esa-sceva/satcom-chunk-collection huggingface dataset:
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 5 Experiments
### Evaluation Metrics  
Four metrics are used to evaluate the performance of the proposed and comparison methods in the experiment [1; 16; 24; 43; 50].  
1. Root Mean Square Error (\(RMSE\)) \[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}||Y_{i}-\hat{Y}_{i}||_{2}^{2}}\] (16)
2. Mean Absolute Error (\(MAE\)) \[MAE=\frac{1}{N}\sum_{i=1}^{N}|Y_{i}-\hat{Y}_{i}|\] (17)
3. Mean Absolute Perceptage Error (\(MAPE\)) \[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{|Y_{i}-\hat{Y}_{i}|}{|Y_{i}|}\times 100\%\] (18)
4. Coefficient of Determination (\(R^{2}\)) \[R^{2}=1-\frac{\sum_{i=1}^{N}||Y_{i}-\hat{Y}_{i}||_{2}^{2}}{\sum_{i=1}^{N}||Y_ {i}-\hat{Y}||_{2}^{2}}\] (19)  
where \(N\) denotes the number of samples in the evaluation dataset, \(Y_{i}\) means the true hourly OD matrix, \(\hat{Y}_{i}\) is the corresponding estimation matrix, and \(\hat{Y}\) is the average of \(Y_{i}\). The operators \(|\cdot|\), \(||\cdot||_{2}\) denote L1 and L2 norms.  
The RMSE and MAE measure the absolute errors between the OD matrix and its estimation, and MAPE quantifies the relative error. \(R^{2}\) compares the estimation error to the fluctuation from the average of the data, and the closer it is to 1, the better the model performs.  
\begin{table}
\begin{tabular}{l l l} \hline
**Categories** & **Parameters** & **Values** \\ \hline Simulation for & Epoch Number for Realistic Data & 600 epoch \\ Realistic Environment & Simulation Duration for Realistic Data & 17.3 h/epoch \\  & Path Selection Ratio & (0.35, 0.53, 0.12) \\ \hline Simulation for & Epoch Number for Paired Data Generation & 4000 epoch \\ Data Generation & Simulation Duration for Paired Data Generation & 12 h/epoch \\  & Path Selection Ratio & (0.4, 0.5, 0.1) \\  & Number of Prior OD Matrices & 20 \\ \hline Model Parameters & Epoch Number for Training & 100 \\  & Batch Size & 16 \\  & Sequence Length of Attention & 12 \\  & Head Number of Attention & 3 \\  & Dimensions of feature space & 150 \\  & Dimensions of Query, Key, Value Matrices & 50 \\  & Hidden Dimensions for forward Networks & 256 \\  & Hidden Dimensions for backward Networks & 1024 \\  & Optimizer & Adam \\  & Learning Rate & 0.0001 \\  & Decay of Learning Rate & 0.2/10 epochs \\ \hline \end{tabular}
\end{table}
Table 1: Parameter configuration.
## 3 Experiment and Results
### Experiment 2: Cartilage Change Prediction at the Middle Horizontal Line  
A parallel experiment was conducted to detect the changing pattern of cartilage at the middle horizontal line on the medial tibia compartment (see Figure 2). For CDI, the three center locations (#4, #5, and #6) were used to extract thickness information, and for manual segmentation, the X2 slice was used. We performed the same rebalance operations as used in experiment 1. Table 3 listed the distribution of the 'change' and 'no-change' categories for experiment 2.  
As Table 4 showed, the CDI method outperformed manual segmentation and achieved AUC = 0.766 and F-measure = 0.839. Additionally, the oversampling did not help the CDI improve its performance in this experiment while the undersampling improved the AUC of the manual segmentation from 0.494 to 0.524. Overall, the CDI still outperformed manual segmentation significantly on predicting cartilage change in the middle slice of the medial tibia compartment. Figure 4 showed the ROC curves for these models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Quantification Method**} & \multicolumn{2}{c}{**‘Changed’**} & \multicolumn{2}{c}{**‘No Change’**} \\ \hline \multirow{4}{*}{CDI} & \multirow{2}{*}{Original} & Train & 36 & 135 \\ \cline{3-5}  & & Test & 9 & 63 \\ \cline{3-5}  & & \multirow{2}{*}{After rebalance} & Train & 144 & 135 \\ \cline{3-5}  & & Test & 9 & 63 \\ \hline \multirow{4}{*}{Manual Segmentation} & \multirow{2}{*}{Original} & Train & 1682 & 6868 \\ \cline{3-5}  & & Test & 691 & 2909 \\ \cline{1-1} \cline{2-5}  & \multirow{2}{*}{After rebalance} & Train & 1682 & 1682 \\ \cline{1-1} \cline{2-5}  & & Test & 691 & 2909 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample distribution in experiment 2.  
Figure 3: Receiver operating characteristic (ROC) plot of bottom CDI (#1, #2, and #3) and manual segmentation on X1 slice.
## 3 Materials and Methods
### Control System Design
#### 3.3.2 Clutch and Actuator Model  
The torque capacity of the clutch was determined by considering the maximum engine torque and safety factor. The vertical force of the clutch was calculated, using the clutch torque capacity, friction coefficient, number of the plate, and clutch disc's effective radius, by the clutch model in Equations (3) and (4) [41]. Considering the engine's maximum torque of 171 Nm and a safety factor of 2.5, the clutch torque capacity was 427.5 Nm. The clutch disc's effective radius was calculated to be 115.4 mm, considering the outer radius of the clutch plate as 275 mm and the inner radius of the clutch plate as 180 mm. In addition, considering the two friction plates and a friction coefficient of 0.3, the clutch vertical force was calculated to be 6174 N.  
\[F=\frac{T_{c}}{\mu_{c}\cdot n\cdot r_{m}}, \tag{3}\]  
\[r_{m}=\frac{2\left({R_{0}}^{3}-{R_{i}}^{3}\right)}{3\left({R_{0}}^{2}-{R_{i}} ^{2}\right)}, \tag{4}\]  
where \(T_{c}\) is the clutch torque capacity (Nm), \(\mu_{c}\) is the friction coefficient, \(n\) is the number of the plate, \(F\) is the clutch vertical force (N), \(R_{0}\) is the outer radius of the clutch plate (m), \(R_{i}\) is the inner radius of the clutch plate (m), and \(r_{m}\) is the clutch disc's effective radius (m).  
The clutch actuator is connected to the release fork and diaphragm spring by a link, and the engagement and disengagement of the clutch is determined as the displacement of the clutch actuator is controlled. The dynamic model of the actuator is as shown in Equations (5) and (6), and the model for the vertical force acting on the clutch plate, according to the output force of the actuator, is shown in Equations (7)-(9) [41]. The ratio of the diaphragm and release bearing and ratio of the clutch fork lever are 0.3 and 0.5, respectively.  
\[x_{rel}=k_{CF}\cdot x_{act}, \tag{5}\]  
\[x_{dia}=k_{dia}\cdot x_{rel}, \tag{6}\]  
Figure 6: Control procedure of the AMT system in case of neutral to forward.  
where \(k_{dia}\) is the ratio of the diaphragm and release bearing, \(k_{CF}\) is ratio of the clutch fork lever, \(x_{rel}\) is the displacement of the release fork (mm), \(x_{act}\) is the displacement of the clutch actuator (mm), and \(x_{dia}\) is the displacement of the diaphragm spring (mm).  
\[F_{rel}=k_{CF}\cdot F_{act}, \tag{7}\]  
\[F_{dia}=f_{dia}\cdot x_{dia}, \tag{8}\]  
\[F=F_{dia}-F_{rel}\cdot k_{dia}, \tag{9}\]  
where \(F_{act}\) is the force of the clutch actuator (N), \(F_{dia}\) is the force of the diaphragm (N), \(F_{rel}\) is the force of the release fork (N), and \(f_{dia}\) is the coefficient of the diaphragm spring (N/mm).
## 3 Results and Discussion
### Uniaxial Compressive Strength  
Concerning the UCS study, Figure 3 compares REC curves of all four ML algorithms, confirming the lower performances of MR and the superior response of ANN. In a REC representation, a high performance corresponds to an accuracy of one (y-axis) achieved for as low as possible absolute deviation (x-axis). Thus, taken ANN.UCS as a reference, one can observe that ANN.UCS achieved accuracy close to one for an absolute deviation of  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Hyperparameters**} & \multicolumn{2}{c}{**Time (s)**} \\ \cline{2-5}  & **UCS** & **ITS** & **UCS** & **ITS** \\ \hline ANN & \(H=6\pm 1\) & \(H=5\pm 1\) & \(5.18\pm 0.18\) & \(4.79\pm 0.23\) \\ SVM & \(\gamma=0.23\pm 0.05\) & \(\gamma=0.17\pm 0.08\) & \(2.12\pm 0.07\) & \(2.32\pm 0.05\)  
750 kPa. On the opposite side, and for the same absolute deviation, the MR.UCS accuracy is around 25% lower. SVM.UCS and RF.UCS have similar performances, although the first one shows a better response for lower absolute deviations.  
Figure 4 depicts the relation between observed and predicted UCS values (scatterplot) according to ANN.UCS (Figure 4a) and RF.UCS (Figure 4b) models. From its analysis, a very interesting fit can be observed (all points are very close to the diagonal line), which corroborates the metrics values above summarized in Table 3 and discussed.  
As important as the model's accuracy is its interpretability, particularly from an engineering point of view. Accordingly, in this study, a detailed sensitivity analysis was applied, aiming to measure the relative importance of each model attribute and, this way, understand what has been learnt by the algorithms and compare it with the empirical knowledge. Figure 5 plots the relative importance of each one of the sixteen attributes considered in the UCS prediction of soil-binder-water mixtures reinforced with fibers, according to the four ML algorithms implemented in this study. Taken ANN.UCS model as reference, which achieved the best overall performance as above shown, in the ranking of the first four key variables, it may be found the influence of the binder dosage (\(D_{Kg/m}\)= 13.5%), soil characteristics (\(\omega_{0}\)= 12.8%, %Clay= 8.5%) and fiber type (\(T_{fiber}\)=  
Figure 4: The relationship between UCS experimental versus predicted values of soil-binder-water mixtures reinforced with fibers according to: (**a**) the ANN.UCS model; (**b**) the REUCS model.
# A High-Resolution SAR Focusing Experiment Based on GF-3 Staring Data
## 2 Focusing Experiment Based on GF-3 Staring Data
### Processing Flow  
In this subsection, a processing flow of GF-3 in ST mode is proposed and it can be found in Figure 5. After data acquisition and preprocessing, the echo data and auxiliary parameters are obtained. Then, the antenna element correction mentioned in Figure 3 is performed on the echo data and some parameters, such as the Doppler centroid frequency and the Doppler frequency modulation rate, are calculated based on auxiliary parameters. In azimuth-time domain, the first-order motion compensation forces the range history of the target into a perfect hyperbola, which is the foundation of the imaging algorithms. The azimuth prefiltering operation can suppress the Doppler aliasing phenomenon in azimuth. The next step is using the classical chirp scaling algorithm to deal with the prefiltered data. At the same time, the stop-and-go correction is carried out in range-Doppler and two-dimensional frequency domains. Before azimuth FFT, the cubic model error is corrected in range-Doppler domain. In order to suppress the sidelobes, Taylor windows are added in range and azimuth. The azimuth weight window was added in azimuth-time domain after prefiltering, and the range weight window was added in range-frequency domain. Finally, antenna modulation was compensated at the image level and a focused image was produced.
## 4 Bifurcation Analysis, Stability Charts, and Control Performance  
Depending on the mathematical modeling and analysis given above, the bifurcation behaviors, stability conditions, and control performance of the twelve-pole rotor system are discussed in this section. The steady-state vibration amplitudes (\(a\) and \(b\)) of the twelve-pole rotor are plotted versus the different parameters by solving the derived nonlinear algebraic Equations (34)-(37). In addition, the stability of these amplitudes is studied via the exploration of the eigen values of Equation (40). In all response curves (i.e., Figures 3-6, 12-14, 19-21 and 25), the solid lines represent the stable solution, and the dotted lines refer to the unstable solutions. As Equations (34)-(37) govern the steady-state oscillation amplitudes of the rotor system that is governed by Equations (8) and (9), numerical simulations for the plotted response curves are performed by solving Equations (8) and (9) using the Runge-Kutta algorithm. The steady-state oscillation amplitudes that were obtained numerically by solving Equations (8) and (9) are plotted as small-circles (when sweeping the bifurcation parameter forward) and as big-dots (when sweeping the bifurcation parameter backward). The analytical and numerical results are obtained by adopting the following values of the system parameters unless otherwise is mentioned: \(p=1.22,\ d=0.005,\ \alpha=30^{\rm o},\ f=0.01\), and \(\Omega=\omega+\sigma,\sigma=0\)[2; 3; 4; 5; 6; 7; 8; 9; 10].
## 3 Materials and Methods
### Evaluation Metrics  
To evaluate and compare the performance of the proposed fall detection algorithm with four types of potential architectural models [41], _Sensitivity_ (also known as _Recall_), _Precision_, _Accuracy_, and _F1-Score_ are investigated. These metrics were selected because they provide a comprehensive assessment of a model's performance, particularly in binary classification tasks like fall detection, where both false positives and false negatives have significant implications. In this study, a True Positive (TP) occurs when the model accurately identifies a fall, which is crucial for timely intervention. A True Negative (TN) is when the model accurately identifies an ADL, preventing unnecessary alarms. A False Positive (FP) is when the model incorrectly identifies an ADL data sample as a fall, leading to nuisance alerts. A False Negative (FN) is when the model incorrectly identifies a fall data sample as an ADL, which is the most critical error, as it means a real fall goes undetected. _Sensitivity_, also referred to as recall, measures the model's ability to correctly identify TPs, indicating the ratio of actual positives correctly identified by the model. In fall detection, high _Sensitivity_ is paramount to ensure that actual falls are not missed. _Precision_ measures the correctness of positive predictions, showing the ratio of TP predictions to all positive predictions made by the model. High _Precision_ helps in minimizing false alarms, which can reduce user annoyance and caregiver burden. _Accuracy_ measures the overall correctness of the model's predictions, representing the proportion of TP and TN predictions among all predictions made by the model. It provides a general overview of the model's performance. The _F1-Score_ is the harmonic average of _Precision_ and _Sensitivity_, offering a balanced evaluation of the model's performance by considering both _Precision_ and _Sensitivity_. This metric is especially important in datasets with imbalanced classes, as it provides a more robust measure than _Accuracy_ alone, ensuring that the model performs well on both falls and ADLs.
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
## 3 Verification of the Numerical Algorithm
### Field Experiment  
#### 3.2.1 Description of the Experiment  
To verify the proposed numerical algorithm, a field experiment on a real bridge in Tianjin was carried out. Figure 6 shows the testing site. It can be seen that the construction of the pile-group foundation has been completed, while the superstructure has not yet been constructed.  
Figure 5: Vertical impedance function of pile-group foundation compared with a rigorous method.  
The size of the testing cap was 7.6 m \(\times\) 5 m \(\times\) 2 m, and each foundation had 6 piles in total, the layout of which is shown in Figure 7. The length and diameter of each pile were 42.1 m and 1.0 m, respectively. The main kinds of soil in the bridge site were silt, mucky silty clay, silty clay, and clay. The distribution of the soil is shown in Figure 8, and the corresponding characteristics are shown in Table 2.  
Figure 6: Testing site.  
Figure 7: Layout of piles (unit: cm).  
There were three working conditions in the test:  
1. Impact the top of the cap in a vertical direction and obtain its vertical acceleration response;
2. Impact the north side of the cap and obtain its acceleration response in a north-south direction;
3. Impact the east side of the cap and obtain its acceleration response in an east-west direction.  
Figure 10: Sensors used in the test.  
Figure 9: Knocking the cap surface with the hammer.  
The type of hammer and the data acquisition instrument were DFC-1 and INV3018A, respectively--both from the China Orient Institute of Noise & Vibration. The type of sensor selected was 941B, from the Institute of Engineering Mechanics, whose sensitivity and passband are 0.3 Vs\({}^{2}\)/m and 0.25-80 Hz, respectively, due to microtremors, and their sensitivity can meet the required test precision. The sampling frequency was set to 256 Hz and, under each working condition, the hammer was knocked about 20 times so that the signal could be collected.
## 3 Results and Discussion
### Surface Current  
The surface current for the various lower band antenna configurations, i.e., single-element, 2-element array and 2-port MIMO antenna, is depicted in Figure 9. It can be noted that, in all the cases when either antenna-1 or antenna-2 is excited while keeping the other elements un-excited, there is non-significant change in the surface current density of the other antenna. This is an indication of good isolation characteristics, or, in other words, the radiating characteristics of any single element remain free from the influence of another element. In addition, it can also be observed that the current density is distributed around feed and patch of each element, which results in the generation of an omni-directional radiation pattern, as explained in briefly in [25].  
Figure 8: Hardware prototype for the integrated design: (**a**) front view; (**b**) back view.  
Figure 7: A 4-port mmWave MIMO configuration.  
The surface current for the mmWave MIMO configuration is reported in Figure 10. It can be noted that, in all of the cases in which either antenna-1, antenna-2, antenna-3 or antenna-4 is excited, there is non-significant change in the surface current density of the other antennas, thus showing low influence of the surrounding elements on the performance of the excited element. Moreover, it can also be observed that the maximum current density is around the mid of the patch for both single-element and MIMO antenna configuration, as depicted in Figure 10. It is worthy to note here, that the surface current for the integrated antenna configuration remains identical to the individual MIMO configuration of 4G and 5G antenna system. Thus, the radiation characteristics of both lower and higher frequency antenna system remain unchanged after their integration on the same board.  
Figure 10: Surface current for the high-band MIMO final: (**a**) single element surface current distribution; (**b**) MIMO-1 surface current distribution; (**c**) MIMO-2 surface current distribution; (**d**) MIMO-3 surface current distribution; (**e**) MIMO-4 surface current distribution.  
Figure 9: Surface current for the mid-band MIMO final and progression stages: (**a**) single-element surface current distribution; (**b**) array surface current distribution; (**c**) MIMO-1 surface current distribution; (**d**) MIMO-2 surface current distribution.
## 4 Volatile Aroma Compounds Biosynthetic Pathways and Related Enzymes
### Fatty Acids Pathway
#### 4.1.1 \(\beta\)-Oxidation  
Although the degradation of straight chain fatty acids by \(a\) and \(\beta\)-oxidation is a major process for the formation of flavor molecules in all organisms, the specific pathways in plants are not well understood. [PERSON] _et al._ described varied roles for this pathway in relation, not only to fatty acid catabolism but also, to amino acid metabolism and biosynthesis of hormonal compounds [130]. \(\beta\)-Oxidation results in successive removal of C\({}_{2}\) units (acetyl CoA) from the parent fatty acid. The detailed mechanisms of conventional \(\beta\)-oxidation are well established [131]. [PERSON] _et al._ reported that \(\beta\)-oxidation of fatty acids is the primary biosynthetic process providing alcohols and acyl coenzyme A (CoAs) for ester formation [4]. Fatty acid acyl-CoA derivatives are converted to shorter chain acyl CoAs by losing two carbons in every round of the \(\beta\)-oxidation cycle, requiring flavian adenine dinucleotide (FAD), nicotinamide adenine dinucleotide (NAD), and free CoA. Acyl CoAs are reduced by acyl CoA reductase to aldehyde that in turn is reduced by ADH to alcohol for use by AAT to produce esters [132]. Pear and apple aromas have been two classical examples of volatile formation through the \(\beta\)-oxidation pathway [133].  
The biosynthesis of lactones, key aroma components in fruits such as peach and nectarine (\(\gamma\)-decalactone and \(\gamma\)-dodecalactone), pineapple (\(\delta\)-octalactone), or coconut (_Cocos nucifera_ L.) (\(\gamma\)-octalactone), is also associated with the \(\beta\)-oxidation pathway [134]. In fact, most hypotheses on lactone biosynthesis in fruits put in contact the two major pathways producing aroma compounds from fatty acid, \(\beta\)-oxidation, and LOX [4]. Despite the importance of these compounds in fruit aroma, there is a lack of enzymatic studies in fruits, and microorganisms serve as a model for studying lactone biosynthesis [135].
# Large Field-Size Elliptic Curve Processor for Area-Constrained Applications
## 3 Proposed Crypto Processor Architecture
### Control Unit and Clock Cycles Calculation  
An FSM-based dedicated controller is used to implement Algorithm 1 for PM computation. Therefore, during each stage of the FSM, the corresponding read/write signals are generated for the RegFile unit. Moreover, the dedicated controller is (also) responsible to generate the corresponding control signals for routing multiplexers. Using the proposed architecture, the affine to projective conversions of Algorithm 1 take 4 clock cycles. The \(for\) loop (or point multiplication in projective coordinates) in Algorithm 1 requires 14 instructions of Table 1. Out of these 14 instructions, 7 are for point addition and the other 7 are for point double. All these 14 instructions require addition, multiplication and square operations. Therefore, the computational cost of the \(for\) loop of Algorithm 1 is \(14(m-2)\) clock cycles, where \(m\) is the implemented key length.  
For \(GF(2^{409})\) and \(GF(2^{571})\), the clock cycle requirement is 5698 and 7966, respectively. The reconversions from projective to affine of Algorithm 1 require two modular inversion operations. As we have mentioned before, the inversion needs \(m-1\) squares followed by 10 and 11 field multiplications. In addition, the cycle count for each square and multiplication computation is one. Therefore, the total cycle count for each inversion over \(GF(2^{409})\) and \(GF(2^{571})\) is 4080 and 6270 respectively. The total cost for inversion (in terms of clock cycles) over \(GF(2^{409})\) and \(GF(2^{571})\) is 8160 and 12,540 respectively. The 41 cycles are needed to calculate the remaining computations of reconversion in Algorithm 1. Consequently, the total number of clock cycles (to implement Algorithm 1 over \(GF(2^{409})\) and \(GF(2^{571})\)) are 13,903 and 20,551 respectively.