esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of a Modal Work Approach for Forced Response Analysis of Bladed Disks
## 3 Results and Comparisons
### LPT Bladed Rotor Test Case
#### 3.2.6 Harmonic Analysis with ANSYS  
Two harmonic cyclic symmetry analyses based on mode superposition were performed to solve the forced response amplitude of the bladed rotor under the effect of wakes due to the IGV and the stator. In detail, the first analysis considers the IGV excitation (EO \(\,=\,7\)) as a forcing function, while the second computation accounts for the stator excitation (EO \(\,=\,126\)). For each analysis, the corresponding decomposed pressure perturbation coming from unsteady multi-row analysis and the aerodynamic damping coming from flutter single-row analysis are considered.  
Before solving the harmonic cyclic symmetry analyses, the results of the forcing mapping step have to be discussed, as the transfer of the forcing functions from the fluid to the solid domain represents a key aspect of the validation comparison for this test case. The results of the mapping steps can be checked by using the PLMAP command in terms of target and source pressures. The real and imaginary parts of pressure perturbation are shown in Figure 14 for EO \(\,=\,7\) and Figure 15 for EO \(\,=\,126\), representing a portion of the target blade surface and source nodes.  
The forcing functions mapped on the FEM blade surface slightly differ from the original ones coming from the fluid domain, not only in the maximum and minimum values but also in the distribution shape. The choice of a different number of nodes for the interpolation in the MAP command does not have a beneficial effect on the mapping results. The discrepancies can be explained by the higher difference in the number of discretization points between FEM and CFD models; in fact, in this exercise, the number of CFD blade surface nodes is more than four times greater than those of the FEM blade surface. This difference justifies the small discrepancies in the mapped pressure on the coarser FEM mesh where the forcing distribution is smoothed, affecting the forced response results. The second reason is the error in the overlapping of the forcing function point cloud with the FEM blade surface. This overlapping procedure is performed by the user in the ANSYS  
Figure 13: Critical damping ratio as a function of circumferential order for the bladed rotor.  
environment, whereas in the Tremor procedure, this aspect is automatically handled by means of an optimization procedure.  
The maximum amplitude of the total displacement resulting from the harmonic cyclic symmetry analyses is computed at the frequencies of interest for the first mode family: the peak of the resonance response at 361.67 Hz in the case of IGV excitation, corresponding to ND \(\,=\,-7\) in the ZZENF diagram, and the first BPF of the stator (6510 Hz), far from the natural frequency of the first mode family corresponding to ND \(\,=\,+21\) with a natural frequency equal to 362.48 Hz.
# Image-Based Tactile Deformation Simulation and Pose Estimation for Robot Skill Learning
## 4 Discussion
### Pros and Cons  
The primary advantage of the TacTip simulation model lies in its comprehensive tunability and adaptability. It features fully customizable parameters, allowing for precise adjustments to enhance accuracy, accommodate various TacTip sensor types, and even extend to other tactile sensors with similar mechanisms. This level of customization offers significant flexibility and precision, making it a highly versatile tool for tactile sensor simulation. The researchers behind this model anticipate that its mathematical-based approach will inspire the development of future tactile sensor simulations, thereby advancing the field and broadening the scope of applications.  
Another strength of the model, from the TacTip simulation task's perspective, is its ability to accurately simulate deformations for standard contact surfaces, demonstrating high reliability in scenarios involving regular, predictable surfaces. Comparing to [12], the simulated shape in this simulation is more similar to deformed skin observed in real-world. Compared to [4], this simulation also provides accurate depth information of the deformed skin in addition to pin position in 2D plane. By integrating the tactile feedback simulation from this research into the pix2 pix architecture from [PERSON] et al. [4], the alignment between simulated and real tactile feedback would be more accurate, potentially improving sim-to-real policy transfer learning. However, its limitations become evident when dealing with irregular or non-uniform surfaces. Specifically, the model struggles to determine the chain shape in scenarios involving discontinuous contact surfaces, highlighting the need for further refinement to address a broader spectrum of tactile conditions.  
The current approach for approximating the contact boundary area involves scaling down the detected collision surface by a predetermined factor. Although this method offers simplicity, its accuracy may vary across different applications. There is an opportunity to develop a more nuanced mathematical approach for determining the contact boundary scale, which could significantly enhance the model's precision and practical applicability.
# A Review of Neuro-ML Breakthroughs in Addressing Neurological Disorders
## 5 Discussion
The answers to the RQS established at the beginning of this research are as follows:  
* RQ1: The most commonly used ML methods for diagnosing neurodegenerative diseases are SVM (597 articles), ANN (525 articles), RF (457 articles), CNN (251 articles), and LSTM (142 articles). Other models, such as XGBoost, SVR, and MLP, have been studied less. These methods are mainly applied in diagnosing Alzheimer's, Parkinson's, and MS.
* RQ2: ML models surpass traditional methods in diagnosing neurodegenerative diseases through their ability to process large volumes of heterogeneous data. ML models can identify hidden patterns. For example, RF and SVM have reported accuracies of over 90% in diagnosing Alzheimer's and Parkinson's, compared to traditional methods that rely on subjective clinical assessments. Performance varies depending on the size of the dataset and its quality. * RQ3: The main challenges include unbalanced datasets, affecting the models' generalization ability. Additionally, the lack of standardized protocols for evaluatingthe performance of ML models makes it difficult to compare results between studies. Computational resources represent another challenge. Models such as CNN and LSTM require significant resources for training, which is a limitation for researchers. * RQ4: The major limitations arise from the challenges presented earlier. Thus, in the category of limitations, the dependence on datasets that need to be voluminous and correlated, the underrepresentation of rare diseases, the underrepresentation of a class of algorithms that present difficulties in implementation both computationally and algorithmically, as well as the lack of standardization of performance metrics in relation to the specifics of neural diseases are identified. The results are discussed in detail below.
## 4 Conclusions, Limitations and Future Research  
### Conclusions  
This study assessed the potential increase in flood hazard for the city of Elblag due to storm surges in the Vistula Lagoon under changing climatic conditions. The hydrodynamic simulations conducted using the HEC-RAS 2D model demonstrated a strong correlation between wind-driven water accumulation and the flood hazard in the southwestern part of the lagoon.  
Key findings of the study include the following:  
* The historical storm surge event of January 2019 was successfully reproduced using the HEC-RAS 2D model, validating its applicability for simulating wind-induced water level changes in shallow lagoons;
* The numerical simulations of synthetic extreme wind scenarios showed that water levels in the lagoon could exceed 3 m above sea level when wind speeds reach 35 ms\({}^{-1}\), significantly surpassing flood warning and alarm thresholds;
* A non-linear relationship was observed between wind speed and water accumulation, indicating that even moderate increases in storm intensity could lead to disproportionately higher flood hazards.  
The results highlight the increasing vulnerability of Elblag and the surrounding Zulawy Elblaskie polder areas to extreme weather events, emphasizing the need for enhanced flood risk management strategies.
# Analysis of the Transonic Buffet Characteristics of Stationary and Pitching OAT15A Airfoil
## 2 Numerical Methods  
### Unsteady Aerodynamic Governing Equations  
Unsteady aerodynamic computations were performed using an in-house hybrid-unstructured flow solver which solves URANS equations, which were coded by the Institute of Mechanics, Chinese Academy of Sciences (CAS).  
NS equations in the Arbitrary Lagrangian Eulerian (ALE) coordinate system can describe the motion of a fluid in the coordinate system moving at an arbitrary velocity, and integral flow equations can be expressed as follows:  
\[\frac{\partial}{\partial t}\int\limits_{V}\mathbf{Q}dV+\oint\limits_{S}\left(\mathbf{ G}_{c}-\mathbf{G}_{v}\right)\mathbf{n}dS=0 \tag{1}\]  
where \(\mathbf{Q}\) represents the conservation vector, and \(\mathbf{G}_{c}\) is the convective flux, denoted as \(\mathbf{G}_{c}=\mathbf{G}-\mathbf{x}_{t}\mathbf{Q}\). The vector \(\mathbf{x}_{t}\) represents the velocity of motion of the kinematic coordinate system in the inertial system. When the structure is stationary in the flow field, the motion of the coordinate system is not considered, i.e., \(\mathbf{x}_{t}=0\). \(\mathbf{G}_{v}\) represents the viscous flux;denotes the area of the boundary of the control body \(V\); \(\mathbf{n}\) represents the normal unit vector of the surface; and \(t\) stands for the physical time. Roe format is used for the inviscid term discretization as well as the second-order central difference format for the viscous term discretization. For unsteady computations, the second-order dual-time stepping method is applied for the time advance. The method iterates over the pseudo time step several times until it converges, and then updates the flow field in the physical time step. Details of the numerical method can be found in Nie [17]. To close the equations, the sst-k\(\omega\) two-equation turbulence model is adopted.  
When the structure is in the pitching oscillation, a grid deformation method must be used to match the grid with the new wall boundary condition. The scheme of the computational grid deformation is based on radial basis function (RBF) interpolation [17]. The interpolation coefficients of aerodynamic surface nodes are calculated based on the structural displacements using the RBF method, and then the computational domain mesh is updated.
# A Method of Ultrasonic Finger Gesture Recognition Based on the Micro-Doppler Effect
## 2 Methods
### Micro-Doppler Processing  
#### 2.3.1 Preprocessing  
In Section 2.2, the designed modulated pulses can be expressed by:  
\[T(t)=u(t-nT)\exp(j2\pi f_{c}t), \tag{3}\]  
where \(n=0,1,2,\ldots,N\), \(T=PRI\) is the pulse repetition interval, \(f_{c}\) is the center frequency of the transmit signal, and \(u(\cdot)\) is the complex envelope of modulated pulses. After reflected from finger motions, the received signal can be described as:  
\[R(t,T^{{}^{\prime}})=u^{{}^{\prime}}(t-T^{{}^{\prime}})\exp\left[j2\pi f_{c} \left(t-\frac{2R(T^{{}^{\prime}})+2x(T^{{}^{\prime}})}{c}\right)\right], \tag{4}\]  
where \(T^{{}^{\prime}}=nT\), \(n=0,1,2,\ldots,N\). The received signal preprocessing flow as show in Figure 4 includes sampling, bandpass filtering, IQ demodulation, and lowpass filtering. After preprocessing, a two-dimensional pulse Doppler data matrix can be obtained. Each cell in the matrix can be represented by \(y[l,n]\), and \(y[l,n]\) is  
\[y[l,n]=u^{{}^{\prime}}\exp(-j\frac{4\pi}{\lambda}R_{l})\exp(j2\pi f_{d}nT), \tag{5}\]  
where \(l\) is the range bin and \(n\) represents the nth pulse, \(n=0,1,2,\ldots,N\). The larger n is, the longer the data collected is. The flow of preprocessing is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c|c c} \hline \hline \multicolumn{2}{c|}{**Parameters Setting**} & \multicolumn{2}{c}{**Performance**} \\ \hline Pulse carrier frequency (\(f_{c}\)) & 300 kHz & Doppler frequency resolution(\(f_{des}\)) & 15.625 Hz \\ Width of pulses (\(\tau\)) & 12 & Velocity resolution (\(v_{res}\)) & 0.009 m/s \\ Pulse repetition interval (\(PRI\)) & 1 ms & Range resolution (\(R_{res}\)) & 0.0069 m \\ Coherent processing interval (\(CPI\)) & 64 ms & Unambiguous distance & 0.172 m \\ Sampling frequency (\(f_{s}\)) & 2 M/s & Unambiguous velocity & 0.2867 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters setting and performance.  
Figure 3: The designed modulated pulses.
## 4 Experimental Results
### Comparison of GS Method and SDS Method  
For bright objects, the star image can be well segmented by using the correlation segmentation method combined with the standard deviation thresholding selected with the two methods in Section 3.2, and the difference is that the lower standard deviation thresholding slightly results in more noise points. However, for a dark object without clear contour and significant Gaussian morphology, the SDS method has obvious advantages, as shown in Figure 7.  
Figure 6: Recognition results in two scenes including point objects and star streaks are shown, and the difference between the two scenes is whether the point object in the image is close to the streak. Supplementary recognition is beneficial to the completion of point object contour recognition, but has little effect on streak recognition. (**a**) Original images containing stars and objects. (**b**) Column recognition. (**c**) Horizontal supplementary recognition.  
We analyze the recognized pixel number of the five objects in different \(\sigma\) thresholdings. When the \(\sigma\) thresholding is more than 24.2, the pixel number begins to decrease, which means that part edges of the objects are filtered out, as shown in Figure 7d. The thresholding obtained with SDS method, 24.11, is perfectly consistent with the optimal thresholding, 24.2. This result proves that the SDS method is more valid, and it can identify the dark objects and can also filter out most of the noises. Although the removal effect of background noises with GS method is good, it makes the object recognition incomplete or impossible.
## 3 Reducing Transmission Overhead without Compromising Throughput
### LARep Design
#### 3.2.1 Phase 1: Determining the Relative Distance between Nodes
```
Input:\((x_{i},y_{i})\), location references in \(LT\{loc_{a}\}\), \(r\) Output:\(loc_{i}\)
1 foreachincoming location \((x_{i},y_{i})\) in sampling interval \(\Delta t_{i}\)do
2 foreachInput \(\{loc_{a}\}\)do
3\(d_{a,i}=\sqrt{(x_{a}-x_{i})^{2}+(y_{a}-y_{i})^{2}}\);
4 if\(d_{a,i}<2r\)then
5\(loc_{i}=\langle x_{a},y_{a}\rangle\);
6 break;
7
8
9
10 end if
11
12 end if
13 return\(loc_{i}\)
```  
**Algorithm 1** The algorithm for recording location information  
Extracting Significant Locations  
Significant locations are identified by more number of recurrences in \(LT\). At every sampling interval \(\Delta wt\), a node running on LARep extracts the two most recurring location references from its location table--where \(w\) is a constant of the algorithm. These locations represent POIs, and are denoted by \((x_{h},y_{h})\) and \((x_{w},y_{w})\), corresponding to the node's home and work locations, respectively. Although incoming GPS coordinates may slightly vary each time the node is in either location, the location reference records only a single pair of approximated coordinates each sampling interval  
Figure 12: Structure of the location table. \(\Delta t\), provided the circular area formed by the incoming pair of coordinates intersects with that of a previous coordinate. Here, to facilitate understanding, we further explain the working principle of PoiTrace with an example scenario. Consider the map of a fictional city in Figure 13 which is divided into 9 regions labelled \(A\) to \(I\). [PERSON], a user in the city lives in region \(C\) and travels to work in region \(G\). The first three GPS readings takes place in his house at location \(a\), \(b\), and \(c\). The next reading takes place on his way to work, at location \(d\). The next two readings take place in his office, at location \(e\) and \(f\). \((x_{a},y_{a})\), the GPS reading in location \(a\) is recorded as \(\langle x_{a},y_{a}\rangle\) in the location table. However, as shown in Table 3, the readings at location \(b\) (i.e., \((x_{b},y_{b})\)) and \(c\) (i.e., \((x_{c},y_{c})\)) are also recorded as \(\langle x_{a},y_{a}\rangle\), instead of \(\langle x_{b},y_{b}\rangle\) and \(\langle x_{c},y_{c}\rangle\), respectively. This is because the circle of radius \(r\) formed from these locations intersects with the circle formed from a previous location, i.e., location \(a\). Likewise, the readings at \(e\) and \(f\) are both recorded as \(\langle x_{e},y_{e}\rangle\). After a period of \(\Delta 6t_{1}\) (here we take \(w\) as 6), the two most significant locations of this user become \((x_{a},y_{a})\) and \((x_{e},y_{e})\), which we consider as the home and work location, respectively.
## 3 Methods
### Improvements to the RT DETR Algorithm
#### 3.2.2 Improving Positional Encoding in the AIFI Module of the RT DETR Algorithm  
To enhance the model's positional information encoding capability, this study used learned positional encoding (LPE) to improve the positional encoding mechanism in AIFI. In transformer-based models, positional encoding is an essential mechanism to maintain the order information of sequence data.  
Traditional fixed positional encoding uses fixed positional encoding (FPE) calculated by the following Formulas (3) and (4):  
\[\text{PE}(pos,2i)=\sin\Bigl{(}\frac{pos}{10000^{2i/d_{model}}}\Bigr{)} \tag{3}\]  
\[\text{PE}(pos,2i+1)=\cos\Bigl{(}\frac{pos}{10000^{2i/d_{model}}}\Bigr{)} \tag{4}\]  
where \(d_{model}\) is the dimension of the model, \(pos\) is the position, and \(i\) is the dimension. In this way, the positional encoding of each position is a fixed vector that does not change with model training.  
Compared to fixed positional encoding, learned positional encoding (LPE) provides a more flexible method for encoding positional information [39]. In this mechanism, posi  
Figure 6: Streamlined Design of time-efficient layers.  
tional encoding is part of the model parameters, learned through the model training process rather than predefined. Specifically, each positional encoding is randomly initialized at the start of model training and adjusted through optimization algorithms like backpropagation and gradient descent during training.  
Learned positional encoding (LPE) is obtained during the model training process, where each position's encoding is part of the model parameters and can dynamically adjust according to the task and data. Fundamentally, LPE increases the model's flexibility and adaptability in encoding positional information, allowing the model to better capture and utilize positional information in sequence data. This can improve performance, particularly in tasks where the sequence's structure or pattern is not very regular, or the model needs a finer-grained understanding of positional information.
## 2 Materials and Methods
### Remote Sensing Data  
#### 2.3.1 Sentinel-1 Data  
Sentinel-1 is a space mission of the European Space Agency (ESA) consisting of a constellation of two satellites: S1-A and S1-B. On the satellites, a Synthetic Aperture Radar (SAR) sensor is mounted, which provides C-band (5.405 GHz) images under all weather conditions. For the period from April to September 2019, the \"COPERNI-CUS/S1_GRD\" collection was downloaded from the Google Earth Engine (GEE) platform ([[https://eartheengine.google.com/platform/](https://eartheengine.google.com/platform/)]([https://eartheengine.google.com/platform/](https://eartheengine.google.com/platform/)), accessed on 23 February 2021) using five sub-regions of Apulia as extracting masks (Figure S1, see Supplementary Material). The 164 S1 images were acquired in the Interferometric Wide Swath (IW) mode with two polarizations, VH (Vertical transmit-Horizontal receive) and VV (Vertical transmit-Vertical receive), at 10-m resolution with ascending orbit (Table 1). GEE already provided pre-processed images ([[https://developers.google.com/earth-engine/guides/sentinel1](https://developers.google.com/earth-engine/guides/sentinel1)]([https://developers.google.com/earth-engine/guides/sentinel1](https://developers.google.com/earth-engine/guides/sentinel1)), accessed on 23 February 2021).  
Figure 2: Field sampling protocol. Example of a field plot with a schematic transect (**left**). Operators measuring dead woody material with a caliper during a field survey campaign (**right**).  
The pre-processing steps include applying the orbit file, GRD border noise removal, thermal noise removal, radiometric calibration, and terrain correction using Shuttle Radar Topography Mission (SRTM) at 30 m, or the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) for high latitudes. The backscatter coefficient (\(\sigma^{\circ}\)) is converted to decibels (dB) using Equation (1):  
\[\sigma^{\circ}(\text{dB})=10^{\bullet}\text{log}_{10}\sigma^{\circ} \tag{1}\]  
where \(\sigma^{\circ}\) is backscatter for a specific pixel, \(\sigma^{\circ}(\text{dB})\) is the backscattering coefficient or normalized radar cross-section measured in dB units with values ranging from +5 dB to \(-\)40 dB. We generated the VH and VV time series by calculating the median values, then extracted the mean value of VH and VV polarizations within the plots.