esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigates the dynamic properties of the human middle ear and the energy transfer phenomena between the stapes footplate (SF) and the round window membrane (RWM) under sound stimulation. A series of laboratory tests were conducted, and a numerical model of the system was prepared. During the experiments, vibrations in human temporal bones were recorded using a Laser Doppler Vibrometer (LDV), and the frequency response functions (FRFs) of the RWM and SF footplate were computed. Key resonances were identified, with notable differences in vibration amplitude depending on whether the artificial external ear canal was left open or closed. To evaluate the amplification of acoustic waves within the cochlea, the authors proposed a novel index defined as the ratio of the FRF of the RWM and SF, respectively. The performed computations showed that signal amplification is particularly noticeable in the frequency range from 1 to 2 kHz. Subsequently, a simplified computational fluid dynamics (CFD) model of the cochlea was developed to simulate internal pressure distribution within the scala vestibuli (SV) and scala tympani (ST) spaces. The numerical computations of acoustic signal amplification showed good agreement with the experimental data, particularly at the frequencies of 1 and 2 kHz. These findings provide new insights into cochlear acoustics and offer a potential tool for evaluating pathological disorders and designing prosthetic devices.  
human inner ear; round window vibration; cochlea; energy transfer phenomenon +
Footnote †: journal: Applied sciences
## 3 Results
### Biomarkers Analysis
#### 3.3.10 Platelet Aggregation Factor (PAF)  
The estimated marginal means of platelet aggregation factor (PAF) across the four time points (baseline [1], 1 h [2], 2 h [3], and 4 h [4]) are summarized in Table 13 and illustrated in Figure 11. Baseline PAF levels were comparable across the three groups: CTRL (0.916 \(\pm\) 0.064), OLT (0.996 \(\pm\) 0.047), and OLB (0.916 \(\pm\) 0.059).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **TAC** & **TAC** & **TAC** & **TAC** & \multicolumn{2}{c}{**95\% Confidence Interval**} & \multicolumn{1}{c}{**\(p\) Value**} \\  & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **(Mean \(\pm\) SE)** & **Time** & **Lower** & **Upper** & **(Group \(\times\) Time)** \\  & **at Baseline (t1)** & **at 1 h (t2)** & **at 2 h (t3)** & **at 4 h (t4)** & **Time** & **Bound** & **Bound** & **Upper** & **(Group \(\times\) Time)** \\ \hline CTRL & 0.370 \(\pm\) 0.017 & 0.377 \(\pm\) 0.018 & 0.370 \(\pm\) 0.016 & 0.344 \(\pm\) 0.017 & t1 & 0.335 & 0.405 & \\  & & & & t2 & 0.340 & 0.413 & \\  & & & & t3 & 0.337 & 0.403 & \\  & & & & & t4 & 0.310 & 0.379 & \\ \hline  & & & & & t1 & 0.338 & 0.398 & \\ OLT & 0.368 \(\pm\) 0.015 & 0.381 \(\pm\) 0.015 & 0.371 \(\pm\) 0.014 & 0.358 \(\pm\) 0.016 & t2 & 0.350 & 0.412 & \\  & & & & t3 & 0.343 & 0.399 & \\  & & & & & t4 & 0.325 & 0.391 & \\ \hline  & & & & t1 & 0.337 & 0.388 & \\  & & & & & t2 & 0.349 & 0.400 & \\  & & & & t3 & 0.339 & 0.397 & \\  & & & & t4 & 0.321 & 0.387 & \\ \hline  & & & & & & \\ \end{tabular}
\end{table}
Table 12: Postprandial changes in plasma total antioxidant capacity (TAC) levels (mg/L).  
Figure 10: Postprandial TAC concentrations (Mean \(\pm\) SE) over time (Baseline, 1 h, 2 h, 4 h) in control (CTRL), olive leaf tea (OLT), and olive leaf biscuit (OLB) groups.  
Over the postprandial period, no statistically significant group-by-time interaction was observed (_p_ = 0.713), indicating that the evolution of PAF values over time did not significantly differ between the three groups.  
Specifically, in the CTRL group, PAF levels showed small positive values at t2, t3, and t4 (0.316 \(\pm\) 0.288, 0.099 \(\pm\) 0.124, and 0.231 \(\pm\) 0.178, respectively), while the OLT and OLB groups exhibited negative or near-zero values at corresponding time points. In the OLT group, PAF levels at t3 and t4 were \(-\)0.086 \(\pm\) 0.073 and \(-\)0.024 \(\pm\) 0.066, respectively; similarly, in the OLB group, PAF was \(-\)0.040 \(\pm\) 0.200 at t3, although a slight rebound occurred at t4 (0.138 \(\pm\) 0.179).  
Although not statistically significant, these negative differences showed decreased PAF levels in the OLT and OLB groups at later time points. However, the 95% confidence intervals for these changes included zero in all cases (e.g., OLT t3: [\(-\)0.238, 0.065]; OLB t3: [\(-\)0.454, 0.374]), indicating a lack of robust evidence for a true decrease.  
Overall, PAF levels remained relatively stable throughout the study period, with no significant group-by-time interaction observed.
## 8 Conclusion  
Based on a new dynamic modeling theory, a novel dynamic control method is proposed for the free-floating dual-arm space robot. This method can not only realize the tracking of the desired trajectory by the two arms of the space robot, but also realize the stable control of the space robot base. The in-orbit service mission of space debris capture was designed and simulated. The main contributions of this paper are as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Pseudo-Inertia** & **Generalized** & **Explicit** \\ \multicolumn{1}{c}{**Numbers**} & \multicolumn{2}{c}{**Matrix**} & \multicolumn{1}{c}{**Momentum**} & \multicolumn{1}{c}{**Canonical Method**} \\ \cline{3-5}  & & **Method** & **Method** & **Canonical Method** \\ \hline Inertia & Multiplication & \(21n^{3}+63n^{2}+66n\) & \(7n^{3}+12n^{2}+3n\) & \(3.5n^{3}+16n^{2}+12.5n\) \\ \cline{2-5} matrix & Addition & \(18n^{3}+49.5n^{2}+43.5n\) & \(5n^{3}+10n^{2}+2n\) & \(3.5n^{3}+10n^{2}+6.5n\) \\ \hline Bias force & Multiplication & \(91n^{2}+157n\) & - & \(16.5n^{2}+43.5n\) \\ \cline{2-5} vector & Addition & \(67n^{2}+105n\) & - & \(7.5n^{2}+27.5n\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of computational complexity.  
1. Based on a new explicit multi-rigid-body dynamic modeling method and employing the concept of DEM, the dynamic model of a free-floating dual-arm space robot is established. The modeling process of the model is simple and standardized, which is convenient for computer programming.
2. After introducing the control idea of the sliding mode variable structure into the traditional NMPC method, a novel NMPC controller is designed, which effectively solves the problem of tracking the desired trajectory and stabilizing the base attitude of the free-floating dual-arm space robot under unknown model uncertainty and external disturbance.  
This article focuses on the challenging problem of dynamic modeling and control of free-floating space robots. The proposed new dynamic modeling and control method can be effectively applied to the on-orbit service tasks of free-floating dual-arm space robots, providing new insights into the challenging problems regarding the dynamics and control of complex multi-DOF space robots. In order to fully verify the method proposed in this paper, a prototype platform of a free-floating dual-arm space robot will be built in the future, and the development and test of hardware system will be carried out. At the same time, in the future, factors such as link flexibility, joint friction and contact collision will be considered to study the dynamic modeling, control and planning of multi-arm space robots.  
Methodology, Z.G. and C.L.; software, Z.G.; writing--original draft preparation, Z.G.; writing--review and editing, Z.G., H.J. and K.W. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China (61673010). The authors will supply the relevant data in response to reasonable requests. The authors have no relevant financial or non-financial interests to disclose.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 6 Results
### Ebro River--Zaragoza, Spain 2021  
The CEMS system was activated for this event on 10 December 2021 [64]. The flood delineation map generated by analyzing RADARSAT 3 data acquired on 15 December 2021 was considered as the reference data for this case study. The flood map was generated by CEMS using a semi-automatic approach, with an estimated geometric accuracy (RMSE) of 20 m [64]. This reference map was compared with the SAR-RST-FLOOD map computed by investigating the Sentinel 1 image acquired on 16 December 2021.  
UAf exceeded 0.97, indicating accurate flood extent mapping; the PAf value was 0.87 and K was 0.87 (Table 7). A high level of agreement was found between the two maps (Figure 8) despite the high level of vegetation surrounding the river.  
Figure 8: Flood map of the Ebro River (Zaragoza) event (16 December 2021).  
Figure 7: Flood map of the Ebro River (Tudela) event (13 April 2018).
# A Systematic Review of Radio Frequency Threats in IoMT
## 3 Results and Discussion
### Cyber Impacts and Layered Attacks
#### 3.1.1 Cyber-Attacks Taxonomy  
In cyber-harm, five themes are distinguished: physical damage, theft, destruction, infection, exposure, corruption, performance reduction, pain, death, prosecution, and mistreatment.  
Economic harm (disruptions of operations, sales, customers, growth, profits, extortion, joblessness, and scams).  
Anger, shame, guilt, guilt, worthlessness, reduced satisfaction, and incorrect perceptions are all physical cyber-harms.  
A reputational cyber-harm (leading to damage to public perception, brand damage, customer-corporate damage, and decreased business opportunities).  
As a result of social cyber-harm, dynamic inconsistencies in public opinion are caused, cultural efficacy is disrupted, a negative impact is incurred on communities, and perceptions of organizational behavior are reduced.  
The author proposes to extend their future work on an asset-oriented model for the corporate ecosystem and identifying high-value and low-value assets, and how the critical stakeholders involved in the interest of direct and indirect harm. However, this approach does not provide analytics or tools for advanced prediction or intelligent cybersecurity systems to help corporations understand cyber-harm. Despite not focusing on a specific theme, their approach was sufficiently flexible and highly scalable. The data were sourced from an open-source database. The Vocabulary for Event Recording and Incident Distribution System (VERIS) framework is loaded with open-source cybersecurity key performance indicators (OCKPI) to identify the security incident insights, increase the companies' risk mitigation strategies, and extend that framework for effective incident handling mechanism [28]. This framework provides details related to incident insights. It classifies incidents based on external, internal, and partner-based threats. It also provides insights into hacking evidence, including IoT forensics, malware behavior, social engineering attacks, privilege misuse, known and unintentional errors, and how confidentiality, integrity, and availability are affected through critical metrics.  
The key metrics on incidents are classified based on victims (size of the organization), actors, actions, assets, attributes, timelines, impacts, and repeated events. Moreover, this framework provides facilities to understand the efficacy of a business continuity plan through the discovery and response process targeting how the discovery is processed, the root causes, and the corrective actions. How do you differentiate between targeted and opportunistic attack scenarios? This data-driven framework gives greater visibility and reasoning on the key performance indicators. However, the open-source community lacks the credibility of data and future support.
# Tire Bubble Defect Detection Using Incremental Learning
## 3 Experimental Results
### Results Obtained by the Modified YOLOv3 Architecture  
Although the results obtained by the modified YOLOv3 architecture are slightly lower when compared to those obtained by the YOLOv3 SPP architecture, we have observed a slightly improved detection rate relative to the YOLOv3 architecture in both the tread and sidewall defect detections, as seen by comparing Tables 4 and 6. The modified scale prediction network layer improves the detection rates and decreases the number of outputs, since reducing the large-scale feature extraction and strengthening the small-scale feature extraction increase the extracted bubble features. For the tire bubble detection, similarly, the two-output modification is more suitable for verification. The incremental YOLO architecture further increases the detection rate by about 4%.  
### Results Obtained by the Modified YOLOv3 Architecture  
Table 7 shows the experimental results of the proposed architecture. The proposed architecture has achieved higher accuracies than the YOLOv3 SPP architecture in the tire tread and sidewall detections due to the backbone network modifications, where large-scale feature extraction layers were removed, and small-scale layers were strengthened to allow the network to learn detailed defect features. Verifying results using two outputs instead  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Result**} & \multicolumn{2}{c}{**(a)**} & \multicolumn{2}{c}{**(b)**} \\ \cline{2-5}  & **Tread** & **Sidewall** & **Tread** & **Sidewall** \\ \hline TP & 668 & 442 & 736 & 431 \\ TN & 500 & 490 & 499 & 485 \\ FP & 0 & 10 & 1 & 15 \\ FN & 142 & 63 & 74 & 74 \\ \hline Sensitivity & \multicolumn{2}{c}{84.81\%} & \multicolumn{2}{c}{88.75\%} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results Obtained by (a) the Modified YOLOv3 Architecture, (b) the Incremental YOLOv3 Architecture.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Result**} & \multicolumn{2}{c}{**(a)**} & \multicolumn{2}{c}{**(b)**} \\ \cline{2-5}  & **Tread** & **Sidewall** & **Tread** & **Sidewall** \\ \hline TP & 714 & 431 & 770 & 459 \\ TN & 497 & 492 & 489 & 477 \\ FP & 3 & 8 & 11 & 23 \\ FN & 96 & 74 & 40 & 46 \\ \hline Sensitivity & \multicolumn{2}{c}{87.07\%} & \multicolumn{2}{c}{93.46\%} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results Obtained by (a) the YOLOv3 SPP Architecture, (b) the Incremental YOLOv3 SPP Architecture.  
of three outputs also resulted in better accuracy. By adding the SPP structure before the two outputs, the detection rate of the architecture is increased significantly while reducing nearly 40 million parameters. Adding the incremental YOLO architecture further increases the detection rate by about 6%.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
###### Abstract
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Term** & **Full Meaning** & **Term** & **Full Meaning** \\ \hline AML & Adversarial machine learning & AMQP & Advanced message queuing protocol \\ \hline AI & Artificial intelligence & BBU & Baseband unit \\ \hline BLE & Bluetooth low energy & QoS & Quality of service \\ \hline CaaS & Connectivity-as-a-service & CoAP & Constrained application protocol \\ \hline CNN & Convolutional neural network & CPS & Cyber–physical system \\ \hline CTDE & Centralized training distributed execution & CU & Control unit \\ \hline DCS & Distributed control systems & DNN & Deep neural network \\ \hline DoS & Denial of service & DU & Distributed unit \\ \hline FL & Federated learning & emBB & Enhanced mobile broadband \\ \hline eCPRI & Enhanced common public radio interface & GNN & Graph neural network \\ \hline ICT & Information communication technology & ICS & Industrial control system \\ \hline IIoT & Industrial Internet of Things & IP & Internet protocol \\ \hline IoT & Internet of things & IoV & Internet of vehicle \\ \hline LLM & Large language model & LoRaWAN & Long-range wide area network \\ \hline LTE & Long-term evolution & ML & Machine learning \\ \hline MIMO & Multiple-input and multiple-output & MITM & Man in the middle \\ \hline emMTC & Enhanced massive machine type communication & MQTT & Message queuing telemetry transport \\ \hline OT & Operational technology & P2P & Peer-to-peer \\ \hline PMU & Phasor measurement unit & PLC & Programmable logic controller \\ \hline QoS & Quality of service & RAN & Radio access network \\ \hline RF & Radio frequency & RFID & Radio frequency identification \\ \hline RIC & RAN intelligent controller & RNN & Recurrent neural network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of terms and abbreviations.
## 3 Results
### Correlation between Seven CT, Twenty-Four MRI Features and Ki-67 Score
The regression method was selected to be \"stepwise\" in SPSS. The multiple regression model converged to a single variable--Cube IMC1. The resulting equation from the model is \(\left(Ki-67\right)_{CT}=0.114+0.145\times\left(Cube\;IMC1\right)_{CT}\), with a correlation coefficient of 0.988, and \(p=0.001\). The 95% confidence interval for the slope and the intercept in the CT equation are 0.104 to 0.186 and 0.093 to 0.134, respectively. Similarly, the multiple regression model for all MRI features from Table 2 resulted in a single predictor--Cube IMC2. The predictive linear equation is: \(\left(Ki-67\right)_{MRI}=0.221-0.22\times\left(Cube\;IMC2\right)_{MRI}\) with a model \(p\) of 0.001. The 95% confidence interval for the slope and the intercept in the MRI equation are \(-0.275\) to \(-0.168\) and 0.178 to 0.265, respectively. These results indicate that the third-order joint probability texture features, represented by the co-occurrence cubes, are best suited as predictors in the multiple regression analyses among imaging variables and post-therapy histopathology tumor proliferation markers in both CT and MRI.
## 3 Calibration Circuit and Testbed for a Large Number of Antennas  
### Basic Calibration Principle  
To realize IBF, a calibration technique that compensates for the difference between the complex amplitudes of the transmitters and receivers at the BS is required [20]. Figure 3 shows the calibration circuit configuration. As shown in this figure, we obtain \(T_{i}R_{j}\)\((i=1,2,\,j=2,1)\), where \(T_{i}\) and \(R_{j}\) are the complex amplitudes of the transmitter and receiver, respectively. Because the calibration value required by the \(k\)th branch is \(T_{k}/R_{k}\)[20], the relative calibration values can be obtained from the circuit shown in Figure 3.  
Figure 2: Frame format of multi-user multiple-input multiple-output (MU-MIMO) without channel state information (CSI) feedback.  
This concept can be extended to a large number of antennas [21], and the configuration for this case is shown in Figure 4. To enable the calibration of a large number of antennas using the hardware in Figure 3, switches with \(M\) branches are required when the number of elements is \(N\). In contrast, the configuration in Figure 3 obtains the calibration values between two adjacent branches using a single-pole double-throw (SPDT) switch.  
When \(T_{1}R_{2}\) and \(T_{2}R_{1}\) are obtained in advance, for example, using the configuration shown in Figure 3, the ratio between antennas #1 and #3 can be denoted as  
\[D_{3,1} = D_{3,2}\cdot D_{2,1} \tag{1}\] \[= \frac{T_{2}R_{3}}{T_{3}R_{2}}\frac{T_{1}R_{2}}{T_{2}R_{1}}\] (2) \[= \frac{T_{1}R_{3}}{T_{3}R_{1}} \tag{3}\]  
From Equation (3), we can obtain the relative calibration value of antenna #3 against antenna #1. Further, the ratio between antennas #k and #3 is expressed as  
\[D_{k,1} = D_{k,k-1}\cdot D_{k-1,k-2}\cdots D_{3,2}\cdot D_{2,1} \tag{4}\] \[= \frac{T_{k-1}R_{k}}{T_{k+1}R_{k}}\frac{T_{k-1}R_{k}}{T_{k}R_{k-1} }\cdots\frac{T_{2}R_{3}}{T_{3}R_{2}}\frac{T_{1}R_{2}}{T_{2}R_{1}}\] (5) \[= \frac{T_{1}R_{k}}{T_{k}R_{1}} \tag{6}\]  
Figure 4: Calibration circuit for a large number of base station (BS) antennas. Tx: Transmitter. Rx: Receiver.  
Figure 3: Basic circuit configuration of calibration method.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Fourier Transform
The DFT spectrum may be obtained by taking the absolute value of the Fourier coefficients in Equation (4). The Fourier transform is not defined for unequally spaced time series. Even DFT is not an appropriate method of estimating the signal amplitudes and frequencies of unequally spaced time series [3; 9]. The Fourier transform and its modifications do not consider trends and/or datum shifts present in time series, and they are not appropriate for analyzing non-stationary time series. Moreover, there is no rigorous statistical testing for assessing the Fourier transform results.
## 3 Results
The sensitivity of the family DPSM-computed results on the assumed radiation pattern of the individual families is investigated in Figures 10 and 11. The standard deviation (\(\sigma\)) of the Gaussian radiation profile is changed from \(1.3r_{s}\) to \(1.7r_{s}\) with an interval of \(0.2r_{s}\) and the normal velocity profiles on the transducer surface along a diameter are plotted in Figure 3.7 Clearly, the results are sensitive to the assumed radiation pattern. However, all three curves generated by the family DPSM formulation are much closer to the true solution (\(v_{z}=1\)) than the classical DPSM-generated results. It should be mentioned here that Figures 5-9 were generated for \(\sigma=1.5r_{s}\). These results show that if we are interested in computing the field very close to the transducer surface (\(z<r_{s}\)) then the family DPSM formulation must be adopted because simply multiplying the computed field by a scaling factor cannot get rid of the oscillations in the computed field near the transducer. However, if such near field solutions are not needed, then the classical DPSM solution with a scaling factor generates accurate results.
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 5 Experiments
### Robustness of Compressed Models on On-Manifold Test Data  
We investigate the robust optimization with adv-on, which we consider as the worst-case of natural noise that can enhance the robustness of model compression.  
Figure 3 shows the robustness results in terms of the prediction accuracy on the original test and adv-on test datasets. The grey vertical lines indicate the compression rate values for which the compressed models will have at least 90% of the uncompressed reference models obtained by MC(regular). The results support our hypothesis at high compression ratios over 70% (the area marked with blue background color in the plots): MC-AT(on) has an average performance improvement of 33.76% on adv-on test data compared to the MC(regular).  
Compared to MC-TRADES, our MC-AT(on) showed 23.68% higher average accuracy on adv-on test. In addition, at high compression ratios over 70%, MC-AT(on) reached 26.24% higher average accuracy than MC-AT(off), and 10.74% higher than MC-AT(dual). Interestingly, in the case of the HAR dataset, which is an IoT sensor dataset containing a relatively large amount of noise, only MC-AT(on) and MC-AT(dual) worked properly in the adv-on test.  
Table 2 shows the comparison of robustness at the compression rates where the reduced models exhibit at least 90% of the reference models (also indicated with grey vertical lines in Figure 3). In particular, for the combined test composed of natural test and adv-on test data, MC-AT(on) consistently showed the highest performance compared to the other robust model compression methods.  
From the above results, we can conclude that MC-AT(on) can produce highly compressed DNN models robust to natural noise.  
Figure 3: The test results on five datasets (simulation dataset, MNIST, FMNIST, CIFAR-10, and HAR) tested with the original testset and adv-on testset according to the model sparsity rates. The color of each line indicates the different model compression method. The light gray dotted-vertical lines indicate the optimal compression rate at the maximum point that regularly compressed models maintain above 90% of the original accuracy. We can confirm that MC-AT(on) effectively handles the test accuracy of original examples and on-manifold adversarial examples in all five dataset, especially with high model compression ratios.