esa-sceva/satcom-chunk-collection huggingface dataset:
# Snap-Through Buckling Mechanism for Frequency-up Conversion in Piezoelectric Energy Harvesting
## 4 Results
### Finite Element Analyses and Numerical Results of the STB-EH Device in Open-Circuit Configuration
The numerical outcomes of the dynamic analyses are shown in the next section, along with the experimental counterparts. It can be observed that the response is dominated by the first two modal frequencies, which involve a combined motion of the bistable mechanism and of the bimorph beam.
# A Comparison between Two Reduction Strategies for Shrouded Bladed Disks
## 4 Numerical Results
### Additional Test Case for Efficiency Analysis.  
In order to double-check the results of Table 4, a 32-sector bladed disk is generated as Test Case C, in two configurations with linear (13,885 nodes) and quadratic (89,224 nodes) elements.  
While for Test Cases A and B the mesh size was uniform over the entire sector (blade and disk), in Test Case C (Figure 8), a more realistic meshing strategy is adopted. The mesh is more refined in the blade, while it is coarser in the disk, consistently with what usually happens with FE models developed for bladed disk dynamics, since the critical component for high cycle fatigue failure is the blade.  
The results listed in Table 4 are updated with the addition of the Test Case C results (Table 5). The new results contradict the conclusions drawn before, since the two-step method looks more efficient for Test Case C with linear elements that for Test Case A with quadratic elements, although the latter has a bigger size.  
Based on the results in Table 5 and on the remarks in Section 3.3, it is observed here that a suitable parameter to rank the Test Cases in terms of numerical efficiency is the ratio \(\boldsymbol{q_{H}}/\boldsymbol{q_{I}}\). (interface/inner nodes), since:  
1. the calculation of the constraint modes in the 1 st step is the most time-consuming operation in the two-step process,
2. the computation of the fixed-interface modes is the most time-consuming step of the direct method.  
In Figure 9, the normalized computational time is plotted versus the ratio \(\boldsymbol{q_{H}}/\boldsymbol{q_{I}}\) and a monotonic curve is in fact obtained.  
According to these results, if the number of interface nodes is lower than 10% of the inner nodes of the fundamental sector, the two-step method is more efficient than the direct method.  
Figure 8: Particular of the Test Case C mesh (**a**) at the blade root and (**b**) at the blade tip (blade-shroud contact area, right).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Case** & **Node \#** & **Direct (s)** & **Two-Step (s)** & **Two-Step/Direct** \\ \hline B-Lin & 2454 & **328** & 641 & 1.95 \\ B-Quad & 6052 & **779** & 1169 & 1.50 \\ A-Lin & 11,672 & **322** & 336 & 1.04 \\
**C-Lin** & 13,885 & 646 & **110** & 0.17 \\ A-Quad & 30,920 & 1120 & **620** & 0.55 \\
**C-Quad** & 89,228 & 2244 & **332** & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Total calculation times for the proposed reduction techniques (Test Cases A, B and C).  
Since, as mentioned above, the FE models of bladed disks are more refined in the blade than in the disk, as for Test Case C, the two-step method looks promising for industrial applications, when time is always one of the major constraints.
# A Study of Emergency Aircraft Control During Landing
## 4 Research Platform  
The platform setup consisted of a computer with a preconfigured MATLAB and Simulink R2022a environment, an X-Plane 11 flight simulator with UDP connectivity, and a joystick. Data transmission modules designed in Simulink enabled the recording of flight parameters and allowed feedback control commands to be sent to the simulator.  
To design data transmission and control system modules, several toolboxes, including Control System Toolbox, Embedded Coder, Simulink Desktop Real-Time, Real-Time Kernel for Desktop Real-Time, DSP System Toolbox, Signal Processing Toolbox, System Identification Toolbox, and Simulink Control Design, need to be installed.  
The control system concept was designed using the Simulink Connected IO Mode template, a part of the Desktop Real-Time package preconfigured to run external data transmission. The template extends the simulation capabilities to include real-time mode. Handling of the input and output signals from data transmission blocks during simulation is performed by a system kernel parallel to execution of the control algorithms in Simulink application. Both processes are run on a single machine using shared memory for data transmission. In this project, the Software-In-the-Loop simulations were not run in real-time mode due to limitations of the accessible hardware.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions
### Flexural Strength  
As shown in Figure 7, on the 3 days with the W/B at 0.5, the flexural strength without adding GGBFS and waste PE was 9.1 MPa to 5.9 MPa, which indicated that the flexural strength lowered with increasing added volume (7.7% to 35.2%). After 28 days, the flexural strength of the control group was 15.8 MPa, the added volume of waste PE increased to 4%, and the flexural strength was 12.7 MPa (decreased by 19.6%). On the 91 st day, the flexural strength of the control group was 22.7 MPa. The flexural strength (19.9 MPa) was the lowest when the added waste PE was 4%. When the added volume of waste PE increased from 1% to 4%, the flexural strength decreased by 2.3% to 15.8%.  
After 56 days, the replaced volume of GGBFS was 10%, added waste PE was 1% to 2%, and the flexural strength decreased by 0.5% to 2.4%. When the added waste PE was 3% to 4%, the flexural strength dropped by 8.6% to 10%. After 91 days, the added waste PE was 1% to 2%, and the flexural strength dropped by 1.6% to 4.5%. When the added waste PE was 3% to 4%, the flexural strength dropped by 9.4% to 10.2%. This result indicated that GGBFS could continue the pozzolanic reaction during the late period and increase flexural strength.  
Figure 7: Flexural strength of cement mortars with the addition of different ratios of waste PE and GGBFS replacement.  
The experimental results showed that when the waste PE content was less than 2%, the percentage of strength reduction was within 5%. This way, the waste could be effectively recycled, and energy consumption and environmental pollution could be reduced. As the replaced volume of GGBFS increased, the increase in strength in the early period was slow, but that in the late period was obvious under the pozzolanic reaction. The flexural strength could be the highest when GGBFS was 20% in the late period, followed by the flexural strength when GGBFS was 10%. To sum up the above, the addition ratio of waste PE should be controlled within 2%, which will effectively reuse waste and reduce pollution.
# Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
## 2 The Multi-Access Framework
### Protocol-Agnostic Multipath Framework  
For flexible experimentation, we also implemented a protocol-agnostic user-space multipath framework supporting MP-DCCP. This framework operates as a multi-threaded application, receiving packets from a Linux TUN device and encapsulating them in a custom header, mirroring the function of kernel-space MP-DCCP headers. The header includes a global sequence number, a path sequence number, and a timestamp. The application  
Figure 3: Nested Congestion control with 3 instances.  
schedules packets across multiple single-path sockets, with separate threads used for sending and receiving for each traffic flow. A separate thread is used at the proxy side to listen for connection requests and establish new tunnels. This framework incorporates the CPF scheduler and offers the same reordering functionality as MP-DCCP. The user-space implementation permits the use of DCCP-based sockets per tunnel to resemble MP-DCCP functionality but also supports, e.g., TCP sockets. Consequently, it allows us to compare the performance when using multiple TCP tunnels (e.g., using TCP BBR) versus multiple DCCP tunnels when combined with the schedulers and reordering modules of MP-DCCP.
## 2 The Principle of Remanence Generation and Elimination
### The Principle of Demagnetization of Remanence in the Core  
The principle of demagnetization of remanent magnetism in the core is primarily achieved by applying a gradually decreasing alternating magnetic field, which continuously deflects and reverses the magnetic domains within the core, ultimately accomplishing the goal of demagnetization [17; 18].  
The magnetic domain is the fundamental magnetic unit within ferromagnetic material, and the magnetic moments within it are oriented in the same direction. In the unmagnetized state, the arrangement of magnetic domains is disordered, causing the material as a whole to lack magnetism. When an external magnetic field is applied, the magnetic domain is reoriented such that the direction of its magnetic moment aligns with the direction of the external magnetic field, leading to the magnetization of the material. Within the transformer core, the reorientation of the magnetic domains serves as the foundation for the magnetization and demagnetization processes [19].  
In the degaussing process, the intensity of the applied alternating magnetic field gradually decreases, and the direction of the magnetic moment of the magnetic domain is continuously adjusted in response to the changing magnetic field. When the strength of the alternating magnetic field reaches zero, the direction of the magnetic moment of the magnetic domain becomes disordered, and the remanence within the core is eliminated. This process can be illustrated by a hysteresis loop, the area of which represents the energy loss during magnetization and demagnetization. The domain deflection process is depicted in Figure 2[20; 21; 22].  
The demagnetization techniques for power transformers encompass several methods, including the direct current (DC) method, polarity reversal method, micro pulse demagnetization method, and constant voltage frequency conversion method. The DC method, as outlined in the \"Guidelines for DC Demagnetization Test of Power Transformers\", employs a current source with alternating directions and reduced amplitude for degaussing. The polarity reversal method leverages the symmetrical properties of the hysteresis loop to achieve effective demagnetization by varying the DC application time of different polarities. The micro pulse demagnetization method employs small bipolar pulses to reduce the core flux, thereby increasing the excitation inductance and controlling the current value for demagnetization. This oscillation demagnetization method utilizes an RLC oscillation circuit to generate an attenuating oscillation current during the demagnetization process [23; 24; 25].  
Figure 2: Schematic diagram of the domain deflection process (The arrows in the figure represent the magnetic directions in each magnetic domain).
## 3 Results
### Conclusion of Case Study 2
#### 3.1.3 Case Study 3: \"Memories of Kellie\"  
Many cultural heritage projects use creative technologies to convey information about artifacts and locations, with varying success [67; 68; 69; 70]. Ensuring a robust lifecycle of design and testing is essential [71; 72; 73]. \"Memories of Kellie\" was developed at the Glasgow School of Art to explore using interactive narratives for DCH. It is a 3D application for desktop PCs, digitally representing Kellie Castle and Gardens in Scotland, reflecting its historical evolution from 1150 AD to the 1970s [74].
## 4 Experimental Results
### Performance Comparison of Other Method and WFBRCSP
3 Comparison of Computational Time Between WFBRCSP and Various Methods  
Figure 6 exhibits the total training time and total testing time for each method on the two datasets. The X-axis represents the methods, while the left Y-axis and right Y-axis represent the training time and testing time, respectively, both of which include the EEG data-preprocessing time. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Subject** & **B01** & **B02** & **B03** & **B04** & **B05** & **B06** & **B07** & **B08** & **B09** & **Mean** & **Mean Kappa** \\ \hline CSP & 71.25 & 54.29 & 56.88 & 92.50 & 67.81 & 73.12 & 73.75 & 87.81 & 78.44 & 72.87 & 0.46 \\ RCSP & 71.25 & 61.79 & 58.13 & 94.06 & 71.88 & 74.38 & 73.75 & 90.00 & 81.25 & 75.17 & 0.50 \\ FCSP & 75.94 & 58.21 & 57.81 & 95.62 & 88.75 & 86.56 & 76.88 & 89.38 & 82.19 & 79.04 & 0.58 \\ FRCSP-PSS & 80.31 & 73.93 & 68.12 & 95.94 & 94.38 & **87.50** & 87.50 & 91.25 & 81.88 & 84.53 & 0.69 \\ FRCSP-MIPS & 80.94 & 72.86 & 68.44 & 96.25 & 93.12 & 85.94 & 86.88 & 90.62 & 81.88 & 84.10 & 0.68 \\ OFRSCP & 70.62 & 62.14 & 57.81 & 81.25 & 84.38 & 69.69 & 81.25 & 88.75 & 86.25 & 75.79 & 0.52 \\ WFRCSP & **83.44** & **76.07** & **71.56** & **97.19** & **95.00** & **87.50** & **88.12** & **91.88** & **82.81** & **85.92** & **0.72** \\ \((\beta,\gamma,N)\) & \((0.6,0.1,10)\) & \((0.3,0.9,6)\) & \((0.9,0.3,14)\) & \((0.5,0.5,16)\) & \((0,1,4)\) & \((0.9,0.6,14)\) & \((0.0,4.12)\) & \((0,1,14)\) & \((0.8,0.8,10)\) & **85.92** & **0.72** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracy of BCICIV IIb (%). Figure 4: The confusion matrix of BCICIV IIb for subjects “B01”, “B03”, and “B04”. Figure 3: The confusion matrix of BCICIII IVa for subjects “aa”, “al”, and “av”. 4. Comparison of ROC Curves and AUC Values Between WFBRCSP and Various Methods  
Figure 7 exhibits the comparison of ROC curves and AUC values for various method models on subject \"aa\" in Data set I. The ROC curve is a widely used graphical representation to evaluate the performance of binary classification models. It exhibits the relationship between the true positive rate and the false positive rate, illustrating the model's performance under different thresholds. The point in the top-left corner represents a high true positive rate and a low false positive rate. The steeper the curve, the better the model's generalization ability. The AUC value represents the area under the ROC curve and is used to quantify the overall performance of the ROC curve. The larger the AUC value, the better the performance of the model. Figure 5: The classification accuracy according to the parameter set for subject “aa” in BCICIII IVa. Figure 6: (**a**) The required time for training and test in BCICIII IVa; (**b**) the required time for training and test in BCICIV IIb.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 5 Results  
In this section, we investigated the performance of different satellite constellations with regards to the RTT and ping loss across two IoT scenarios.
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 2 Physical Modeling of the Device  
The wearable device mainly consisted of seven micropumps arranged in a wrist watch dial fashion, as shown in Figure 1a. Each micropump consisted of a pump chamber, a pentagonal microneedle, a four quarters' piezoelectric bimorph actuator (FQPB), a silicon diaphragm, and a biosensor, as shown in Figure 1b. The piezoelectric bimorph was made up of two layers of piezoelectric material glued together with the help of epoxy glue. The mechanical properties of the materials are given in Table 1. The working fluid considered was blood, a non- Newtonian fluid, with a density of 1060 kg/m\({}^{3}\) and a viscosity of 0.0035 Pa-s [25].
# Effect of Subject-Specific Region of Interest on Motor Imagery Brain-Computer Interface
## 5 Conclusions  
In this study, subject-specific regions of interest (ROIs) based on optimal ERD patterns were proposed for motor imagery BCI systems. Higher mutual information of time-frequency was obtained from each motor imagery sample. The features were extracted using ITD or DWT from each time-frequency segment and combined with the neural network classification to evaluate the effectiveness of the proposed ITD with ROI and DWT with ROI techniques. The own recorded data was validated with a dataset from BCI Competition IV; here, the ROI-based ITD achieved the highest accuracy of 92.70%, whereas for the ROI-based ITD using the Competition IV data, the accuracy was 96.87%. Compared to DWT with ROI and CSP, ITD with ROI estimation showed a higher classification accuracy of 7.32% and 8.56%, respectively. Thus, the proposed algorithm employing subject-specific regions of interest is promising for practical applications in motor imagery-based brain-computer interface systems.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13116364/s1](https://www.mdpi.com/article/10.3390/app13116364/s1)]([https://www.mdpi.com/article/10.3390/app13116364/s1](https://www.mdpi.com/article/10.3390/app13116364/s1)), Figure S1: ROC of ITD for subjects 3 to 14, Figure S2: Topography maps for subjects 3, 4, 5, 7, 8, 9, 10, 12, and 13 at the frequencies of the mu rhythm (8 to 12 Hz) and beta rhythm (18 to 30 Hz), comparing each motor imagery task with the resting condition, Table S1: Performance using ITD with and without ROI for subject's 3 to 15.  
Conceptualization, E.A.M. and M.Z.Y.; data curation, E.A.M. and I.K.A.; formal analysis, E.A.M., I.K.A. and M.Z.Y.; investigation, E.A.M. and I.K.A.; methodology, E.A.M. and M.Z.Y.; supervision, M.Z.Y.; validation, E.A.M. and I.K.A.; visualization, E.A.M.; writing--original draft, E.A.M. and I.K.A.; writing--review and editing, M.Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Ministry of Education Malaysia under the Higher Institu-tional Centre of Excellence (HICoE) Scheme awarded to the Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Malaysia; and in part, by CISIR's Opex Incentive FY2023 (Cost Centre: 015 LB0-063).  
The study was conducted in accordance with the Declaration of Helsinki, and approved by an internal ethics committee of the Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (29 November 2013).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data is unavailable for sharing due to privacy or ethical restrictions.  
The authors would like to thank Universiti Teknologi PETRONAS and the University of Blue Nile, and the participants for their supports and cooperations.  
The authors declare no conflict of interest.
## Appendix C Capacitance Matrix and Total Capacitance  
The system's total capacity is given by (10). The formula can be derived starting from the Maxwell capacity matrix  
\[C^{M}=\begin{bmatrix}C_{11}&C_{12}\\ C_{21}&C_{22}\end{bmatrix}.\] (A18)  
The relation between the charge and the potential is \(\mathbf{Q}=\mathbf{C^{M}}\mathbf{V}\):  
\[\begin{bmatrix}Q_{1}\\ Q_{2}\end{bmatrix}=\begin{bmatrix}C_{11}&C_{12}\\ C_{21}&C_{22}\end{bmatrix}\begin{bmatrix}V_{1}\\ V_{2}\end{bmatrix},\] (A19)  
where \(Q_{1}\) and \(Q_{2}\) are the charges of the pad up and pad down respectively and \(V_{1}\) and \(V_{2}\) are the corresponding charge potentials. Defining the inverse matrix \(\mathbf{E}=(\mathbf{C^{M}})^{-1}\), the relation become:  
\[\begin{bmatrix}V_{1}\\ V_{2}\end{bmatrix}=\begin{bmatrix}E_{11}&E_{12}\\ E_{21}&E_{22}\end{bmatrix}\begin{bmatrix}Q_{1}\\ Q_{2}\end{bmatrix}\] (A20)  
with the corresponding equation:  
\[\begin{cases}V_{1}=E_{11}Q_{1}+E_{12}Q_{2}\\ V_{2}=E_{21}Q_{1}+E_{22}Q_{2}\end{cases}.\] (A21)  
The voltage difference between the pads is:  
\[\Delta V=V_{1}-V_{2}=(E_{11}-E_{12}+E_{22}-E_{21})Q\] (A22)considering \(Q=Q_{1}=-Q_{2}\) due to the symmetry of the system. The current flowing between the pads is given by \(I=-\frac{dQ}{dt}\), therefore \(\frac{dI}{dt}=-\frac{d^{2}Q}{dt^{2}}\). Considering a linear inductance \(L\) between the pads, the voltage is:  
\[\Delta V=L\frac{d^{2}Q}{dt^{2}} \tag{123}\]  
Combining (122) and (123), results in  
\[\frac{d^{2}Q}{dt^{2}}=\frac{E_{11}-E_{12}+E_{22}-E_{21}}{L}Q \tag{124}\]  
As the transmon in the first approximation is an LC harmonic oscillator we can compare it with (124) and see that  
\[C=(E_{11}-E_{12}+E_{22}-E_{21})^{-1} \tag{125}\]  
The formula (125) can be written depending on the components of the Maxwell capacity matrix \(\mathbf{C^{M}}\), obtaining the relation for the capacity:  
\[C=\frac{C_{11}C_{22}-C_{12}C_{21}}{C_{11}+C_{12}+C_{21}+C_{22}} \tag{126}\]
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 3 Experimental Results  
### Datasets  
We have employed five open source databases (i.e., Diaretdb1, MESSIDOR, HRF, DRHAGIS and ORIGA) that we utilized in our experiments for eye diseases detection, which do not have bbox ground truths, so we first developed the bbox annotations of the datasets which are necessary for the FRCNN training.  
Diaretdb1 [51] is the dataset used for benchmarking of DR identification from retinal images. The Diaretdb1 database comprises 89 fundus samples of which 84 images contain the DR signs and the five remaining samples are considered as normal, that means they have no sign of DR. DR lesions are characterized by the following signs; hemorrhages, microaneuryss, hard and soft exudates. The images are of size 1500*1125 pixels and were taken with a fundus camera utilizing the same 50-degree field-of-view (FOV) with different image settings. The MESSIDOR database comprises 1200 images having two grades DR and DME. The images were captures with a 3 CCD camera with a 45-degree FOV with a resolution of 2304 \(\times\) 1536, 2240 \(\times\) 1488, and 1440 \(\times\) 960 pixels.  
HRF stands for a high-resolution fundus database developed by the ophthalmology department, at Friendrich Alexander University Erlangen-Nuremberg (Erlangen, Germany) and the Brno University, Faculty of Electrical Engineering and Communication, Department of Biomedical Engineering, Brno (Czech Republic). The HRF database has a total of 45 images in which 15 images are of healthy patients, 15 images of patients with DR, and 15 images are from glaucomatous patients [52]. DRHAGIS stands for Diabetic Retinopathy Hypertension Age-related macular degeneration and Glaucoma images database, and was developed by Health Intelligence (Sandback, UK). The database consists of 40 images in which 10 images are affected with glaucoma [53]. ORIGA is the Online Retinal Fundus Image Database for Glaucoma analysis, with 650 data samples, containing 168 glaucomatous and 482 normal images. The images were collected from the Singapore Eye Research Institute [54] and were annotated by medical experts.