which includes all instrumental parameters, is expressed as:  
\[K\ (\lambda)=\left\{\begin{array}{c}\frac{\lambda}{\text{h}c}\cdot QE \cdot OE\cdot A\cdot\Delta z\ \text{photon}-\text{counting detection}\\ OE\cdot A\cdot G\cdot R_{c}\cdot\frac{c}{2}\ \text{analog detection}\end{array}\right. \tag{3}\]  
where \(QE\) and \(OE\) are the quantum efficiency of the photo-detector and the total optical efficiency of the lidar system, \(A\) is the surface of the receptor (e.g., telescope), \(G\) is the system gain of both the pre-amplification and the detector, and \(R_{c}\) is the load resistance. The Planck constant and the light velocity are h (\(\sim\)6.62 \(\times\) 10\({}^{-34}\) J\(\cdot\)s) and \(c\) (\(\sim\)3 \(\times\) 10\({}^{8}\) m\(\cdot\)s\({}^{-1}\)) respectively.  
In our model, both the UV and NIR wavelengths were considered (_i.e._, 355 and 1064 nm). The corresponding instrumental parameters were chosen according to the existing spaceborne lidar systems. The most commonly used photo-detectors in UV and NIR are the photomultiplier tube (PMT) and the avalanche photodiode (APD), respectively. State of the art PMTs/APDs were considered for our simulations with their quantum efficiency (\(QE\)) and gain (\(G\)) shown in Table 1. The optical components (optical efficiency (\(OE\)), Table 1) used for these two wavelengths domains have similar properties. Other instrumental parameters (_i.e._, \(A\), \(R_{c}\), \(\Delta z\)) were considered as the same for both wavelengths in our simulations (Table 1).
## 9 Discussion
### Privacy Preservation Techniques in Vehicular Communications
As part of their functioning, autonomous vehicles need to communicate with various external entities, such as other vehicles (V2V communication), infrastructure (V2I communication), and broader networks (V2N communication). This communication process often involves the transmission of sensitive data, including vehicle location, speed, navigation details, and even potentially sensitive user information. Therefore, ensuring privacy becomes crucial. Each method described in this subsection--encryption, secure communication protocols, anonymization, blockchain technology, differential privacy, and homomorphic encryption--serves to protect the privacy of data communicated between autonomous vehicles and third-party entities. Ultimately, while these techniques are not exclusive to autonomous vehicles and can be applied to various domains where secure data transmission is required, they are highly relevant and necessary in the context of autonomous vehicular communications.
## 3 Experimental Results and Discussion
### Hysteretic Behavior and Maximum Shear Resistance
This is further confirmed by the reinforcement strain measurements shown in Figure 9. In the presence of external compressive stress, \(\sigma_{N,ext}\), the tensile strains in the reinforcing bars are reduced, compared to those measured on reinforcing bars crossing an interface under zero normal stress, implying that the contribution of the interface reinforcement to the overall resistance is reduced. It should be noted that the large scatter of the strain values for interfaces under \(\sigma_{N,ext}=0\) is attributed to the fact that the respective points in Figure 9 correspond to specimens with various reinforcing ratios and embedment lengths. Figure 8: Maximum interface resistance, plotted against the embedment length. (**a**) Specimens without external compressive stress. (**b**) Specimens with external compressive stress, \(\sigma_{N,ext}\). Empty symbols are used for specimens with cast-in bars, with full symbols for specimens with post-installed bars. Figure 7: The ratio V/s for the first cycle, corresponding to the shear stiffness of the specimens (**a**) without external compressive stress and (**b**) with external compressive stress. Figure 10 shows the energy absorbed for all specimens during the first loading cycle at various imposed shear slip steps, estimated by the area of the respective hysteresis loops. Due to the fact that, for the same imposed shear slip at the interface, the specimens without external compression exhibit smaller shear resistance, the area of the hysteresis loops is smaller than for specimens subjected to external compression. This difference is depicted in the two graphs of Figure 10.
# Elemental Abundances of Moon Samples Based on Statistical Distributions of Analytical Data
## 3 Statistical Methods and Results  
### Elemental Abundances in Moon Samples  
In order to calculate the elemental abundances in moon samples, a total of 2365 records of analytical data were used with equal weight, ignoring the different missions or landing sites, sample types, and analytical methods from the literature.  
According to the rule of \"The contents of major oxides commonly obey normal distribution\", the average of the analytical data excluding outliers was calculated as the abundance for each major oxide here. Firstly, the average (Avg) and standard deviation (Std) of each oxide's data were calculated for all the samples (the count of samples is labeled as n\({}_{0}\)). Then, the boundary values of Avg \(\pm\) 3 Std were used to delete the outliers repeatedly until no outlier data were found. Finally, the average of each oxide's data without outliers (the count of samples is labeled as n) was calculated and viewed as its abundance. The abundances of 11 major oxides (including Cr\({}_{2}\)O\({}_{3}\)) along with counts of samples (n\({}_{0}\) and n) are listed in Table 3, and statistical histograms of major oxides are illustrated in Figure 2. The sum of the 11 major oxides (or Total in Table 2) is 99.10%, which is close to the closure value of 100%.  
Figure 2: Histograms of major oxides with their counts of samples (n\({}_{0}\) is the count of total analytical data and n is the count of analytical data without outliers). The histogram of K\({}_{2}\)O contents is drawn without the three highest values of 5.8, 3.2, and 3.1, and the histogram of P\({}_{2}\)O\({}_{5}\) contents is without the two highest values of 1.38 and 1.1.  
According to the rule of \"The contents of trace elements commonly obey log-normal distributions\", the geometric average of analytical data excluding outliers was calculated as the abundance for each trace element. The elemental abundances of 50 trace elements (including Ti, P, Mn, and Cr), along with their counts of samples (n\({}_{0}\) and n), are also listed in Table 3.  
REE pattern is a useful tool for traceability or provenance in geochemistry [44; 45], and the key signature is the variation trend of the pattern, which is dependent on REE concentrations. Therefore, the covariation of REE abundances needs to be considered. Here, the additive log-ratio (alr) transformation method [46; 47] was adopted to calculate the REE (including Y) abundances, and Yb was selected as the denominator to calculate the ratios for other REEs. Yb was selected as the denominator of the alr transformation method because it not only has the largest count of analyzed samples (n\({}_{0}\) = 1680 and n = 1652) but also obeys the log-normal distribution well relative to the other REEs. According to the geometric average of Yb without outliers, 5.84 \(\upmu\)g/g was set as its abundance of moon samples and was used to calculate the abundances of the other REEs (including Y). The abundances of 15 REEs (including Y) with their counts of samples (n\({}_{0}\) and n) are also listed in Table 3.
## 4 Study Results and Discussion
### Parametric Study
#### 4.3.1 Effect of Pitching Angle of Aerodynamics
The results show that the lift force in the cases of 0\({}^{\circ}\)-75\({}^{\circ}\) and 0\({}^{\circ}\)-90\({}^{\circ}\) are very close, but the lift in the 0\({}^{\circ}\)-45\({}^{\circ}\) case is significantly smaller (larger negative) during the first half up-stroke period \(\hat{t}\) = 0.25-0.5 and continues until \(\hat{t}\) = 0.63. In the down-stroke period \(\hat{t}\) = 0.75-1 however, the smaller pitching angles 0\({}^{\circ}\)-45\({}^{\circ}\) result in a significantly greater positive lift force compared to the other two large pitching angle cases. It can be inferred that a much greater lift force can be generated if a quick increase of pitching angle from 45\({}^{\circ}\) to 75\({}^{\circ}\) can be made in the up-stroke period \(\hat{t}\) = 0.25-0.63 and a quick transition back from 75\({}^{\circ}\) to 45\({}^{\circ}\)-0\({}^{\circ}\) in the down-stroke period \(\hat{t}\) = 0.75 \(\sim\) 1. The results indicate that an optimal kinematics of motion can produce a much greater lift force although additional driving power may be required. The resulting drag forces in a flapping cycle for the three pitching angle cases 0\({}^{\circ}\)-45\({}^{\circ}\), 0\({}^{\circ}\)-75\({}^{\circ}\) and 0\({}^{\circ}\)-90\({}^{\circ}\) is shown in Figure 19 with a similar feature to the lift variation. A large difference occurs between the drag forces in case 0\({}^{\circ}\)-45\({}^{\circ}\) and large pitching angle cases 0\({}^{\circ}\)-75\({}^{\circ}\) and 0\({}^{\circ}\)-90\({}^{\circ}\) in the up-stroke period \(\hat{t}\) = 0.25-0.6. In the first half of the up-stroke, negative drag forces (thrust) are produced in the case of a small pitching angle of 0\({}^{\circ}\)-45\({}^{\circ}\), and achieves a peak value \(-\)1.4 N when the pitching angle reaches maximum 45\({}^{\circ}\) at the midpoint of up-stroke (\(\hat{t}\) = 0.5). While a small positive drag force occurs in this period and reaches a peak value 0.22 N and 0.76 N at large pitching angles 0\({}^{\circ}\)-75\({}^{\circ}\) and 0\({}^{\circ}\)-90\({}^{\circ}\), respectively. The drag forces keep small positive for the three cases in the down-stroke period \(\hat{t}\) = 0.75-1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Pitching Angle Range** & **0\({}^{\circ}\)-15\({}^{\circ}\)** & **0\({}^{\circ}\)-30\({}^{\circ}\)** & **0\({}^{\circ}\)-45\({}^{\circ}\)** & **0\({}^{\circ}\)-50\({}^{\circ}\)** & **0\({}^{\circ}\)-60\({}^{\circ}\)** & **0\({}^{\circ}\)-75\({}^{\circ}\)** & **0\({}^{\circ}\)-80\({}^
## 2 Materials and Methods
### Description of the Three-Phase Approach
The method for how to identify these key factors is quantifying the contribution of factors to predictors with the _VIP_ (variable importance in the projection) function of partial least squares regression (PLSR) [33; 34]. PLSR was developed for econometrics by [PERSON] in 1975 [33], which has been used to handle multi-collinearity among independent variables [34]. The _VIPs_ represent the importance of model descriptors, both with respect to correlation to \(Y\) (dependent variable set) and with respect to \(X\) (the projection). Each descriptor is uniquely and independently described by its _VIP_, which represents a measure of the contribution of the term to the decomposition of \(X\), and the correlation with \(Y\). Moreover, the _VIP_s are normalized to enable comparison. Terms with the largest _VIP_ value are the most relevant for explaining the dependent variable. The importance degree of independent variable \(x_{j}\) can be obtained with Equations (3) and (4). \[VIP_{j}=\sqrt{\frac{p}{Rd\big{(}\hat{Y};t_{1},t_{2},...,t_{m}\big{)}}\sum_{h=1 }^{m}Rd\Big{(}\hat{Y}_{h}\Big{)}w_{hj}^{2}} \tag{3}\]  
\[Rd\big{(}\hat{Y};t_{1},t_{2},...,t_{m}\big{)}=\sum_{h=1}^{m}Rd\Big{(}\hat{Y}_{ h}\Big{)}=\sum_{h=1}^{m}r^{2}\Big{(}\hat{Y}_{h}\Big{)} \tag{4}\]  
where \(VIP_{j}\) is the variable importance projection of independent variable \(x_{j}\) on dependent variable \(\hat{y}\); \(Rd\Big{(}\hat{Y};t_{1},t_{2},...,t_{m}\Big{)}\) is the ability to explain \(\hat{Y}\) with \(t_{1},t_{2},...,t_{m}\); \(Rd\Big{(}\hat{Y}_{h}\Big{)}\) is the ability to explain \(\hat{Y}\) with \(t_{h}\); \(r^{2}\Big{(}\hat{Y}_{h}\Big{)}\) is the relative coefficient between \(\hat{Y}\) and \(t_{h}\); \(t_{1},t_{2},...,t_{m}\) are the least partial components of independent variables; and \(t_{h}\) is the \(h^{\text{th}}\) least partial component.
## 4 Experiments
### Detail and Parameters  
We implement our proposed method on Staple and SAMF trackers, and we name them Staple_SCA and SAMF_SCA, respectively. Staple_SCA tracker integrates HoG [49] and color histogram, and the scale scheme is based on DSST like Staple, the more information refer to [18]. Similar to SAMF tracker [16], SAMF_SCA copes to the scale variation with a scaling pool \(\mathbf{S}\), and \(\mathbf{S}=\{0.985,\,0.99,\,0.995,\,1.0,\,1.005,\,1.01,\,1.015\}\). SAMF_SCA employs HoG and Color-naming [33] features, using a cell size of \(4\times 4\) and the number of bin is 9. We also increase the padding windows from 2.5 times of target object to 3, due to the increased robustness from context patches. The number of context patches k is set to 4. The regularization factor \(\lambda_{1}\) is set to e\({}^{-4}\). We set the other regularization factor \(\lambda_{2}\) to \(\{0.5,\,0.4\}\) and the initial learning rate \(\beta\) are \(\{0.015,\,0.005\}\) for Staple_SCA and SAMF_SCA, respectively.  
For our proposed method, the PSR score of filter generally ranges between 16 and 34 under normal tracking conditions. When PSR score drops to around 7, it signifies that the object is heavy-occluded or tracking has failed. Thus, the threshold \(THR\) of PSR is set to 7.  
The proposed method is implemented in MATLAB R2014a version. All the experiments are conducted on an Intel Xeon(R) E3-1226 V3 CPU (3.30 GHz) PC with 32 GB RAM. Staple_SCA and SAMF_SCA trackers run at 24.9 fps and 10.6 fps, which are still within real time range.