esa-sceva/satcom-chunk-collection huggingface dataset:
# Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis
## 3 Sensing Technologies
### Non-Contact Technology  
#### 3.4.1 RFID  
Radio Frequency Identification (RFID) technology offers a unique approach to posture monitoring, particularly in environments where non-intrusive and seamless integration is desired. It consists of small, lightweight tags that can be attached to different parts of the body or embedded in clothing. These tags transmit data to RFID readers, which process this information to determine the position and movement of the tags. They are commonly used for tracking and identification and can also be employed in innovative ways to monitor sitting posture. The system leverages the movement, orientation, and presence of RFID tags attached to key body parts or objects (such as a chair) to infer posture. [PERSON] et al. [102] used commercial RFID technology for a more convenient and flexible solution for posture monitoring. They placed an RFID tag next to the user, as shown in Figure 8, unlike wearable sensor-based approaches that require attaching sensors to specific points on the body. The position of the RFID tag remains stable beside the user, unaffected by factors such as breathing movements, ensuring consistent and reliable posture detection. By analyzing the phase variations induced by different postures, the system can acquire high-performanceposture recognition. [PERSON] et al. (2013) effectively identified seven common sitting postures affixing three RFID tags on the back of a user. They leverage the relationship between RFID tag phase changes and sitting postures, extracting valuable features to identify postures using suitable machine learning algorithms. [PERSON] et al. (2014) employed several passive RFID tags and analyzed the distribution of Received Signal Strength Indicator (RSSI) from them for recognizing the posture in real-time as individuals assumed various postures.
## 1 Introduction  
Secure information transmission has been widely investigated in the past, e.g., [1, 2, 3, 4, 5], where complicated techniques needed to be used to avoid information leakage. Since eavesdroppers cannot directly receive an optical signal without beam alignment and security authentication, FSO communication is a promising technique for secure information transmission. Compared to electromagnetic wave transmission, there are several advantages for such technique, e.g., high security performance, high data rate, long transmission distance, and low interference, etc. However, optical signals only can be transferred over a line-of-sight (LoS) path, where the signal quality will considerably deteriorate if obstacles exist in the transmission link. With the rapid development of wireless communications, longer transmission distance and higher reliability are more and more desirable for real applications. While noting that a relay can considerably improve communication coverage and reliability, FSO relaying transmission attracted a lot of attentions from both industry and academia over the past decade.
## 3 PIESAT-01 Multiple Satellites Interferometric Imaging
### Synchronous Data Processing  
For the extraction of time synchronization and phase synchronization in bistatic distributed SAR systems, the PIESAT-01 constellation employs synchronous links to alternately  
Figure 2: The flowchart of the PIESAT-01 imaging algorithm.  
transmit and receive signals between two satellites. This process involves range pulse compression, selection of direct wave signals from the high-resolution range profile (HRRP), extraction of synchronization information from the direct wave signals, and subsequent adjustment of sampling gates and compensation of echo phase errors to achieve time and phase synchronization.
# Design of an Active and Passive Control System of Hand Exoskeleton for Rehabilitation
## 4 Active and Passive Control  
### Control Strategy  
The overall control strategy of the hand rehabilitation exoskeleton designed in this paper is shown in Figure 4. If it is detected by the sensors that the patient's hand can do flexion-extension, then the active rehabilitation mode will be adopted, and the exoskeleton will follow the motion of the fingers. Otherwise, the passive rehabilitation mode will be adopted, and the exoskeleton will actuate the fingers to move. The motion ability of the hand is judged by the contact force between the exoskeleton and the hand detected by the force sensors, which is used for the real-time switch of the control strategy between active and passive rehabilitation modes.  
In the passive rehabilitation mode, the control system adopts a highly robust and reliable PD method to control the motion of the patient's finger to the functional position. In this mode, the patient or physician can choose suitable velocity gear for the inverse dynamics control to implement in early rehabilitation stage. Then the exoskeleton helps the finger follow the desired trajectory and move to the functional position at the preset velocity.  
In the active rehabilitation mode, the control system adopts impedance control to track the force on the fingertip and track the desired trajectory by the position control loop. In this mode, the patient or physician can preset the resistance according to the patient's movement ability. Then the exoskeleton tries to follow the patient's motion and keep the force between exoskeleton and fingertip around the preset resistance to improve the movement ability.  
The active plus passive rehabilitation mode is designed based on the two basic rehabilitation modes, detecting the force between the exoskeleton and the patient's fingertip through the force sensors distributed on the tip. The switch of the two modes is determined by the threshold value. In this mode, if the patient could exert force actively, the control system will switch to the active mode and follow the patient's motion. Otherwise, whenever the patient cannot exert enough force to move the fingers, the control system will switch to the passive mode to help the finger track the desired trajectory to reach the functional position at a preset velocity gear. In order to keep the system stable and compliant, in the control system, the impedance parameter is adjusted to be close to the input velocity parameter in the passive mode to provide smooth rehabilitation experience for the patients.  
Figure 4: The active/passive control block diagram.  
In the interaction rehabilitation software, when the virtual finger touches the contact plate, the collision signal is sent to the control system that will judge the functional position according to the signal. In the overall rehabilitation control strategy, the switch is accomplished by the sequence controller.
## 3 Results and Discussion  
In the following sections, we consider a typical corridor configuration, where a metallic pipe is buried within the soil under an HVAC transmission line. In particular, we consider a single section of the corridor in a 2D planar domain. The choice of a 2D approach allows for a direct comparison between the solvers in FLARE and the ones implemented in COMSOL Multiphysics(r)[46], which we use as a benchmark for FLARE. However, this simplifying assumption corresponds to the assumption that each considered conductor has an ideal return path. We refer the reader to the more in-depth discussion of this limitation of 2D approaches provided in [37; 47], and we save the implementation in FLARE of the numerical techniques described herein for future works.  
We simulated the considered configuration, focusing on the current induced along the pipeline for two different operating conditions of the transmission line, i.e., sinusoidal and transient. These two physical scenarios required different numerical approaches implemented in FLARE (see Section 2). In each case, the results yielded by FLARE were validated against COMSOL Multiphysics(r), a well-established commercial software that has already been used to analyze electromagnetic interference on pipelines in [48].
## 4 Materials and Methods
### Free Amino Acid Quantification Using GC-MS  
First, each sample was thawed and 4.0 g was randomly portioned and combined with 40 mL DI water. The mixture was centrifuged at 4000 rpm for 10 min. A 100 \(\mu\)L sample of each supernatant was subsequently derivatized per the EZ:faast Free (Physiological) Amino Acid Analysis by GC-MS kit instructions.  
Gas chromatography-mass spectrometry (GC-MS) analysis of the derivatized samples was performed using equipment specifications listed in our previous publication [49]. Each amino acid was identified by matching the kit's provided major ions and retention times to the sample ion spectra chromatogram. Identification was confirmed using the kit's SD1 and SD2 amino acid standards.  
A total of 14 free amino acids were quantified using the constructed calibration curves with the EZ:faast-GC-MS analysis kit [49]. Briefly, 13 aliquots (equivalent to 12.5-3200 nmol/mL) of standard reagents SD1 and SD2 were analyzed via EZ:faast-GC-MS in the same manner as the samples. The calibration curves had 6-13 levels (r\({}^{2}\geq\) 0.99 excluding serine \(r^{2}\) = 0.97).
## 3 Results and Discussion
### Coreflooding
The maximum oil recovery of 32% was obtained by flooding carbonate samples with Ca2 GLDA solutions. It should be noted that all coreflooding experiments were conducted at the same conditions and similar chemical concentrations and pH were used for all GLDA types. However, the chelating agent salt was changed during the coreflooding experiments. Hence, the additional oil recovery can be attributed mainly to the GLDA salt type; the best chelating agent type provides the highest oil recovery. Overall, the coreflooding experiments indicated that under the same conditions Ca2 GLDA can outperform the other GLDA types and results in the highest oil recovery from carbonate rocks. Figure 5 shows the oil recovery profiles from the sandstone and carbonate samples during SW and Ca2 GLDA flooding. The total oil recovery of 70% of the OOIP was obtained for the carbonate sample, while around 68% of the OOIP was produced from the sandstone sample, indicating that relatively higher oil recovery can be achieved from carbonate reservoirs. During SW flooding, the oil recovery from the sandstone sample was higher compared to that from the carbonate sample, which is correlated mainly to the higher rock permeability for sandstone rocks. An average permeability of 75 mD was obtained for  
Figure 3: Pore volume to breakthrough during glutamic acid di-acetic acid (GLDA) flooding in carbonate and sandstone samples. the sandstone sample, while the carbonate sample had an average permeability of 10 mD. On the other hand, higher oil recovery was produced from the carbonate sample during the chelating agent flooding, indicating that GLDA solutions can work better in carbonate reservoirs compared to in sandstone formations. In addition, the pressure drop was measured for all experiments. Figure 6 shows the pressure drop profiles for the three salt types in limestone rocks. As expected, the pressure profiles were different, because the GLDA breakthrough times were different and depended on the salt type. The SW injection period was very similar in the three experiments, because the breakthrough times were almost the same and the recovered oil amounts were the same. In the case of the GLDA injection period, the breakthrough time was different for the calcium-based GLDA compared to for the sodium- and potassium-based GLDA. In  
Figure 4: Oil recovery during the injection of different types of GLDA solutions into sandstone and carbonate rocks. Figure 5: Oil recovery profiles from carbonate (ILS1) and sandstone (BSS1) rocks during SW and Ca\({}_{2}\)GLDA flooding. addition, the pressure drop values were higher for both K\({}_{4}\)GLDA and Na\({}_{4}\)GLDA compared to for Ca\({}_{2}\)GLDA, because the oil recovery was higher in the case of calcium-based GLDA compared to both sodium- and potassium-based GLDA. Overall, at the end of the flooding experiments, Ca\({}_{2}\)GLDA showed the lowest pressure drop values, indicating that the flow conditions were improved.
## 3 Results
### Characterization of the Synthesized ZnO NPs
#### 3.1.4 FTIR Analysis  
The FTIR analysis was performed to confirm the presence of the functional groups of the metabolites of _C. spinosa_ L. over the surface of the synthesized nanoparticles. The FTIR spectrums of the green synthesized ZnO NPs are shown in Figure 4. The stretching vibration of the hydroxyl group(O-H) is related to the peak in the FTIR spectrum that appears between 3291 and 3464 cm\({}^{-1}\), which is attributed to the phenolic compound in the plant extract that is involved in the synthesis of NPs [32]. The peak at 1635 cm\({}^{-1}\) in the FTIR spectrum _of C. spinosa_ L. extract represents the stretching bands of (C=C) and (C=O) functional groups; an identical peak can also be seen at this location in the FTIR spectra of ZnO NPs produced at various temperatures. The organic functional groups at 1635 cm\({}^{-1}\) may not have entirely broken down because ZnO NPs' annealing duration was barely one hour. This results in the vibrational peak we observed in our sample. This finding is consistent with the information provided by [PERSON], [PERSON] et al. [33]. The peak at 2977 cm\({}^{-1}\) is caused by the aliphatic (C-H) groups. The peaks at 1380 and 1382 cm\({}^{-1}\) indicate the (C-O-H). Between 492 and 422 cm\({}^{-1}\), metal-oxygen (Zn-O) stretching can be seen [34]. As a result, it can be said that the _C. spinosa_ L. fruit extract has functional groups with (O-H) and (C=O) that operate as a capping, reducing, and stabilizing agents for ZnO NPs made biosynthetically. As efficient capping agents, the biomolecules in the plant extract aid in  
Figure 2: UV–Vis spectroscopy analysis of the _C. spinosa_ extract and ZnO NPs.  
the production of nanoparticles. Several processes, including electrostatic stability, steric stabilization, hydration force stabilization, and van der Waals forces, appear to be used by the capping agents to stabilize NPs. The function and applications of nanoparticles depend on their stability.
# Design and Multi-Objective Optimization of Fiber-Reinforced Polymer Composite Flywheel Rotors
## 3 Modeling
### Total Stored Energy  
The rotor stores kinetic energy by rotation. Assuming a mere rotation around its axis of symmetry, \(z\), with the rotational velocity \(\omega\) and the moment of inertia \(I_{\text{zz}}\), the energy can be expressed as  
\[E_{\text{kin}}=\frac{1}{2}I_{\text{zz}}\omega^{2} \tag{1}\]  
The moment of inertia is defined by  
\[I_{\text{zz}}=\frac{1}{2}{\sum_{j=1}^{N_{\text{rim}}}}m_{j}\Big{(}r_{j+1}^{2}+ r_{j}^{2}\Big{)}, \tag{2}\]  
where \(m_{j}\) is the mass of each rotor rim and \(r_{j}\) and \(r_{j+1}\) are the inner and outer rim radius, respectively. Given the material density \(\rho_{j}\), the rim mass can be expressed as  
\[m_{j}=\tau h\rho_{j}\Big{(}r_{j+1}^{2}-r_{j}^{2}\Big{)}. \tag{3}\]  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Time of Operation per Year** & **2016** & **Hours** \\ \hline
**Acquisition costs** & 349,000 & CAD \\
**Economic lifetime** & 10 & years \\
**Maintenance costs** & 17,450 & CAD/year \\
**Space** & 34 & m\({}^{2}\) \\
**Room costs** & 680 & CAD/month \\ \(K_{\text{A}}\) & 17.30 & CAD/h \\ \(K_{\text{M}}\) & 8.66 & CAD/h \\ \(K_{\text{R}}\) & 4.05 & CAD/h \\
**MHR** & 30.01 & CAD/h \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of a calculation of the MHR for the four-axis filament winding system.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Name** & **MHR [CAD/h]** \\ \hline
**Filament winding system** & 30.01 \\
**Curing oven** & 7.29 \\
**Press** & 4.32 \\
**Lathe** & 4.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MHR for the considered machines.
## 2 Methodology  
In this study, a metalized carbon fiber composite material is developed, and a lightweight electromagnetic shielding chassis is designed using this material. Using the FDTD method and COMSOL software, the TDSE and heat dissipation performance of the chassis with different materials are simulated, and the comprehensive performance of the chassis with three different materials is compared.  
First, the nickel-plated carbon fiber composite is prepared, which was initially undertaken through an electroless deposition process. Following this, the essential task of calculating the SE involved the meticulous measurement of the complex permittivity and magnetic permeability. These critical electromagnetic parameters were accurately determined using a vector network analyzer, ensuring the reliability of the subsequent analyses. Subsequently, the architecture of the composite chassis was modeled to facilitate a comprehensive examination of electromagnetic field distributions within the structure. This modeling enabled the precise identification of field values at strategically placed observation points inside the chassis, achieved through the application of 3D-FDTD iterative calculations. The utilization of the FDTD method allowed for the extraction of time-domain SE metrics, providing a nuanced understanding of the chassis's shielding capabilities. Expanding upon this foundation, the study delved deeper into the nuanced analysis of the composite chassis's time-domain SE under a variety of test conditions. These conditions encompassed differing angles of incidence and polarization, alongside a spectrum of polarization modes, thereby offering a robust evaluation of the chassis's performance across diverse electromagnetic scenarios. Each scenario was meticulously designed to mimic realistic operational environments, thus ensuring the applicability of the findings. Finally, COMSOL software was used to analyze the heat dissipation rates of metal chassis, carbon fiber chassis, and composite chassis to fully evaluate the comprehensive performance of the shielding chassis proposed in this paper. Illustrative of the methodological approach, Figure 1 encapsulates the flow of the study, delineating each step from the preparation of the composites to the final analysis of time-domain SE.
## 2 Materials and Methods
A two-step method for modeling the dynamic motion of the instruments was prescribed in FEA, as described below, and in Figure 2.  
Step-1: To mimic the insertion process, the displacement-driven load was applied on the bottom surface of the instrument to push the instrument into the fatigue rig, until 1 mm of the tip extruded from the fatigue rig. The bottom surface was kinematically constrained. Step-2: To mimic the rotation of the instrument, a radian-driven input load was applied to the bottom surface of the instrument until the completion of the first rotation. Using the ODB field output function in ABAQUS, the maximum von Mises stress and maximum principal stress values were tracked and plotted every over 20 increments (every 0.05 s) in step 1, by selecting the element (under the \"Element Nodal\" option in the field output) that expressed the highest peak values at the end of the insertion step, excluding any stress singularities. The changes in the stress values were recorded during the rotation in step 2. The location of the overall maximum von Mises stress and principal stress values were also recorded using the \"Query\" function to determine the distance from the tip of the instrument, and the location was compared to the experimental fragment length. \begin{table}
\begin{tabular}{c c c c c} \hline
**Symbol** & **Parameter** & **PTU** & **PTG** & **VB** & **TRN** \\ \hline \(E_{A}\) & Austenite elasticity (MPa) & 28,248 & 30,573 & 28,672 & 26,580 \\ \(\
u_{A}\) & Austenite Poisson’s ration & 0.33 & 0.33 & 0.33 & 0.33 \\ \(E_{M}\) & Martensite elasticity (MPa) & 20,146 & 21,254 & 20,900 & 21,029 \\ \(\
u_{M}\) & Martensite Poisson’s ration & 0.33 & 0.33 & 0.33 & 0.33 \\ \(\varepsilon_{L}\) & Transformation strain & 0.0445 & 0.0377 & 0.0385 & 0.0356 \\ \(\delta\sigma/\delta T\)\({}_{L}\) & \(\delta\sigma/\delta T\) Loading & 6.7 & 6.7 & 6.7 \\ \(\sigma_{SL}\) & Start of transformation loading (MPa) & 422 & 341 & 296 & 266 \\ \(\sigma_{EL}\) & End of transformation loading (MPa) & 481 & 424 & 351 & 366 \\ \(T_{0}\) & Reference temperature (\({}^{\circ}\)C) & 25 & 25 & 25 & 25 \\ \(\delta\sigma/\delta T\)\({}_{U}\) & \(\delta\sigma/\delta T\) Unloading & 6.7 & 6.7 & 6.7 \\ \(\sigma_{SU}\) & Start of transformation unloading (MPa) & 161 & 161 & 161 & 161 \\ \(\sigma_{EU}\) & End of transformation unloading (MPa) & 118 & 118 & 118 & 118 \\ \(\sigma_{SCL}\) & Start of transformation stress in compression (MPa) & 28,248 & 30,573 & 28,672 & 26,580 \\ \hline \end{tabular}
\end{table} 
Table 2: Material constants used for UMAT inputs for the subroutine module \({}^{\star}\)ABQ_SUPER_ELASTIC_N3D\({}^{\prime\prime}\). Figure 2: Loading and boundary conditions are applied in a two-step process for instrument models. Blue arrows show the loadings on the bottom surface of the instrument. The yellow triangles show the fixed support on the bottom surface of the rig.
## 6 Data Transfer and the Creation of a 3D Model for VR and AR
### AR Visualization  
#### 6.3.1 AR 3D Models--Phases 1 and 2  
The process for processing 3D models for use in AR was very similar, but the model was more decimated due to the use of simpler hardware (tablet, smartphone, and regular laptop). Decimation of the 3D model was done in Instant Mesh and the result had 60,000 polygons and 4k diffuse and normal texture. The normal texture was calculated in the program xNormal.
## 3 Experiment and Results
### Experiment 1: Cartilage Change Prediction at the Bottom Horizontal Line
The ROC curves of these four ANN models were plotted in Figure 3.  
Note that different rebalance strategies were applied to the training set of the CDI and manual segmentation data, due to the difference in sizes. For the CDI, the synthetic minority oversampling technique (SMOTE) [22] was applied. SMOTE is an oversampling technique that generates more samples for the minority class. The new samples are not just copies of existing minority cases; instead, the algorithm combines the features of the target case with the features of its neighbors. Since only nine CDI locations were used to generate samples for each knee case, the dataset size was relatively small. Using SMOTE can increase the training data size and make samples more general. To rebalance the classes for manual segmentation, an undersampling strategy was applied to the training set which \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Quantification Method**} & \multicolumn{2}{c}{**Precision**} & **Recall** & **F-Measure** & **MCC** & **AUC** \\ \hline CDI & 0.667 & 0.133 & 0.222 & 0.235 & 0.651 \\ \hline Manual Segmentation & 0.201 & 0.220 & 0.210 & 0.025 & 0.496 \\ \hline CDI with rebalance & 0.400 & 0.267 & 0.320 & 0.190 & **0.669** \\ \hline Manual Segmentation with rebalance & 0.190 & 0.432 & 0.264 & 0.010 & 0.521 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction performance of artificial neural network (ANN) using bottom CDI locations and manual segmentation on X1 slice. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Quantification Method**} & \multicolumn{2}{c}{**‘Changed’**} & \multicolumn{2}{c}{**‘No Change’**} \\ \hline \multirow{4}{*}{CDI} & Original & Train & 33 & 138 \\ \cline{2-5}  & Test & 15 & 57 \\ \cline{2-5}  & After rebalance & Train & 132 & 138 \\ \cline{2-5}  & Test & 15 & 57 \\ \hline \multirow{4}{*}{Manual Segmentation} & Original & Train & 1652 & 6898 \\ \cline{2-5}  & Test & 667 & 2933 \\ \cline{2-5}  & After rebalance & Train & 1652 & 1652 \\ \cline{2-5}  & Test & 667 & 2933 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample distribution in experiment 1.  
reduced the majority class. This was because the original dataset was already very large (since every pixel along the cartilage was used as a sample). After dataset rebalances, the performance of both models improved. The AUC of the CDI method improved from 0.651 to 0.669 while the AUC of manual segmentation was improved from 0.496 to 0.523.