esa-sceva/satcom-chunk-collection huggingface dataset:
# Dielectric Elastomer Actuator for Soft Robotics Applications and Challenges
## 8 Optical Application with Tunablility  
Focus tunable lens technology has promising application areas including robotic systems, consumer electronics, medical imaging devices, optical detection, and many other types of optical equipment. Compared to traditional focus tunable lens structures, involving bulky motor assemblies, the adaptive focus tunable lens can tune the focal length by varying the refractive index of the optical medium or the shape of the lens surface profile. The adaptive focus tunable lens adopted various kinds of mechanisms, such as liquid-crystal (LC) based refractive index change, soft polymer deformation, liquid-droplet deformation, and electroactive polymer actuator. Recently, DEAs have been considered as attractive alternatives due to their advantages such as fast response, low power consumption, mechanical robustness, polarization-independent optical transparency, and highly reversible dynamic responses [10,133]. The adaptive focus tunable lens using DEAs can be categorized into a fluid-filled elastomeric lens [10,133,134,135,136], all-polymeric lens [137,138,139,140], and liquid droplet lens [141] based on their actuation mechanism.
# Fault Studies and Distance Protection of Transmission Lines Connected to DFIG-Based Wind Farms
## Appendix A
### The Derivation of Pre-Fault Rotor Flux Linkage  
Using a generator convention for the stator windings and a motor convention for the rotor windings, the flux linkage equations in a synchronous \(dq\) reference frame are expressed as [20]:  
\[\left\{\begin{array}{c}\psi_{sd}=-L_{s}i_{sd}+L_{m}i_{rd}\\ \psi_{sq}=-L_{s}i_{sq}+L_{m}i_{rq}\end{array}\right.,\] (A7)  
\[\left\{\begin{array}{c}\psi_{rd}=-L_{m}i_{sd}+L_{r}i_{rd}\\ \psi_{rq}=-L_{m}i_{sq}+L_{r}i_{rq}\end{array}\right..\] (A8)  
The stator active and reactive power output of DFIG can be expressed as [37]:  
\[\left\{\begin{array}{c}P_{s}=\frac{3}{2}(u_{sd}i_{sd}+u_{sq}i_{sq})\\ Q_{s}=\frac{3}{2}(u_{sq}i_{sd}-u_{sd}i_{sq})\end{array}\right..\] (A9)  
Assuming that the \(d\)-axis of the reference frame is aligned with the stator flux linkage vector,  
\[\psi_{sd}=\psi_{s},\,\psi_{sq}=0\] (A10)  
\[u_{sd}=D\psi_{sd}=0,\,u_{sq}=V_{s}=\omega_{s}\psi_{s},\] (A11)  
can be obtained.  
Substituting Equation (A10) into Equation (A7),  
\[\left\{\begin{array}{c}i_{rd}=\frac{L_{s}}{L_{m}}i_{sd}+\frac{\psi_{s}}{L_{ m}}\\ i_{rq}=\frac{L_{r}}{L_{m}}i_{sq}\end{array}\right.,\] (A12)can be obtained.  
Substituting Equation (A11) into Equation (A9),  
\[\left\{\begin{array}{c}i_{sd}=\frac{2\mathcal{O}_{s}}{3\mathcal{V}_{s}}\\ i_{sq}=\frac{2\mathcal{P}_{s}}{3\mathcal{V}_{s}}\end{array}\right.,\] (A13)  
can be obtained.  
Based on Equations (A8), (A12) and (A13), the rotor flux linkage equation is expressed as:  
\[\left\{\begin{array}{c}\psi_{rd}=(L_{s}L_{r}-{L_{m}}^{2})\frac{2\mathcal{Q}_ {s}}{3L_{m}V_{s}}+\frac{V_{s}L_{r}}{\omega_{s}L_{m}}\\ \psi_{rd}=(L_{s}L_{r}-{L_{m}}^{2})\frac{2\mathcal{P}_{s}}{3L_{m}V_{s}}\end{array} \right..\] (A14)  
If \(V_{s}=V_{pre}\) and the DFIG stator terminal instantaneous complex power before the fault is \(S_{pre}=P_{s}+j\mathcal{Q}_{s}\), then pre-fault rotor flux linkage is  
\[\overset{\rightarrow}{\Psi}_{r0}=(L_{s}L_{r}-{L_{m}}^{2})\frac{2\mathcal{Q}_ {s}}{3L_{m}V_{s}}+\frac{V_{pre}L_{r}}{\omega_{s}L_{m}}+j(L_{s}L_{r}-{L_{m}}^{2 })\frac{2\mathcal{P}_{s}}{3L_{m}V_{s}}.\] (A15)
## 2 Related Works
In [44], [PERSON] and colleagues calculated an RMSE value of 0.47 between the marker-less tracking systems and the VICON, considering the average results among all joints. In contrast, the range of value of the angular mean absolute error is 5-15 degrees for all the upper joints [44]. Azure Kinect and its previous versions were applied in various circumstances in the medical field. For instance, [15; 16] applied Kinect in evaluating patients with hip disorders. The inclination angles of the trunk and the pelvis were similar to the outcomes from the VICON system. Ref. [17] presents a tool for deducting forearm and wrist range of motion. In this study, results are obtained by a reliability test performed by a healthy group. In [18], evaluating the Global Gait Asymmetry index (GGA index) after knee joint surgery is accomplished using a set of Kineets. Moreover, in [19], the device was used to monitor the dynamic valps of the knee. The Kinect measurements were compared with OptiTrack, and the absolute average difference for the pelvis was 1.3 \(\pm\) 0.7 cm and for the knee in lateral-medial movement 0.7 \(\pm\) 0.3 cm. Moreover, the Azure Kinect is also useful in a telemedicine system to teleport the knowledge and skills of doctors [45]. Many studies evaluate AR-based applications highlighting challenges that still need to be addressed. Ref. [46] reassumes and sifts through all the technical challenges of AR and MR: tracking, rendering, processing speed, and ergonomics. The new Microsoft headset for MR still needs hardware improvements to overcome these issues and to allow the real-time use of MR in daily life applications. Indeed, ref. [47] shows all limitations of AR in sports and training fields. The most impacting challenge is the tracking accuracy which depends on the speed of motion, distance, noise, and hardware performances, followed by the Field of View limited by the headset. The Kinect can be considered the pioneer among markerless tracking systems [47]. However, skeleton tracking and motion reconstructions must still be monitored and filtered. Ref. [48] reports factors that can influence the results of Kinect performance, such as the absence of silhouette visual changes and the changeable hands and foot joint estimations. Newer Kinect versions have reduced some issues; however, other improvements or algorithm corrections may still be needed.
## 4 Test and Results
### Multistage TNEP Problem
#### 4.2.1 Garver System  
Four tests were also carried out for this system considering a multistage TNEP. The same parameters for the static TNEP were used. The results obtained are summarized in Table 5.  
Test 1: The best solution found by the HEGA for the multistage TNEP problem without SCC and active power losses has a total investment cost of US$80.79 \(\times\) 10\({}^{6}\) and no load shedding. This solution is equal to the one reported in [15] and found after solving 924 LPs with a computational time of 0.277 min.  
Test 2: The solution found by the HEGA for the multistage TNEP with SCC is equal to the one of test 1.  
Test 3: The best solution found by the HEGA for the multistage TNEP problem considering power losses presents a total investment cost of US$90.35 \(\times\) 10\({}^{6}\), and no load shedding. This solution is found after solving 571 LPs with a computational time of 0.241 min.  
Test 4: The best solution found by the HEGA for the multistage TNEP with SCC and power losses has a total investment of US$82.22 \(\times\) 10\({}^{6}\), and no load shedding. This solution is found after solving 12,415 LPs with a computational time of 3.94 min.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test**} & **Total Cost (10\({}^{6}\) US$)** & **LPs** & **Time (min)** & **Stage (P10\({}^{6}\) US$)** & **Power Losses (MW)** & **Configuration** & **Compensated Lines** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{80.79} & \multirow{2}{*}{924} & 1 & 30.00 & – & \(n_{4-6}=1\) & – \\  & & & & 2 & 36.45 & – & \(n_{3-5}=1\), \(n_{4-6}=1\) & – \\  & & & & 3 & 14.34 & – & \(n_{4-6}=1\) & – \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{80.79} & \multirow{2}{*}{924} & 1 & 30.00 & – & \(n_{4-6}=1\) & No compensated lines \\  & & & & 2 & 36.45 & – & \(n_{3-5}=1\), \(n_{4-6}=1\) & No compensated lines \\  & & & & 3 & 14.34 & – & \(n_{4-6}=1\) & No compensated lines \\ \hline \multirow{3}{*}{3} & \multirow{2}{*}{90.35} & \multirow{2}{*}{571} & \multirow{2}{*}{0.241} & 1 & 30.00 & 19.267 & \(n_{4-6}=1\) & – \\  & & & & 2 & 36.45 & 27.673 & \(n_{2-6}=1\), \(n_{3-5}=1\) & – \\  & & & & 3 & 17.21 & 31.643 & \(n_{2-3}=1\), \(n_{4-6}=1\) & – \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{82.22} & \multirow{3}{*}{12,415} & 1 & 30.00 & 19.607 & \(n_{4-6}=1\) & No compensated lines \\  & & & & 2 & 36.45 & 27.531 & \(n_{3-5}=1\), \(n_{4-6}=1\) & No compensated lines \\ \cline{1-1}  & & & & 3 & 15.77 & 36.045 & \(n_{4-6}=1\) & \(n_{4-6}=1\) (type 1) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of results for the Garver system (multistage TNEP).
## 3 Fundamental Technologies for RegTech
### Applications  
The combination of blockchain and IoT can provide a powerful solution for compliance management, enabling companies to monitor and enforce compliance requirements securely, transparently, and efficiently. Some ways in which blockchain and IoT can be used in traceability and quality assurance are:  
* _Supply chain management_: IoT devices can be used to collect data from physical assets and locations, such as factories or supply-chain nodes. These data can be stored on a blockchain, creating a secure and transparent record of transactions [57]. This can help to ensure compliance with regulations, such as food safety regulations, by providing an auditable trail of the movement of goods and the conditions under which they were transported.
* _Anti-Money Laundering_ (AML): Blockchain can be used to create a secure and tamper-proof record of transactions, making it easier to detect and prevent money laundering and other financial crimes. This reduces the risk of fraud and increases transparency in the financial system [53].
* _Asset tracking and management_: IoT devices can be used to track and monitor the condition of assets, such as machinery or vehicles [58; 59; 60]. Similar to the supply chain. These data can be stored on a distributed database, creating a reliable and quickly accessible record of the asset's history. This can help to ensure compliance with regulations, such as maintenance schedules or environmental standards, by providing an auditable trail of the asset's use and maintenance.
* _Identity verification_: Blockchain can be used to create a decentralized and secure identity verification system. IoT devices can be used to collect biometric data, such as fingerprints [61] or facial recognition, to verify the identity of individuals. This can help streamline financial institutions' KYC processes and reduce the risk of identity theft or fraud.
* _Product tracking and monitoring_: IoT devices can be used to monitor the quality of products in real-time, such as temperature, humidity [62; 63], or vibration. These data can also be stored on a blockchain, creating a record of the product's history. This can help identify quality issues and ensure that products meet the required quality standards.
* _Quality control and testing_: IoT devices can collect data for quality control [64] and testing processes, such as test results or inspection data, to monitor compliance with these requirements. Based on this, it can trigger automatic actions if required. This can help identify quality issues and improve the overall quality of products.
# Ohmic Heating in the Food Industry: Developments in Concepts and Applications during 2013-2020
## 4 Electrical Conductivity of Ohmic Heating  
Electrical conductivity is the most important characteristic that affects the ohmic heating process. It depends on the temperature, the dissociation of ionic bonds, and the intensity of the electric field of the food material [41]. Ohmic heating depends on the type of base material to be treated, moisture content, and ion concentration. Electrical conductivity increases linearly with the conductivity of solid and liquid foodstuffs, which are affected by temperature, voltage, and concentration. The temperature is inversely proportional to the concentration of the foodstuff. The electrical conductivity increases linearly with the water content and temperature, and solid food is more impervious to electrical conductivity [42].  
When AC passes through food, it converts electricity into heat [18]. Ionic compounds such as acids and bases can also help increase electrical conductivity, but they are few in non-polar compounds such as fats. Therefore, the value of the electrical conductivity of the material is not constant [43]. The electrical conductivity is expressed in Siemens per meter (S/m) [44]. In general, the electrical energy is transferred to different parts of the food by using ohmic heating. Heat is volumetrically produced, which gives a great opportunity to heat solid and liquid foods at the same rate [45]. Researchers observed that when heating tomato to reduce the water content, the contact area between the tomato sample and the two electrodes was decreased. The contact area can be calculated from the following equation:  
\[A=\ \frac{m_{t}}{p\ L} \tag{2}\]  
where  
\(A\): Contact area (m\({}^{2}\))  
\(m_{t}\): Mass of the sample at any time (kg)  
\(p\): Tomato density (kg/m\({}^{3}\))  
\(L\): Distance between poles (m)  
[PERSON] et al. [46] found that when making tomato paste with ohmic heating and using five different levels of electrical voltage (6-14 V/cm), the ohmic heating rate was greatly affected by electrical conductivity. In this study, a temperature measurement was varied from (26-96 \({}^{\circ}\)C), and the results showed that electrical conductivity increased linearly with increasing temperature, and there was a significant effect (\(p<0.05\)) of the voltage gradient on heating time. [PERSON] et al. [47] studied the effect of ohmic heating on the electrical conductivity of tomato samples during the concentration process. The electrical conductivity increased linearly (3.19-95.95 S/m) with the concentration, while the treatment time decreased (28.32-4.3 min) with the increase in the electrical voltage (6-14 V/cm), which led to the decrease in energy consumption that ranged between (4.63-3.05 MJ/kg).
# Numerical Study of Pressure Attenuation Effect on Tunnel Structures Subjected to Blast Loads
## 2 Experiment
### Explosion Experiment on the Pressure Reduction Module Effect
#### 2.1.2 Linear Tunnel with One-Pressure Relief Orifice Plate  
The linear tunnel was a square-section tunnel with a side length of 30 cm--the total length was 200 cm, and the orifice plate (circular orifice in diameter of 12 cm) was mounted at 127 cm away from the tunnel portal. The orifice plate specimen was designed by reducing the scale of U.S. Force regulation UFC 3-340-01 [13] by 2.5 times. The pressure transducers were mounted on the specimen sidewall and at 2, 30, 90, and 170 cm away from the tunnel portal. In order to know the pressure reduction effects under different quantities of explosives, we used five quantities of C-4 explosive (100, 150, 200, 250, and 350 g), and the explosive was hung at 30 cm aboveground and detonated at 60 cm away from the tunnel portal. The experimental configuration and specimen are shown in Figure 2.
###### Abstract  
Although constructed wetlands (CWs) are a viable solution for wastewater treatment, substrate selection significantly affects their performance. This study evaluated the adsorption behavior of ammonium and orthophosphate on natural zeolite (coarse- and fine-grained) and coarse gravel using kinetic and isotherm experiments. Coarse materials are intended for use as filler media in CWs to address problems such as clogging. Ammonium removal due to adsorption reached 96.20% and 96.49% for coarse and fine zeolite, respectively, and 16.84% for gravel. For orthophosphate, the removal was 11.46% and 12.81% for coarse and fine zeolite, respectively, and 6.70% for gravel. Kinetic analysis showed that the adsorption of both nutrients followed the pseudo-second-order model. Zeolite exhibited high ammonium adsorption capacities (181.87 and 174.23 mg/kg), with granulocyte showing minimal effect. The orthophosphate adsorption capacities were lower (11.76 and 12.35 mg/kg for zeolite; 6.44 mg/kg for gravel). Isotherm modeling indicated that ammonium adsorption fitted better to the [PERSON] model (monolayer adsorption), while orthophosphate followed the Freundlich model (heterogeneous surface adsorption). Zeolite adsorbed six times more ammonium and twice as much phosphate as gravel. These findings suggest that natural zeolite is an effective and sustainable CW substrate, enhancing nutrient removal and serving as an economical and environmentally friendly alternative to traditional filler media.  
ammonium; phosphate; zeolite; gravel; adsorption; isotherm; kinetics Article
## 4 Results
### Results CWRU Dataset  
In general, the results on the CWRU dataset in Table 5 clearly show that in almost every case the deep learning applications outperform traditional ML approaches, therefore emphasizing on the power of automated learning approaches in fault detection and condition monitoring. Overall, _Ada-WMCNN_ shows the best performance on the different experimental settings suggesting that using a multichannel structure is particularly useful for the _first_ wide-kernel layer. However, compared to the _A-WMCNN_, the margins are really close. Therefore, choosing a multichannel approach seems to be well-suited for the task at hand. In that case, one should look at the amount of computational resources needed for the model, which is significantly less for the _Ada-WMCNN_ due to its fewer trainable parameters. This model merges the separate inputs in an earlier stage, compared to the _A-WMCNN_, resulting in less convolutional operations. In addition, the results for all models demonstrate that lowering the amount of training data will almost always result in a lower performance on the test set, especially for the more complicated classification tasks e.g., with all types of motor speed (RPM). These results clearly indicate the importance of sufficient training samples for fault detection and condition monitoring. For this particular dataset (CWRU), we expected this behavior due \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Grid Search Range** \\ \hline
**K-NN** & K & 1–20 \\  & Weight Options & Uniform or Distance \\ \hline
**RF** & Nr of Estimators & 100–1000 (100 per step) \\  & Feauture Estimation & Square Root or Log2 \\ \hline
**SVM** & C & 10–40 (5 per step) \\  & Gamma & 0.0001, 0.001 \\  & Kernel & Linear or Radial Basis Function \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of parameter settings used in grid search optimization for every ML model using Skalearn package.
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 6 Discussion  
### Strike-Slip Fault within the Craton Identification Workflow  
The accurate characterization of seismic datasets in deep cratonic basins with strike-slip faults is a challenging task. This difficulty arises from the steep fault sections and closely spaced faults, which make it challenging to identify smaller faults. Furthermore, the misalignment of rock layers on either side of the fault and substantial lateral variations add complexity to the precise imaging of the target layer in seismic datasets. To enhance the signal-to-noise ratio and spatial resolution, principal component filtering, construction-oriented filtering, and fault-focused imaging were employed in this study to protect low-frequency effective signals and extract residual effective signals from noise. Despite achieving a high degree of in-phase axis continuity in the seismic dataset following quality control, the intricate structure and pronounced heterogeneity within the deep cratonic basin make it challenging to pinpoint breakpoints and fault locations. This complicates the direct utilization of seismic datasets for structural interpretation and reservoir prediction. To overcome these challenges, seismic attributes, encompassing parameters like amplitude, phase, frequency, continuity, and morphology, provide a quantitative foundation for discerning geological features and assisting in fault interpretation across various scales. Nonetheless, when we identify and compare multi-attribute strike-slip faults, it becomes clear that traditional attributes have limitations in effectively depicting faults within the craton basin. These limitations are especially pronounced in the deep carbonate strata experiencing intricate tectonic transformations, resulting in less accurate characterizations of the strike-slip faults.  
Recognizing the difficulties involved in pinpointing strike-slip faults within a cratonic basin, this study introduces a hierarchical identification approach. It classifies the faults into primary and subsidiary categories. Both outcrop and seismic dataset analyses validate the prevalence of strike-slip faults, which play a pivotal role in shaping fault combinations and influencing reservoir development. The high-angle development of the main fault section, large fault distances, strong transverse and longitudinal connections, and significant deformation differences on both sides of the local section allow most conventional seismic attributes to accurately locate the main fault. Therefore, a synthesis of various attributes can determine the development location of the main fault. Identifying branch faults is a challenge for many traditional attributes due to the presence of brief faults and closely spaced faults, and their dense arrangement, resulting in either detection issues or a high number of false positives. Branch faults play a pivotal role in oil and gas development and accumulation, making the identification of craton strike-slip faults a crucial and complex research area, with the new attribute being the focus of investigations.
# Revolution or Evolution? Technical Requirements and Considerations towards 6G Mobile Communications
## 7 Challenges of 6G
### Network Security  
As 6G would be extensively used to connect numerous mobile devices, smart devices in automation, AI, XR, satellites, and smart cities, the capacity of security techniques for 5G would be inadequate for 6G. To establish reliable network security for 6G, innovative cryptographic techniques, integrated network security techniques, and physical layer security techniques are necessary [3], particularly at lower cost and complexity without compromising network security.
## 2 Materials and Methods
### Thermogravimetric Analysis  
TGA was performed on powdery samples taken from the pre-treated slices of each test series of mortar prisms. To obtain these powders, the mortar prism slices were mechanically crushed until all material could pass a sieve with a 63 \(\upmu\)m mesh size. Per mortar mix design and carbonation condition, three powdery samples of around 50 mg were heated from 20 \({}^{\circ}\)C to 1100 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min under an inert nitrogen atmosphere in a Netzsch Sta 449 F3 Jupiter TGA apparatus. The recorded mass change as a function of temperature was analyzed in Netzsch-Proteus Analysis software ([PERSON], 2014) (see Appendix A, Figure A2). The main region of interest of the obtained thermal decomposition curves lies between 600 \({}^{\circ}\)C and 800 \({}^{\circ}\)C which corresponds with the thermal decomposition of calcium carbonate (CaCO\({}_{3}\)) to CaO and CO\({}_{2}\) cf. [PERSON] et al. (2013). The mass loss recorded in that region was expressed in % m/m relative to the residual mass of the powdery sample at 1100 \({}^{\circ}\)C recalculated relative to the estimated binder portion of this residual mass. It should be noted that this mass loss was corrected for the concurrent dehydration of other hydrated compounds. This was completed using the tangential method of [PERSON] (2013). For the quantification of the CaCO\({}_{3}\) content, Equation (2) was used.  
\[\%\ \text{m/m CaCO}_{3}=\Big{(}\text{WL}_{\text{CaCO${}_{3}$ exposed}}-\text{WL}_{\text{CaCO${}_{3}$ original CO${}_{2}$}}\Big{)}.\frac{\text{MW}_{\text{CaCO${}_{2}$}}}{\text{MW}_{\text{CO${}_{2}$}}} \tag{2}\]with WLCaCO3 exposed, mass loss attributed to decarbonation, WLCaCO3 original CO2, mass loss due to decarbonation of the original cement and fly ash (see Table 1: CO2), MWCaCO3, molecular weight of CaCO3 (100 g/mol) and MWCO2, molecular weight of CO2 (44 g/mol).