esa-sceva/satcom-chunk-collection huggingface dataset:
# Reactive Balance Control for Legged Robots under Visco-Elastic Contacts
## 6 Results
### Test Description  
Our tests aim to evaluate the ability of each controller to balance the robot on viscoelastic contacts. To do so, we start the simulation with a non-zero CoM velocity, and we observe whether and how each controller can decelerate the CoM and bring it back to its initial position.  
We start by performing three tests, each of which focuses on one specific controller. In our first test (Test A) we show how TSID-Rigid struggles with soft contacts. Then, in Test B, we examine TSID-Flex-K and TSID-Adm, which work well with soft contacts, but get unstable for stiff contacts. Test C shows that Adm-Ctrl remains stable in both cases. Finally, in Test D, we show a comprehensive comparison of all controllers in different situations.  
We have tested the controllers both in an ideal simulation (no noise, modeling errors, and perfect state estimation) and in more realistic conditions by introducing:  
* realistic encoder quantization errors and white Gaussian noise on force sensing and gyroscope (see Table 3);
* an Extended Kalman Filter (explained in Appendix B) to estimate the robot state, with the covariances specified in Table 4;
* limited torque bandwidth by filtering the desired joint torques with a first-order low-pass filter with a cut frequency of 30 Hz. The best torque-tracking bandwidths that have been reported for high-performance actuators are between 40 Hz and 60 Hz (e.g., 40 Hz for hydraulic actuators [2], 46 Hz for electric motors with harmonic drives [21], 60 Hz for series elastic actuators [22]).
* joint Coulomb friction of about 1% of the maximum joint force/torque (0.4 Nm for hip joints, and 4 N for knee joints, which are prismatic).  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Symbol** & **Controller** & **Meaning** & **Value** \\ \hline \(w_{post}\) & TSID-Flex-K & Postural task weight & 0.3 \\ \(w_{post}\) & TSID-Adm & Postural task weight & \(1\times 10^{-3}\) \\ \(w_{post}\) & Adm-Ctrl & Postural task weight & \(1\times 10^{-3}\) \\ \(w_{post}\) & TSID-Rigid & Postural task weight & \(1\times 10^{-2}\) \\ \(w_{f}\) & TSID-Rigid & Force regularization weight & \(1\times 10^{-4}\) \\ \(K^{m}_{p}\) & Adm-Ctrl & Proportional momentum gain & 30.7 \\ \(K^{m}_{d}\) & Adm-Ctrl & Derivative momentum gain & 10.3 \\ \(K^{p}_{p}\) & Adm-Ctrl & Proportional joint position gain & \(10^{4}\) diag\((M_{0})\) \\ \(K^{l}_{d}\) & Adm-Ctrl & Derivative joint position gain & \(200\) diag\((M_{0})\) \\ \(K_{f}\) & Adm-Ctrl & Proportional force gain & \(8\times 10^{-3}\) \\ \(K^{post}_{p}\) & All & Proportional posture gain & 10 \\ \(K^{post}_{d}\) & All & Derivative posture gain & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Controller Parameters. diag\((M_{0})\) is the diagonal part of the mass matrix evaluated at \(q(0)\).  
In the following we will refer to the first kind of simulations as _noiseless_, and to the second kind as _noisy_.
## 2 Methods
### Description of the Problem
#### 2.1.1 Study on Grid Dependence and Parameter Sensitivity Analysis  
Given the complexity of the model and to ensure the correctness of solutions, a mesh sensitivity test at various Reynold numbers was conducted at various mass design points. The test evaluated the impact of the number of mesh elements in a test grid on the \(Nu\) and \(Q\), as can be seen in Figures 1 and 2. It was found that the average percent coefficient of variation between the values of \(Nu\) and \(Q\) are 0.058% and 0.209%, respectively, indicating that the model has reached mesh independence. Accordingly, Mesh A was employed in this study for simple and optimized calculation [21].  
A sensitivity test on model dimensions was also done for different inner and outer diameters, as seen in Figure 3. As can be seen, the %CV for LMTD at three different Reynolds numbers is less than 5%, still indicative that the model can be utilized at different tube dimensions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **Water** & **Toluene** & **Copper** & **Steel** \\ \hline Density (kg/m\({}^{3}\)) & 998.2 & 866 & - & - \\ Viscosity (kg/m-s) & 0.001003 & 0.000586 & - & - \\ Specific heat (J/kg-K) & 4182 & 1675 & - & - \\ Thermal Conductivity (W/m-K) & 0.6 & 0.151 & 387.6 & 16.27 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fluid and solid properties derived from ANSYS Fluent 2021 R2 database.  
The fluid type and solid material were also varied to test the sensitivity of the baseline model to changing fluid and solid conditions. Here, the impact was tested at various  
Figure 1: Impact of mesh variations on \(Nu\) output.  
Figure 3: Impact of pipe diameter variations on \(LMTD\) output.  
Figure 2: Impact of mesh variations on \(Q\) output.  
Relative root mean square error (RRMSE), expressed as Equation (2), was used as an indicator in model validation, whereas model agreement was considered excellent when RRMSE \(<\) 10%, good when 10% \(<\) RRMSE \(<\) 20%, fair if 20% \(<\) RRMSE \(<\) 30%, and poor if RRMSE \(>\) 30% [22].  
\[RRMSE=\frac{\sqrt{\frac{1}{x}\sum_{i=1}^{m}(D_{m}-D_{c})^{2}}}{\overline{O_{m} }}, \tag{2}\]  
where \(x\) is the number of data sets, \(D_{m}\) and \(D_{c}\) refer to the model data and calculated data, respectively, and \(\overline{O_{m}}\) is the mean model data.
# Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
## 4 Proposed Methodology
### Features Extraction
#### 4.2.2 RASTA-PLP  
The majority of noise reduction methods used in voice recognition systems rely on applying various filtering strategies across a range of frequency frames [28]. Similar to the RASTA-PLP feature extraction methodology, several algorithms are based on frames and high-pass or band filtering methods. This method makes use of the RASTA filter. This filter is a band-pass filter used to eliminate frames and background noise that change slowly. The steps listed below can be used to obtain RASTA-PLP:  
1. First, audio signals are divided into frames.
2. Then, the logarithm of the short-time critical-band spectrum is determined. The transformation of the bark frequency \(\Omega\) from the angular frequency \(\omega\) is computed as shown in Equation (7): \[\Omega(\omega)=6 ln\bigg{\{}\omega/1200\pi+\bigg{[}\big{(}\omega/1200\pi\big{)} ^{2}+1\bigg{]}^{0.5}\bigg{\}}\] (7)
3. Next, we take the regression line-based temporal derivative of the aforementioned spectrum.
4. Then, an IIR system is utilized to perform temporal derivative filtering on the log-critical band.
5. In order to imitate human hearing, an equal loudness curve is added.
6. The inverse logarithm of this log spectrum should be determined after obtaining the relative auditory spectra.
7. After all of the spectral pole models have been calculated, the PLP requirements are satisfied.
# Topology-Aware Retinal Artery-Vein Classification via Deep Vascular Connectivity Prediction
## 2 Methods
### Vessel Topology Estimation  
#### 2.3.1 Vascular Connectivity Prediction  
The output of the connectivity classification network in Figure 3c is a probability, between two vessel pixels, of being physically connected. More specifically, the probability \(p^{comn}(v_{jm},v_{kn};W)\) is defined between a pixel \(v_{jm}\) on the \(j\)-th segment and another pixel \(v_{kn}\) on the \(k\)-th segment. \(W\) represents the learned network parameters. We note that the connectivity classification is carried on top of convolutional vascular shape features, which enables a batch processing given a bunch of pixel pairs.  
The connectivity probability between the \(j\)-th and the \(k\)-th vessel segments is then computed by averaging the probabilities of pixel pairs, and is defined by:  
\[p^{comn}_{jk}=p^{comn}(v_{j},v_{k})=\frac{1}{|S_{jk}|}\sum_{(m,n)\in S_{jk}}p ^{comn}(v_{jm},v_{kn};W), \tag{4}\]  
Figure 4: An example of (**a**) binary vessel segmentation and (**b**) AV classification on arteries (red) and veins (blue) that are parallel and touching each other.  
where \(S_{jk}\) is a set of all possible pixel pairs within a certain distance range from the junction. By repeating this computation for all segment pairs defined in \(E\), we finally acquire a set of the connectivity probabilities of all segment pairs, which is \(P^{comn}=\left\{p_{jk}^{comn}|(j,k)\in E\right\}\).
## 7 Proposed Extensions to Bio-2 FA-IoD
### Enhanced Session Key Agreement with Diffie-Hellman for Perfect Forward Secrecy  
The current Bio-2 FA-IoD protocol's session key KS is derived from the operator's long-term local password \(P_{ID_{O}}\) and a timestamp, which provides limited perfect forward secrecy (PFS). To achieve stronger PFS, we propose integrating an ephemeral Diffie-Hellman (DH) key exchange during the authentication phase. This would ensure that the compromise of long-term secrets (\(P_{ID_{O}}\) or \(RPMD_{ID_{O}}\)) does not compromise past session keys.
# Flexural Strength and Stiffness of Donut-Type Voided Slab
## 1 Introduction
### Research Scope and Objectives  
In this study, an analytical and experimental study were conducted to derive the optimal void shape and details focused on the flexural capacities of voided slabs. In particular, this study considered the hole of void shaper as an influence variable and focused on investigating the effect of that on the flexural capacity of the voided slab. Based on the finite element (FE) analysis, a donut-type void shaper, which was a hexahedron with rounded edges and a hole penetrating the center, was suggested as the optimal shape in  
Figure 1: The concept of voided slab system and their application (BubbleDeck) [1]. voided slabs, and experimental study was conducted to verify flexural capacities of the donut-type voided slab, focusing on the influence of void shape and fixing method of void shaper. The results of voided slabs were comprehensively compared to those of a solid slab specimen in five terms: failure behavior, flexural strength, deflection, stiffness and ductility. Based on the test results, a flexural design method of donut-type voided slab associated with void shape and fixing device of void shaper was suggested, and it was confirmed that the donut-type voided slab is one of the efficient alternatives to replace heavy flat plate slab.
# Effects of Magnetic Stimulation on Dental Implant Osseointegration: A Scoping Review
## 2 Materials and Methods
### Study Selection  
We selected preclinical in vitro and in vivo studies evaluating the effects of MFs on bone cells cultured in contact with Titanium (Ti) surfaces and their effects on Ti implant osseointegration in animal models. We also selected:  
* Interventional studies (either randomized or non-randomized controlled clinical trials);
* Observational studies (either analytical or descriptive);
* Case series or Case reports regarding the effects of MFs on osseointegration of dental implants were selected.  
Review articles, conference abstracts, editorials and studies regarding the effects of other physical stimulations on osseointegration were disregarded. Disagreements between the two reviewers were resolved via discussion. After title and abstract analysis, a careful evaluation of all full texts for the eligibility criteria (inclusion/exclusion) was performed. [PERSON]'s kappa coefficient was used to determine inter-rater agreement.  
Three independent reviewers extracted and qualitatively analyzed results and findings from each included study, using an ad-hoc data charting form designed for the present scoping review. The forms report for each study the relevant information about the type of  
\begin{table}
\begin{tabular}{l} \hline \hline Electronic databases: \\ \(-\) & MEDLINE/PubMed; \\ \(-\) & Embase; \\ \(-\) & Scopus; \\ \(-\) & Web of science; \\ \(-\) & The Cochrane Library; \\ \(-\) & clinicaltrials.gov; \\ \(-\) & IEEE Digital Library. \\ \hline \hline Filters: English language \\ \hline \hline \end{tabular} \
\begin{tabular}{l} Inclusion criteria: \\ \(-\) & Interventional, observational studies evaluating the effects of MFs on dental implant osseointegration; \\ \(-\) & Preclinical in vivo studies evaluating MF effects on osseointegration of Ti implants; \\ \(-\) & In vitro studies evaluating the effects of MFs on bone cells cultured in contact with Ti surfaces; \\ \(-\) & No restriction on follow-up duration, number of patients, population characteristics, age or systemic conditions; \\ \hline \hline Exclusion criteria: \\ \(-\) & Review articles; \\ \(-\) & Conference abstracts and editorials; \\ \(-\) & Studies regarding other kinds of physical stimulations. \\ \hline \hline \end{tabular}
\begin{tabular}{l} Additional sources: \\ \(-\) & Hand search of the major international journals of implantabley; \\ \(-\) & Reference lists of all selected studies. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Search strategy.  
study (in vitro, in vivo, clinical), the details of the stimulation used (SMF, PEMF), the study groups, the duration of the follow-up and the key study findings. Furthermore, the quality of the included clinical studies was assessed using the \"Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) Tool\" [29] and the \"Cochrane Risk of Bias Tool\" for randomized clinical trials (RoB 2) [30].
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 5 Numerical Simulation
### Simulation Setup
#### 5.1.2 User Task Parameter  
The user task distribution of each satellite is not balanced. There are many reasons that affect the user task distribution on the satellite. The first is geographical. Specifically, the majority of the population lives and produces on land, so when the satellite covers the ocean, the number of users is very small, and when the satellite passes over land, the number of users' tasks is increased. The second is the economic factor. Users in economically developed areas will have more tasks, because there are more netizens and more demands for computing and offloading. Third, due to the relative movement of the low-orbit constellation, the ground area connected to the same satellite will change, leading to changes in the distribution of user tasks on the satellite. For simplicity, we assume that the user tasks are mainly concentrated on satellites over the land, and the tasks are evenly offloaded to the current satellite nodes covering the land. As shown in Figures 5-7[16], satellite red indicates high user task density. The amount of data \(D_{i}\) required to offload for each user task conforms to the normal distribution \(\mathcal{N}(10,2)\) Mbytes, and the computing resources \(C_{i}\) required conform to the normal distribution \(\mathcal{N}(1,0.2)\) Gcycles.
## 3 Results
### Sensitive Wavelengths of Soil Moisture  
The first-order sensitivity index of input parameters to soil reflectance in soil radiative transfer model was calculated by Equation (6), wavelength by wavelength, in the wavelength range of 450-2500 nm. The variation of the first-order sensitivity index with wavelength is shown in Figure 5a,b is an enlarged detail of Figure 5a. The sensitive wavelength ranges of soil moisture content are 853-1125 nm and 1900-1988 nm, and the influence of soil moisture in this range is significantly stronger than the other parameter.  
Next, the SPA was used to further select sensitive wavelengths. The results are shown in Table 3. The RMSEP of soil moisture content at the selected five wavelengths (1010 nm, 1068 nm, 1915 nm, 1946 nm and 1988 nm) is between 10.4 g kg\({}^{-1}\) and 12.1 g kg\({}^{-1}\), indicating that the selected five wavelengths have excellent performance, especially 1915 nm. In addition, the estimation accuracy obtained through the SA-SPA (RMSEP < 12.1 g kg\({}^{-1}\)) is generally superior to that from full spectrum data (RMSEP < 14 g kg\({}^{-1}\)) [20].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Number**} & **Maximum** & **Minimum** & **Average** & **Standard** \\  & & **(g kg\({}^{-1}\))** & **(g kg\({}^{-1}\))** & **(g kg\({}^{-1}\))** & **Deviation (\%)** \\ \hline Total set & 15 & 240 & 0 & 142.1 & 66.2 \\ Calibration set & 10 & 240 & 0 & 146.0 & 70.1 \\ Validation set & 5 & 200 & 6 & 132.5 & 64.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical description of soil moisture contents for black soil.  
Figure 4: The spectral curves at different SNRs. (**a**) 50. (**b**) 100. (**c**) 200. (**d**) 1000.  
In order to further verify the effectiveness of the SA-SPA method, this study compared and analyzed the performance of wavelengths selected by SA-SPA, ICO-SPA, MWSE-CARS-SPA and MWSE-GA-SPA. The results are shown in Table 4. The comparison results show that there is no significant difference in the number of wavelengths selected by the four methods, and the number of wavelengths selected are 5, 2, 8 and 8 respectively. The RMSEP of SA-SPA method is 12.1 g kg\({}^{-1}\), that of ICO-SPA method is 13.1 g kg\({}^{-1}\), that of MWSE-CARS-SPA method is 12.7 g kg\({}^{-1}\), and that of MWSE-GA-SPA method is 12.7 g kg\({}^{-1}\). Therefore, the performance of the wavelength selected by SA-SPA method is better than the other three methods.
## 4 Experimental Results
### Experimental Study 2
#### 4.2.1 Results Obtained Using the Custom-Built CNN Model  
In this study, a custom-built CNN model was employed to classify breast cancer images. The model architecture accepts the images with 224 \(\times\) 224 pixels and RGB input. The developed model was compiled using the BCE loss function with the Adam optimizer and trained with a batch size of 32 samples, and a learning rate of 0.001 over 50 epochs. To avoid overfitting, an early stopping strategy was used to automatically stop training if no validation error improvement was seen for 10 successive epochs. The model trained over 50 epochs achieved an accuracy of 92.86%, precision of 90%, recall of 56.25%, and F1 score of 69.23%. Furthermore, the proposed model reached an accuracy rate of 87.50% on the validation dataset. Figure 9 highlights the trend of the accuracy and loss graph of the model.
# Novel Real-Time Acoustic Power Estimation for Dynamic Thermoacoustic Control
###### Abstract  
This paper presents a new procedure for the real-time processing and analysis of data from thermoacoustic systems. The approach focuses on continuously acquiring and adjusting measurements of acoustic wave pressure, enabling the instantaneous estimation of acoustic power. This is crucial for real-time control and decision-making, especially in applications that require rapid power estimation, such as the control loop implementation in thermoacoustic engines, where conditions are constantly changing and dynamic adaptation is essential. Two methods for estimating the power delivered to the load are proposed: (method 1) instantaneous power evaluation, which calculates the power consumed by the resistance in the resistance-capacitance (RC) load, and (method 2) one-period average power calculation using the well-established two-microphones method. These methods are validated with both different synthetic signals and experimental measurements. The results reveal that the new method provides real-time accurate estimations of the power delivered to the acoustic load and, thus, has shown potential for control-based applications.  
data processing; acoustic power; instantaneous versus average estimation; thermoacoustic engines +
Footnote †: journal: Applied sciences
# Research on Short-Term Traffic Flow Combination Prediction Based on CEEMDAN and Machine Learning
## 4 Combined Model Construction and Case Analysis Based on CEEMDAN and Machine Learning
### Short-Term Traffic Flow Combination Model Based on CEEMDAN  
The core idea of the short-term traffic flow prediction model based on CEEMDAN is \"primitive traffic flow--decomposition--traffic flow component sequence classification--establishment of component sequence prediction model--superposition prediction result\". Firstly, the traffic flow time series is decomposed into multiple component sequences withCEEMDAN, and then by calculating the permutation entropy of each component sequence, the component sequence is divided into high-frequency, medium-frequency, and low-frequency component sequences according to the size of the permutation entropy. Then, three different component prediction models are constructed according to the fluctuation characteristics and frequency characteristics of the three component sequences. Finally, the prediction results of each model can be superimposed to obtain accurate traffic flow prediction results.
# Deep Learning within a DICOM WSI Viewer for Histopathology
###### Abstract  
Microscopy scanners and artificial intelligence (AI) techniques have facilitated remarkable advancements in biomedicine. Incorporating these advancements into clinical practice is, however, hampered by the variety of digital file formats used, which poses a significant challenge for data processing. Open-source and commercial software solutions have attempted to address proprietary formats, but they fall short of providing comprehensive access to vital clinical information beyond image pixel data. The proliferation of competing proprietary formats makes the lack of interoperability even worse. DICOM stands out as a standard that transcends internal image formats via metadata-driven image exchange in this context. DICOM defines imaging workflow information objects for images, patients' studies, reports, etc. DICOM promises standards-based pathology imaging, but its clinical use is limited. No FDA-approved digital pathology system natively generates DICOM, and only one high-performance whole slide images (WSI) device has been approved for diagnostic use in Asia and Europe. In a recent series of Digital Pathology Connectathons, the interoperability of our solution was demonstrated by integrating DICOM digital pathology imaging, i.e., WSI, into PACs and enabling their visualisation. However, no system that incorporates state-of-the-art AI methods and directly applies them to DICOM images has been presented. In this paper, we present the first web viewer system that employs WSI DICOM images and AI models. This approach aims to bridge the gap by integrating AI methods with DICOM images in a seamless manner, marking a significant step towards more effective CAD WSI processing tasks. Within this innovative framework, convolutional neural networks, including well-known architectures such as AlexNet and VGG, have been successfully integrated and evaluated.  
DICOM WSI; artificial intelligence on DICOM WSI; digital pathology; DICOM web viewer; CAD in histopathology +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Adsorption Properties
#### 3.2.3 Effect of Temperature  
We investigated the effect of temperature on the adsorption capacity of the modified zeolite at different temperatures, at an optimum pH, with different initial metal-ion concentrations (Figure 7). The change in temperature during the absorption process may have been affected by the interaction between the adsorbent and Cu(II), the stability of the  
Figure 5: Effect of pH on the adsorption capacity (\(C_{0}\) = 100 mg/L, \(T\) = 25 \({}^{\circ}\)C, \(t\) = 8 h, \(n\) = 100 rpm).  
Figure 6: Effects of initial concentrations and time on the adsorption capacity (pH = 5, \(T\) = 25 \({}^{\circ}\)C, \(n\) = 100 rpm).  
complex formed by the Cu(II), and the functional group of the modified zeolite and pore walls of the modified zeolite.  
As shown in Figure 7, the adsorption capacity was increased with an increase in temperature from 25 \({}^{\circ}\)C to 45 \({}^{\circ}\)C, which is further evidence of an endothermic reaction. When the temperature increased from 45 \({}^{\circ}\)C to 55 \({}^{\circ}\)C, the adsorption capacity decreased slightly in some cases.