esa-sceva/satcom-chunk-collection huggingface dataset:
# Inertial Parameter Identification in Robotics: A Survey
## 5 The Output Error (OE) and Input Error (IE) Identification Approaches
### The Direct and Inverse Dynamic Identification Model (DIDIM) Algorithm  
In [43], a new algorithm termed Direct and Inverse Dynamic Identification Model (DIDIM) was proposed. The DIDIM method can be seen as a variation of the CLIE algorithm where some judicious approximations, made in the torque Jacobian matrix computation, yield \(\mathbf{G}_{\tau_{s}}=\partial\tau_{s}/\partial\mathbf{\beta}_{\beta-\mathbf{\beta}_{DIDIM }^{i}}\approx\mathbf{Y}_{\beta}(\vec{q}_{s},\vec{q}_{s},\vec{q}_{s})\). The initial nonlinear LS problem at iteration \(i\) hence turns into a much simpler linear LS problem allowing the DIDIM estimates to be calculated as  
\[\mathbf{\beta}_{DIDIM}^{i}=(\mathbf{W}_{s}^{i\top}\mathbf{\Sigma}^{-1}\mathbf{W}_{s}^{i})^{-1 }\mathbf{W}_{s}^{i\top}\mathbf{\Sigma}^{-1}\mathbf{y}_{\tau}\,, \tag{45}\]  
where \(\mathbf{W}_{s}^{i}=\mathbf{W}(\vec{q}_{s}^{i},\vec{q}_{s}^{i},\vec{q}_{s}^{i})\) is the observation matrix constructed with the joint simulated position, joint velocities and joint acceleration obtained from the closed-loop simulation of the DDM with \(\mathbf{\beta}_{DIDIM}^{i-1}\) as explained in Section 4.1. The general principle of DIDIM is illustrated in Figure 4. Once DIDIM method has converged, the covariance matrix of the DIDIM estimates is given by  
\[\mathbf{\Sigma}_{DIDIM}=\Big{(}\mathbf{W}_{s}^{\top}\mathbf{\Sigma}^{-1}\mathbf{W}_{s}\Big{)} ^{-1}. \tag{46}\]  
According to the results gathered in [12; 43; 75], the DIDIM algorithm converges significantly faster than the CLOE and CLIE methods as it only requires a single robot simulation per iteration. In [12], the authors also demonstrate that DIDIM has a similar precision as CLIE. It is interesting to note that the OE methods are generally less sensitive to noise than IDIM-OLS methods since only simulated data are used to build \(\mathbf{W}_{s}^{i}\). Until now, no formal comparison with IDIM-IV was carried out although [76] provides some elements of  
Figure 3: Block diagram of the Closed-Loop Input Error (CLIE) identification method.  
discussion, suggesting that the two methods have similar performance. However, further investigations must be conducted to refine the conclusions made in [76].  
Direct Dynamics Identification Model (DDIM) with Nonlinear Kalman Filtering (NKF) and Neural Networks (NN) Methods
# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 3 Proposed Method
### Construction of TMRC-SSD  
The purpose of this research is to improve the accuracy of tramp material detection in raw coal. We propose an improved SSD detection framework named as tramp materials in raw coal single-shot detector (TMRC-SSD), as shown in Figure 8. It mainly includes backbone network, an extract feature module, a feature fusion module, a detection head and non-maximum suppression. The original image is processed and entered into the subsequent processing network at a preset size. Next, after a stem block module, a four-level feature extractor is used in the backbone to down-sample the feature map stably. Multi-branch dilation convolutional structure was integrated into conv7-conv11 modules to obtain rich receptive fields and extract robust feature map information, so as to avoid the sharp increase of computing cost caused by the expansion of receptive fields. In order to realize the information interaction between feature graphs, four cross-layer feature fusion modules are integrated into the feature fusion module. The attention fusion block is used to fuse adjacent CLFF feature layers. SENet improves the accuracy of detection by enhancing the weight of the region of interest, and at the same time enhances the network's ability to detect small targets. Before each prediction layer, a residual block is used to control the flow direction of the gradient in the loss function and improve the performance of the network.  
Figure 7: Schematic diagram of attention residual block.
# Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors
### Outline of the Current Study  
In this study, a hybrid multi-objective optimization algorithm is presented. Since the material selection is also considered, the optimization problem is discrete. Gradient-based optimization techniques are not feasible for discrete optimization, therefore a genetic algorithm (GA) supported by a local improvement was used. Global optimization techniques such as the GA generally converge slower than gradient-based algorithms. However, evolutionary algorithms, such as the GA, are able, due to their working procedure, to find several Pareto-solutions in a single iteration. Also, they are robust and require only limited knowledge about the problem [17; 18]. The local improvement, which is executed by sequential quadratic programming (SQP), solves a continuous sub-problem based on initial solutions created by the GA. The basic idea of using a local improvement strategy to improve the solution of an optimization is also presented in [19]. SQP is a local gradient-based optimization algorithm for single-objective nonlinear optimization problems with nonlinear equality and inequality constraints [20]. The proposed algorithm optimizes three objectives: energy capacity, costs and productivity. The algorithm therefore considers the influence of manufacturing through a novel modeling approach. This approach provides a better understanding of trade-offs between different design solutions. Employing multi-objective optimization may yield more practicable information for decision-makers to select the right design for a specific flywheel application. To create safe designs, each of the above-mentioned failure criteria was evaluated, i.e., Maximum Strength, Maximum Strain and the Tsai-Wu criterion. The algorithm was used to solve optimization problems for cylindrical disk-shaped geometries commonly used in mobility applications such as LRT.
## 3 Imaging in the Follow-Up of Treated HCC
### Considerations on the MRI Scanning Protocol in HCC Follow-Up
DWI showed superior reliability compared to analysis of the hepatobiliary phase in the detection of small hypovascular HCCs located in the proximity of blood vessels [120]. Calculation of apparent diffusion coefficient (ADC) values may be useful in predicting the therapeutic response, as significantly lower ADC values were associated with poor or incomplete post-TACE tumor responses [121]. Moreover, ADC was shown to predict survival rates after TACE [122]. However, as shown in the previous sections, the treated nodule may show great inhomogeneity due to the variety of physical and biological processes induced by the treatment. Therefore, when measuring average ADC values in the treated nodule, the heterogeneous structure may influence the findings and lead to false results [123]. Subtraction imaging may be particularly useful in discriminating viable tumor tissue from other T1-WI hyperintensities within the treated nodule, such as the peripheral rim occurring after thermal ablation [124]. A high percentage of patients may show hyperintensity on the non-enhanced T1-WI within the ablation zone and subtraction of the unenhanced set of images from the arterial phase images may be crucial in the differential diagnosis [125; 126].
# Using Coherent Hemodynamic Spectroscopy Model to Investigate Cardiac Arrest
###### Abstract  
The Coherent Hemodynamic Spectroscopy (CHS) model provides a quantitative framework for modeling cerebral hemodynamics and metabolism, particularly in response to small physiological perturbations. However, in its original approximate formulation it was limited to conditions where parameter changes were constrained to 10-20%, making it unsuitable for modeling extreme physiological disruptions such as cardiac arrest. In this study, we present a detailed discussion of the algorithm using the complete CHS model, which extends the original framework by solving partial differential equations without approximations to handle large non-periodic perturbations. This model was applied to data from a previously published cardiac arrest and cardiopulmonary resuscitation (CPR) study in pigs, where cerebral blood flow changed by 100%. While our prior work demonstrated the utility of this approach for analyzing cerebral microvascular and metabolic parameters, it did not include the algorithmic details necessary for reproducibility and broader application. Here, we address this gap by describing the algorithm's workflow, including the use of non-linear multivariate optimization, and its ability to recover multiple physiological variables, such as the capillary and venule oxygen saturations, and parameters, such as the capillary oxygen diffusion rate, and arterial oxygen saturation. The latter can be valuable when the pulse oximetry measurements are unavailable due to unstable, weak or absent pulse. This study underscores the importance of non-linear modeling in advancing the application of CHS to extreme physiological conditions and highlights its potential for translational research and clinical innovation.  
coherent hemodynamic spectroscopy; hemodynamic model; near-infrared spectroscopy; NIRS; brain; neuronal activity; cardiac arrest; resuscitation; CPR; hyperspectral near-infrared spectroscopy; microvasculature; laser Doppler flowmetry; diffuse correlation spectroscopy; cerebral blood flow; cerebral metabolic rate of oxygen
###### Abstract  
The aim of this study is to introduce a new filament and novel 3D printing technique to adjust the density of a printing job in order to mimic the radiological properties of different tissues. We used a special filament, Light Weight PLA (LW-PLA), which utilizes foaming technology triggered by temperature. Cylindrical samples were printed at various temperatures, flow rates, print speeds, and diameters. A computed tomography (CT) scan was performed to identify their radiological properties in terms of the mean Hounsfield Unit (HU). The densities of the samples ranged from 0.36 g/cm\({}^{3}\) to 1.21 g/cm\({}^{3}\), corresponding to mean HU values between \(-702.7\pm 13.9\) HU and \(+141.4\pm 7.1\) HU. Strong linear correlations were observed between the flow rate and density as well as the flow rate and mean HU. The axial homogeneity of the samples was reported as being comparable to that of distilled water. A reduction in the mean HU was observed at a lower print speed and it changed slightly with respect to the sample size. Reproducibility assessments confirmed consistent results for identical printing jobs. Comparisons with regular PLA samples revealed a superior homogeneity in the LW-PLA samples. The findings of this study suggest a practical and accessible solution for mimicking all of the soft tissues, including the lungs, by using a single filament.  
3D printing; density modulation; soft tissue; computed tomography; phantom +
Footnote †: journal: Medical Physics Department, Health Sciences Institute, Dokuz Eylul University, Izmir 35320, Turkey; [EMAIL_ADDRESS]
# A Photonically-Excited Leaky-Wave Antenna Array at E-Band for 1-D Beam Steering
## 4 Measurements and Results  
Two different sets of measurements were carried out to evaluate the performance of the designed antenna: electronic measurements to determine return loss and radiation pattern, and photonic measurements to examine the power radiated and its performance in a wireless transmission link.
# A Novel Impact Rotary-Linear Motor Based on Decomposed Screw-Type Motion of Piezoelectric Actuator
## 4 Experimental Tests and Discussions  
### Dynamic Performance of the PA  
The finite element method (FEM) is used to extract the torsional and longitudinal vibration modes of the PA with free boundary condition, and the results are shown in Figure 4. The torsional and longitudinal vibration modes occur at 53.99 kHz and 89.86 kHz, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Parameters** & **Value** & **Unit** \\ \hline Piezoelectric tube: PZT (YT-SL, & Piezoelectric coefficient (\(d_{31}\)) & \(-195\) & pC \(\times\) N\({}^{-1}\) \\ Baoding Yitian Ultrasonic & Piezoelectric coefficient (\(d_{33}\)) & 450 & pC \(\times\) N\({}^{-1}\) \\ Technology co., LTD) & Density & 7600 & kg \(\times\) m\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of the PA.  
Figure 3: The structure of the piezoelectric motor: (a) schematic diagram of the piezoelectric motor; (b) photograph of the fabricated prototype motor.  
The dynamic response is measured by LCR-8105G analyzer (Good will Instrument Company Ltd., Taiwan), which is shown in Figure 5. The torsional and longitudinal vibration modes occur at 54.34 kHz and 89.07 kHz, respectively. The experimental results are close to the simulation results. The experimental results of the dynamic performance indicate that the prototype PA has the screw-type motion ability.
# Simulation of Lid-Driven Cavity Flow with Internal Circular Obstacles
## 2 Numerical Method
### Boundary Conditions  
The simulation of the lid-driven cavity flow has been used as a benchmark for developing the LBM because of its simple shape as well as its extensive application for analyzing complicated flows inside a cavity. This study is aimed at understanding the flow inside a cavity such that it would become important to understand how to implement boundary conditions inside the cavity. No-slip boundary conditions were applied on both the sidewalls and the bottom of the cavity, as well as the obstacle surface, whereas a uniform horizontal velocity (i.e., \(U=0.1\)) was applied at the lid of the cavity, as shown in Figure 3. In Figure 3, the size of each wall is set to the length \(L\) (i.e., \(L=1\)). In order to understand the main circulation of the flow inside the cavity, a large circle is used to indicate the primary flow pattern in the cavity.  
Figure 3: Schematic of lid-driven cavity flow.  
Figure 2: Schematic of bounce-back boundary condition.  
In the case of the flow inside a cavity with an internal obstacle, Figure 4 shows the schematic of lid-driven cavity flow around that internal obstacle. The figure on the left also shows the primary flow pattern indicating the lid-driven flow, as well as a circulating flow around the obstacle. In order to observe the effect of different internal obstacle sizes, the radii of the internal obstacles were varied as \(R/L=1/16\), \(1/6\), \(1/4\), and \(2/5\).
## 3 Results
### Bayesian Network Construction  
The results of the bootstrap are illustrated in Table 2, showing nine connections between features and the outcome. They include \"gad6\" (Becoming easily annoyed or irritable), \"gad2\" (Not being able to stop or control worrying), \"Depression group\" (four PHQ-9 score groups: 5-9, 10-14, 15-19, 20 or above), \"gad4\" (Trouble relaxing), \"ucla3\" (How often one feels isolated from others), \"gad1\" (Feeling nervous, anxious, or on edge), \"ucla1\" (Lack of company), \"phq5\" (Poor appetite or overeating), and \"depression\" (Have you been diagnosed with depression).  
These nine connections are all directly connected to the outcome and appeared at least once in the 1000 bootstrap samples used while building the Bayesian Network, as discussed in Section 2.4.  
As explained in Section 2.4, we constructed nine different Bayesian Networks. We began with the first BN, which had two nodes, with a directed arrow from our outcome \"Recurrent anxiety\" to \"gad6\". Next, we added the node \"gad2\" to the \"Recurrent anxiety\" node, maintaining the connection used in the first BN. Then, we incremented from BN number 3 to number 9 by adding nodes according to Table 2. All nine constructed BNs can be seen in Appendix A Figure A1.  
Figure 3 displays the average AUC values from 100 evaluations on repeated cross-validation versus the nine different BNs constructed from the bootstrap. Figure 3 shows that the AUC consistently increases for the training folds. However, for the validation folds, there is a peak when the number of features is four, indicating that the BN with four features performs best. This result suggests it has the highest potential for strong performance and effective generalisation among all nine possible BNs.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 5 Discussion  
### Case Analysis and Interpretations  
Table 4 shows a comparison of the demodulating capabilities of the LLM-SC and DeepSC over Rayleigh fading channels. At a 6 dB SNR, DeepSC can demodulate some words, realizing a 1-gram BLEU score of 0.54. However, it is challenging to extract useful information from the received content, while its BER is as high as 0.39. In contrast, LLM-SC achieves a 1-gram BLEU score of 0.94 at 6 dB SNR, allowing us to obtain most of the intended semantics from the transmitted sentence, with a BER of only 0.026. At 12 dB SNR, DeepSC reaches a BLEU score of 0.72, enabling partial understanding of the transmitted information, though accurately grasping the semantics remains difficult, with a BER of 0.32. Comparatively, LLM-SC achieves error-free transmission at 12 dB SNR, allowing complete recovery of the transmitted sentence, with optimal BLEU and BER performance. Additionally, at 3 dB SNR, LLM-SC still achieves a BLEU score of 0.86, enabling us to understand most of the critical information from the received data. Of course, the sampling selected a commonly used English sentence with strong context relevance. The results of the average performance are shown in Figure 2.  
A deeper analysis reveals that LLM-SC concentrates errors on a limited number of tokens, allowing for the demodulation of the majority of tokens and thus enabling a high level of information understanding. In contrast, DeepSC prioritizes improving BLEU scores by considering entire sentences. While it correctly demodulates many words, resulting in a higher BLEU score, semantic comprehension remains imperfect. This underscores the limitations of using BLEU as a metric for semantic communication. Importantly, LLM-SC achieves a harmonious integration of technical and semantic communication, with metrics consistently evaluating both aspects. Unlike DeepSC, which excels semantically in BLEU but struggles technically with BER, and unlike traditional technical communication, which excels BER but falls short semantically, LLM-SC effectively balances both dimensions.  
In essence, leveraging mutual information between symbols and meanings, LLM-SC reliably transmits semantic content despite noise. Rather than solely maximizing  
Figure 5: Performance of LLM-SC with different beam search widths.  
symbolic fidelity, the system preserves information at higher linguistic levels--the essence of effective communication.
# Deflection Modeling and Curvature Manipulation of a Variable-Stiffness Flexible Needle
## 3 Results
### Effect of Needle Stiffness on Puncture Trajectory
It should be noted that we were unable to compare the experimental deflection with the theoretical deflection for the following reasons: (1) We used the Timoshenko beam theory for static structural analysis, whereas the piercing process is dynamic and the bending deflection is influenced by the insertion depth, which is not included in our model and may require further kinetic analysis. (2) We considered the tip length \(L\) shown in Figure 1 for the effect on needle deflection, but the tip length is related to structural parameters such as the diameters of the tube and the stylet. The effective needle length is difficult to determine and requires more in-depth mechanical analysis. (3) Figure 10 demonstrates that cutting resistance influences bending deflection based on piercing results of gelatin with varying mass fractions. However, we are currently unable to directly measure the cutting force at the needle tip, which renders the deflection model incapable of providing predictions. Nevertheless, the experimental results preliminarily confirm that controlling the length of the stylet extending out of the tube can alter the stiffness of the needle, allowing for manipulation of the puncture trajectory. Figure 10: Deflection fitting of the stiffness-tunable flexible needles.
# Recognition of Eye-Written Characters Using Deep Neural Network
## 2 Materials and Methods
### Evaluation  
The proposed method was evaluated in a writer-independent manner using a one-subject leave-out validation approach. This enabled us to maximize the amount of training data while completely separating the test data from the training data. We used the characters written by 17 writers as training data and those of the remaining one writer as the test data. This was repeated 18 times such that all the writers' characters were used as the test data once.  
The method was evaluated with recognition accuracies, precision, recall, and F1 score, which were calculated as follows:  
\[\text{Accuracy}=\text{TP}/\text{P}, \tag{3}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}), \tag{4}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}), \tag{5}\]  
\[\text{F1 score}=2\text{TP}/(2\text{TP}+\text{FP}+\text{FN}), \tag{6}\]  
where P denotes the number of total characters, and TP (true positives) denotes the number of correctly classified characters. The accuracy was utilized to evaluate overall classification performance, and the other metrics were utilized to evaluate classification performance for each character. FP (false positives) and FN (false negatives) were calculated for each target letter group. FP was the number of characters in the other letter groups which were classified as the target letter, and FN was the number of the characters in the target group which were classified as other letter groups by the trained network.  
The Adam optimizer was used with the AMSGrad option [26], and the learning rate was set to 0.001. The training was repeated for over 200 epochs with a batch size of 128. The learning rate and number of epochs were derived experimentally using data from all the writers. This does not mean that we optimized the parameters, but we found that the accuracy was stable with the parameters after a number of trials.