esa-sceva/satcom-chunk-collection huggingface dataset:
# Retrofit of Existing Reinforced Concrete (RC) Buildings: Steel vs. RC Exoskeletons
## 2 Case Study Building
### Application of Steel Exoskeleton  
At first, a steel exoskeleton, as shown in Figure 7, was taken into account. As shown, the exoskeleton layout is intended to cover part of the east elevation of the existing building, as well as part of the west elevation side. Structurally speaking, the steel exoskeleton consists of a frame with tubular bracing which is connected to the existing RC frame by rigid links. In more detail, the steel exoskeleton is designed with:  
* Columns for the first, second, and third floors--CHS 457 \(\times\) 40;
* A column for the fourth floor--CHS 457 \(\times\) 32;
* Columns for the fifth and sixth floors--CHS 457 \(\times\) 20;
* Beams--CHS 406 \(\times\) 16;
* Concentric bracing--CHS 273 \(\times\) 16;
* Rigid links to locally connect the external exoskeleton with the existing RC building.  
In order to assess the performance of the RC building with the steel exoskeleton, PO analyses were performed with typical results, as shown in Figure 8 and Table 3.  
Figure 8: PO analysis results: comparison of bare RC building and existing building retrofitted by steel exoskeleton.  
Figure 7: Retrofit intervention for the bare RC building with steel exoskeleton: (**a**) plan and (**b**) axonometric views. The original layout of the RC building and the newly added steel structure.  
From Figure 8, it can be noted that the retrofit intervention produces a series of effects, namely: (i) an increase in the overall lateral stiffness of the bare RC building; (ii) an improvement in the seismic safety index; and (iii) a marked increase in capacity, also due to the reduction in eccentricity between the centre of mass (CM) and the centre of stiffness (CS).  
Table 3 shows the PO analysis results of sixteen load combinations provided by numerical simulation, considering the accidental eccentricity between the mass centroid and the stiffness one.  
As it is possible to observe, the C/D safety index is always greater than the unity value, and no critical situation is found, thus confirming the effectiveness of the proposed project intervention. In particular, the seismic safety index value was estimated at an average of \(\approx\)0.56 for the bare RC building and as \(\approx\)1 after the introduction of the steel exoskeleton. In conclusion, the analyses showed that the intervention on the case study RC building based on the steel exoskeleton could achieve appropriate safety indices for seismic purposes.
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 2 Materials and Methods
### Estimation of Time-Dependent Parameters in Non-Linear ODE Models via the Augmented Kalman Smoother
#### 2.2.3 Expectation-Maximization Algorithm for AKS Initial Parameters
```
1:procedureAKS(\(Raw\_data\)) \(\triangleright\)Data processing
2:\(data\leftarrowRaw\_data\)\(\triangleright\)Model parameters and initial values
3:\(G\leftarrow\)Observation_matrix() \(\triangleright\)Model parameters and initial values
4:\(x0\leftarrow\)initial_state_vector_G_guess()
5:\(u0\leftarrow\)initial_state_covariate_guess()
6:\(P0\leftarrow\)initial_state_covariance(u0)
7:\(Q\leftarrow\)initial_evolution_error_cubesso
8:\(M\leftarrow\)initial_observation_error_cubesso
9:\(x\gets x0\)\(\triangleright\)AKS initialization
10:\(P\gets P0\)
11:\(count\gets 0\)
12:\(e\gets 100\)\(\triangleright\)Main loop for AKS
13:while\(e>1e-3\)do\(\triangleright\)Predict state-space vectors and covariance matrices
14:\(Pred\_Pred\leftarrow\)AK_Prediction(\(x,P,Q\)) [Equations (6)-(8)]
15:\(\triangleright\)Filter state-space vectors and covariance matrices
16:\(Est\_P\_Est,Gain\_P\_lag,Jacob\leftarrow\)AKF_Update(\(data,G\_Pred,P\_Pred,M\)) [Equations (9)-(10)]
17:\(\triangleright\)Smooth state-space vectors and covariance matrices
18:\(smooth\_std,P\_smooth,B\_list\leftarrow\)AKS_Smoother(\(Est,P\_Est,Gain\_P\_lag,Jacob\)) [Equations (12)-(14)]
19:\(\triangleright\)Perform M+step for parameter estimation
20:\(M,Q,x0,P0\leftarrow\)M_step(\(data,Pred,P\_Pred,Jacob,P\_Est,P\_smooth,B\_list\)) [Equations (15) and (16)]
21:\(x,P,count\leftarrow\)update_AKS_variables(\(smooth\_list,P\_smooth,x0,P0,count\))
22:\(e\leftarrow\)check_convergence(\(P,x,Q,M,x0,P0\)) [Equation (17)]
23:endwhile
24:return smoothed_results
25:endprocedure
```  
**Algorithm 1** Augmented Kalman Smoother (AKS)
## 6 Conclusions  
This paper proposes two-phase current detection schemes using reduced current sensors for EMSRMs and OMSRMs, respectively. Using the schemes, the magnetization and freewheeling phase currents under multiphase excitation can be obtained using \(n/2\) and \((m+1)/2\) current sensors for EMSRMs with \(n\) phases and OMSRMs with \(m\) phases, respectively. Compared to the conventional phase current detection method, the proposed schemes can reduce the cost and volume of the system and improve the reliability and current sampling consistency of the system with only half or nearly half of the sensors conventionally used. Compared to the baseline methods, the proposed methods can relax the constraint on the phase conduction region width and thus increase the motor torque, especially at a high-speed operation by excluding the demagnetization currents. For EMSRMs, the proposed method can be much more cost-effective, with the ability to combine the low-side shunt current sensing technique. For OMSRMs, the proposed method can use the measuring range of the sensors more effectively and thus increase the current sensing resolution when the most common hall effect sensors are adopted. The SCCC can be implemented with the proposed methods by selecting the upper switches for chopping. The VPC can also be achieved with a small penalty on the negative average phase voltage by using the proposed PWM configuration and current sampling timing scheme, and  
Figure 21: Experimental platform.  
Figure 22: Experimental waveforms at 500 rpm (\(i^{\star}=50\) A) with the proposed \(N_{\rm p}/2+1\) sensors-based current detection scheme.  
this penalty only occurs when the negative average voltage is close to or higher than the negative dc-link voltage. In summary, the proposed methods are promising alternatives to the existing methods by considering the motor torque capability, the adaptation to different types of the current sensors, the measuring range, and the volume of the most common hall effect sensor.  
Conceptualization, S.S. and M.L.; methodology, H.W.; software, M.L.; validation, S.S. and Y.F.; formal analysis, Y.Z.; investigation, Y.F.; resources, Y.Z.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, M.L.; visualization, S.S. and Y.F.; supervision, H.W.; project administration, H.W.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hunan Provincial Natural Science Foundation of China (2021 JJ30185).  
Not applicable.  
The authors declare no conflict of interest.
# Signal Detection for GSTFIM Systems with Carrier Frequency Offset +
## 7 Conclusions  
In order to combat ICI caused by CFO in GSTFIM systems, we considered the construction of two kinds of detectors, i.e., MMSE- and MP-based detectors, in an iterative process toward enhanced BER performance. Specifically, based on our former works in [24], the MMSE detector was detailed in two working styles to offer a flexible construction of detection. Most important of all, an MP-based detector was developed for improving the detection permanence compared to its MMSE counterpart. When G is relatively small, the MP-based detector is even able to reduce complexity. Considering that CFO is not the only problem in practical transmission, in future works, we will focus on mitigating the influence of non-ideal time synchronization and non-Gaussian distribution toward a more robust construction of GSTFIM systems.  
Conceptualization, T.W. and S.F.; methodology, T.W.; software, S.F.; validation, C.D. and J.J.; data curation, C.D. and J.J.; writing--original draft preparation, T.W. and S.F.; writing--review and editing, C.D. and J.J.; funding acquisition, C.D. and J.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
In [20], a K-means OCR coordination scheme was proposed to group different operative scenarios taking into account the available setting groups of relays. In [21], the authors developed a similar work; nonetheless, instead of K-means, they implemented a self-organizing map (SOM) clustering technique. In [22], the authors discuss the implementation of the K-Medoids technique to cluster the operative scenarios of an MG. In [23], the authors propose an approach that uses K-means, SOM, and hierarchical techniques to improve the operating times of the optimal coordination of OCRs in microgrids; nonetheless, the coordination process must undergo an extra procedure (heuristic adjustment) to achieve feasible solutions, which renders the process more time consuming. Our proposal consists of a novel protection coordination model for directional OCRs in MGs that not only considers DG but also handles numerous operational modes. The proposal can be applicable to adaptive OCRs, with the capacity of configuring multiple settings groups, taking into account OCR operating currents and also their corresponding limitations. The proposed approach incorporates the constraint introduced in [11] which limits the PSM; in addition, as in [19; 20; 21; 22], the setting groups are limited, taking into account the real capabilities of commercially available OCRs. To complement the methodologies proposed in [20; 23] that implement the K-means technique to intelligently group operative scenarios, in this paper, the principal component analysis (PCA) technique is used to reduce the dimensionality of the dataset and improve the performance of the K-means clustering technique. In this sense, our paper complements and improves the methodology proposed in [20] as it jointly uses PCA and K-means to enhance the protection coordination process. Additionally, as opposed to [23], it does not require an additional heuristic adjustment to reach feasible solutions. A comparison with the methodologies proposed in [20; 23] is presented applied to a benchmark MG. Results show that the combination of K-means and PCA results in a better coordination scheme as compared with the use of K-means alone; furthermore, despite the potential applications of the PCA technique, to the best of the authors' knowledge, this one has not been implemented within the optimal protection coordination problem. It is also important to highlight that the simultaneous use of K-means and PCA has also not been reported for solving the optimal coordination of directional OCRs in MGs.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### Geometric Keypoint Attention  
The geometric keypoint attention module captures the global structural correspondences prior to finer-scale refinement. At this stage, capturing the long-range geometric structure is essential for reducing the ambiguity of the correspondence under large respiratory motion, which motivates our use of a geometry-aware attention formulation. Consider the coarse-level feature embeddings \(\mathbf{F}_{\mathcal{P}}^{(L)}\in\mathbb{R}^{|\mathcal{P}|\times D_{\text{c} \text{emb}}}\). The output attention-enhanced feature \(\mathbf{H}_{\mathcal{P}}^{(L)}\) is computed as an attention-weighted aggregation:  
\[\mathbf{h}_{\mathcal{P}_{i}}^{(L)}=\sum_{j=1}^{|\mathcal{P}|}\alpha_{i,j}\Big{(} \mathbf{f}_{\mathcal{P}_{j}}^{(L)}\mathbf{W}^{V}\Big{)}, \tag{6}\]  
where the attention weights \(\alpha_{i,j}\) measure the pairwise geometric relations among points:  
\[\alpha_{i,j}=\text{Softmax}\left(\frac{(\mathbf{f}_{\mathcal{P}_{i}}^{(L)} \mathbf{W}^{Q})(\mathbf{f}_{\mathcal{P}_{j}}^{(L)}\mathbf{W}^{K}+\mathbf{r}_{ i,j}\mathbf{W}^{R})^{\top}}{\sqrt{d_{t}}}\right). \tag{7}\]  
Here, \(\mathbf{r}_{i,j}\) represents the geometric embeddings, as proposed in GeoTransformer [40], while \(\mathbf{W}^{Q},\mathbf{W}^{K},\mathbf{W}^{V},\mathbf{W}^{R}\in\mathbb{R}^{D_{ \text{c}\text{emb}}\times D_{\text{c}\text{emb}}}\) are learnable projection matrices for queries, keys, values, and geometric embeddings, respectively. Analogous computations are performed for the target cloud features \(\mathbf{F}_{\mathcal{Q}}^{(L)}\).  
A similar attention mechanism is adopted for cross-attention, which explicitly models inter-cloud interactions by exchanging information between the source and target point clouds. Compared to self-attention, the key difference is that the query \((\mathbf{f}_{\mathcal{P}_{i}}^{(L)}\mathbf{W}^{Q})\) is computed from one point cloud (e.g., \(\mathcal{P}\)), while the key and value \((\mathbf{f}_{\mathcal{Q}_{i}}^{(L)}\mathbf{W}^{K},\mathbf{f}_{\mathcal{Q}_{i}} ^{(L)}\mathbf{W}^{V})\) are taken from the other (e.g., \(\mathcal{Q}\)). This directional change allows the network to attend across point clouds and capture alignment-relevant correspondences at the coarsest level.  
To robustly capture both the intra-cloud structure and inter-cloud correspondence cues, the self- and cross-attention modules are alternated for \(N_{\text{c}}\) iterations, resulting in enhanced features \(\hat{\mathbf{F}}_{\mathcal{P}}^{(L)}\in\mathbb{R}^{|\mathcal{P}|^{(L)}|\times D _{\text{c}\text{emb}}}\) and \(\hat{\mathbf{F}}_{\mathcal{Q}}^{(L)}\in\mathbb{R}^{|\mathcal{Q}|^{(L)}|\times D _{\text{c}\text{emb}}}\). This interleaved design allows the network to iteratively refine the coarse-level representations by jointly reasoning about the global geometry and alignment consistency, which is particularly beneficial in reducing ambiguity in the correspondence under large deformations.
## 4 Results and Discussion  
Three different orbit solutions are calculated routinely within WEGC's POD processing (listed in Table 2). In order to assess the quality of the orbit solution designated for further RO processing (WEGC-BC), different analyses are performed. The WEGC-BC primary solution is compared to internal control runs (WEGC-BI, WEGC-NC) and orbit solutions from external processing centers (Section 4.1). Satellite laser ranging residuals are analyzed (Section 4.2), and measures for the orbit uncertainties are derived (Section 4.3). The results cover the analysis for CHAMP and GRACE-A in July to September 2008 and the same time period in 2013 for Metop-A/B.
# Homogeneity and Trend Analysis of Climatic Variables in Cap-Bon Region of Tunisia
## 3 Results and Discussion
### Trend in Climatic Variables
Their corresponding rates of change quantified using the method of Sen were 0.065 and 0.045 \({}^{\circ}\)C/year, respectively. It is worth mentioning that the second part of the monthly Tmax time series (after the dates of significant break) shows a mix of non-statistically significant positive and negative trends. Despite the existence of significant positive trends in June and August, no significant upward trend in the summer season was recognized. The Mann-Kendall and modified Mann-Kendall \(p\)-values and the trend slopes estimated by Sen's approach for annual, seasonal and monthly minimum air temperature are shown in Table 3. Sen's slopes of significant trends (95% levels) are shown in bold characters. For the annual Tmin dataset, a non-significant increasing trend was recorded. Furthermore, no significant upward or downward tendencies were registered in the monthly time series. These results matched those obtained by [PERSON] et al. (2018) who reported that there were no significant upward or downward trends in the monthly Tmin series at Zanjan station in Iran. However, several studies have reported the existence of significant increasing trends in Tmin and that the increment in Tmin is more pronounced than in Tmax (Zen and Zen, 2018; Zen, 2018; Zen, 2018). In our case study, the non-significant decreasing trends were captured in February, September and October with Sen's slope values equal to \(-0.002\), \(-0.013\) and \(-0.006\), respectively. Particularly for July, the Tmin dataset exhibited a significant upward trend. The corresponding Sen's slope value was equal to 0.033 \({}^{\circ}\)C/year. In the case of April, when a significant break-point was identified, both parts did not exhibit any significant trend. Therefore, it can be concluded that the observed significant increasing trend for the whole dataset of April may be related to the positive shifts in the mean. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Before Break Point If Any**} & \multicolumn{6}{c}{**After Break Point If Any**} \\ \cline{2-10}  & **MK** & **mMK** & **Sen** & **TFPWMK** & **Sen** & **MK** & **mMK** & **Sen** & **TFPWMK** & **Sen** \\  & \(p\)**-Value** & \(p\)**-Value** & & \(p\)**-Value** & & \(p\)**-Value** & \(p\)**-Value** & **Sen** & **TFPWMK** & **Sen** \\ \hline Annual & 0.453 & 0.546 & 0.007 & 0.280 & 0.010 & & & & & \\ Winter & 0.790 & 0.790 & 0.007 & 0.513 & 0.008 & & & & & \\ Spring & 0.183 & 0.142 & 0.014 & 0.209 & 0.013 & & & & & \\ Summer & 0.116 & **0.021** & 0.018 & 0.152 & 0.020 & & & & & \\ Autumn & 0.790 & 0.790 & \(-0.002\)
## 5 Performance and Simulations of Instantaneous Frequency Rate Tracking Approach
### Basics of Cubic Phase Function and Squaring Loss Issue
Simulations for different sample numbers, \(N\), are implemented, and the results are shown in Figure 13. It could be seen that the estimation threshold SNR decreases from \(-1\) dB to \(-6\) dB when \(N\) is 201, and the estimation threshold SNR decreases from \(-3\) dB to \(-9\) dB when \(N\) is 401. This proves that with the a priori phase model information, SCPF could accurately estimate IFR at a low SNR region, which obviously outperforms the standard CPF. Figure 11: Squaring loss vs. different input signal-to-noise ratios (SNRs). \[\hat{\hat{f}}=\underset{\hat{f}}{\operatorname{argmax}}\bigg{(}\sum_{i=-N/2}^{ (N/2)-1}\bigg{[}\tilde{r}_{i}\times e^{-2\pi(\frac{j}{2}\hat{t}_{i}^{2})}\bigg{]} \bigg{)}^{2}, \tag{26}\]0 The data samples used in the computation of the sequential cubic phase function (CPF).
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 4 Verification of Passenger Car AEB Control Algorithm
### Test Results of AEB Control Algorithm  
#### 4.3.1 CCRs Condition  
The parameters of the CCRs condition are set as follows: the relative distance is 120 m, the initial speed of VUT is 80 km/h, and the speed of VT is 0. SIL testing and HIL testing results in the CCRs condition are shown in Figure 10.  
Take the HIL testing curve in Figure 10 as an example, at 2.7 s, the VUT enters the L I braking status. Then, the VUT enters the L II and L III AEB braking status at 3.1 s and 3.9 s, respectively. At the end of braking at 7.2 s, the speed of VUT decreases to 0. The relative distance is 5.02 m when the VUT stops, the collision between the VUT and the VT is effectively avoided with the AEB control.
# DC2 Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
###### Abstract  
Magnetic resonance imaging (MRI) plays a significant role in the diagnosis of lumbar disc disease. However, the use of MRI is limited because of its high cost and significant operating and processing time. More importantly, MRI is contraindicated for some patients with claustrophobia or cardiac pacemakers due to the possibility of injury. In contrast, computed tomography (CT) scans are much less expensive, are faster, and do not face the same limitations. In this paper, we propose a method for estimating lumbar spine MR images based on CT images using a novel objective function and a dual cycle-consistent adversarial network (DC2 Anet) with semi-supervised learning. The objective function includes six independent loss terms to balance quantitative and qualitative losses, enabling the generation of a realistic and accurate synthetic MR image. DC2 Anet is also capable of semi-supervised learning, and the network is general enough for supervised or unsupervised setups. Experimental results prove that the method is accurate, being able to construct MR images that closely approximate reference MR images, while also outperforming four other state-of-the-art methods.  
Keywords:image cross-modality synthesis; lumbar spine; dual cycle-consistent adversarial network; semi-supervised learning; adversarial training +
Footnote †: journal: Applied
## 3 Results
### Prosthetic Hand Design
#### 3.2.2 Prototyping and Experiments  
Grasping experiments with the hand prototype were performed to validate the prosthetic hand design. A single finger was prototyped using the 3D printing technique as a first step in the experimentation. Polylactic acid (PLA) material was used in the 3D printing process because of its ease of printing. The individual finger prototype was experimentally flexed to calculate fingertip force production. It was observed that the single finger could produce ~1.5 N force when it was at the maximum flexed position. The experimental setup used to calculate the force is shown in Figure 12a.  
Figure 12: (**a**) Experimental setup for fingertip force measurement of 3D printed finger using force gauge. (**b**) Complete CAD model for the hand assembly. (**c**) Enlarged view of the differential mechanism and the motor assembly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Link** & **Length (mm)** & **Link** & **Length (mm)** & **Link** & **Length (mm)** \\ \hline \(l_{1}\) & 6 & \(l_{3}\) & 5 & \(l_{5}\) & 12 \\ \(l_{2}\) & 8 & \(l_{4}\) & 8 & \(l_{6}\) & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Link lengths of the differential mechanism.  
The complete prosthetic hand was 3D printed with four 1-DoF fingers as per the design from the previous subsection. The prosthetic hand was designed to have a simplistic 3-DoF thumb only for experimental purposes. It is critical to note that the term \"DoF\" for the prototype has been used to indicate the kinematic configuration of the fingers and does not represent the number of independent axes of motion. As designed earlier, each finger is controlled by a singular tendon and hence has one degree of actuation for the three joints. The tendon-driven thumb has flexion capabilities at the MCP and PIP joints and Ab/Ad motion at the CMC joint. The CAD model of the hand prototype is shown in Figure 12b,c. The entire hand mechanism was mounted on a 5 mm acrylic sheet for experimental purposes. The 3D printed hand model and the objects that were used in the grasping experiment are shown in Figure 13. It was observed that the hand prototype was able to grasp the objects successfully. Further, because of the differential mechanism that has been implemented, the ring and little finger flex completely during three-fingered grasping, as shown in Figure 13e,f. The motors were controlled using a custom Arduino program loaded onto an Arduino UNO board. The experiments showed that the designed hand could successfully grasp objects of various shapes and sizes. The Supplementary Materials provides additional data about the prototype and experiments.
# Chaotic Pulse-Shaping Filter Based on Root-Raised-Cosine Division
## 3 RRC-Divided Chaotic Filter  
In this section, we first propose a RRC division scheme to obtain two asymmetric pulses. Their convolution result is compared with the standard RC waveform. Taking the impulse response of the novel filter as the basis function, one new hybrid dynamical system is derived. Its topological conjugation to a symbolic sequence and a positive Lyapunov exponent are proven.