esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Method
### Data Reduction  
The average heat transfer rate \(Q_{\text{ave}}\) is adapted to calculate the overall heat transfer coefficient based on outer surface area \(A_{\text{o}}\) by using the logarithmic mean temperature difference (_LMTD_).  
\[U_{\text{o}}=\frac{Q_{\text{ave}}}{A_{\text{o}}\cdot F_{\text{T}}\cdot LMTD} \tag{1}\]  
\[LMTD=\frac{(T_{\text{s,in}}-T_{\text{t,out}})-(T_{\text{s,out}}-T_{\text{t,in}} )}{\ln[(T_{\text{s,in}}-T_{\text{t,out}})/(T_{\text{s,out}}-T_{\text{t,in}})]} \tag{2}\]  
where \(F_{\text{T}}\) is correction factor based on one shell pass and four tube passes heat exchanger design, 0.89.  
The tested heat exchanger is fresh new, thus the influence of fouling resistance is neglected. The shell-side heat transfer coefficient can be obtained by thermal-resistance separation method:  
\[h_{\text{o}}=\left(\frac{1}{U_{\text{o}}}-\frac{d_{\text{o}}}{2\lambda_{\text {wall}}}\ln\frac{d_{\text{eo}}}{d_{\text{ei}}}-\frac{A_{\text{o}}}{A_{\text{i }}}\frac{1}{h_{\text{i}}}\right)^{-1} \tag{3}\]  
where \(\lambda_{\text{wall}}\) is the thermal conductivity of tube wall made from stainless steel, 16.2 W\(\cdot\)m\({}^{-1}\cdot\)K\({}^{-1}\).  
Depending on the shell-side heat transfer coefficient ho and pressure drop \(\Delta P_{\text{o}}\), the shell-side Nusselt number \(Nu_{\text{o}}\), friction factor \(f_{\text{o}}\) and Euler number \(Eu_{\text{o}}\) can be calculated from the following equations [28]:  
\[Nu_{\text{o}}=\frac{h_{\text{o}}d_{\text{eo}}}{\lambda_{\text{f}}} \tag{4}\]  
\[f_{\text{o}}=\frac{\Delta P_{\text{o}}}{2\rho_{\text{f}}u_{\text{max}}^{2}} \cdot\frac{B}{I} \tag{5}\]  
\[Eu_{\text{o}}=\frac{\Delta P_{\text{o}}}{\rho_{\text{f}}u_{\text{max}}^{2}} \tag{6}\]  
where the equivalent inside and outside diameter of the tube, \(d_{\text{ei}}\) and \(d_{\text{eo}}\), and the maximum velocity in shell side \(u_{\text{max}}\) can be obtained from [28].  
Figure 1: Schematic diagram of heat transfer experimental set-up.  
The uncertainties of overall heat transfer coefficient \(U_{\rm o}\), Nusselt number \(Nu_{\rm o}\), friction factor \(f_{\rm o}\) and Euler number \(Eu_{\rm o}\) were estimated by the method suggested by [PERSON] and [PERSON] [29] as follow and turned out to be less than 3.7%, 4.2%, 4.8% and 4.3%, respectively.  
\[W_{R}=\sqrt{\left(\frac{\partial R}{\partial x_{1}}W_{x1}\right)^{2}+\left( \frac{\partial R}{\partial x_{2}}W_{x2}\right)^{2}+\dots+\left(\frac{\partial R }{\partial x_{n}}W_{xn}\right)^{2}} \tag{7}\]  
where \(R\) and \(x_{n}\) are the variable affecting the results of \(R\). \(W_{\rm R}\) is the contribution to the uncertainty in the results from \(x_{n}\).
## 3 Results  
### System Performance Evaluation  
To comprehensively evaluate the proposed AI-integrated fire suppression platform, a series of controlled fire experiments were conducted under realistic indoor conditions, as detailed in Section 2.5. Key performance indicators included fire detection latency, ventilation response time, suppression effectiveness, false alarm incidence, and system robustness under variable environmental conditions. The results presented in this section demonstrate the system's ability to meet critical operational requirements for real-time fire mitigation in smart building environments. The following subsections provide detailed quantitative results for each major subsystem, highlighting the platform's consistency, accuracy, and resilience across diverse fire scenarios.
## 3 Materials and Methods
### Proposed ATM System Design and Implementation
#### 3.3.1 Vehicle Location Tracking  
The proposed ATM system helps choose those routes that provide higher precision. The model is validated for its performance with the benchmark's lower bound precision value. Still, suppose the proposed model generates the desired precision for the lower bound. In that case, this strongly implies there are good effective routes, and all the other lesser communication routes are removed. However, if the lower bound is more than the predicted precision rate, there are not enough routes. The critical paths for effective vehicle localization are further added to the set. Figure 4 displays the functioning model of the proposed vehicle location tracking system module. In the first phase, data are collected using the sensor and camera devices. The capture of data by sensor and camera and the preprocessing of those data are vital components in ITM. The missing value estimation methods are used in the preprocessing data phase [53]. The processing method processes these collected data and later applies the training method to train the dataset. The vehicle's exact location and traffic details are collected.  
Figure 3: Layered architecture of the proposed ATM model.  
The feature clustering is achieved by creating a graph to prevent it. The nodes represent feature paths, the edges represent path clustering interactions, and the network nodes (feature groups) represent vehicle observations [54]. For each feature and attribute at a time interval (\(T_{i}\)):  
* Step 1--Features identified at a time interval (\(T_{i}\)) for the frame (\(F_{i}\)) are picked and monitored for a threshold number of frames, if the expressed cumulative personal motion is sufficiently massive. Almost every newly formed feature that is extracted is linked to the presently recorded characteristics inside an Euclidean distance minimum.
* Step 2--The distance (\(Dis_{i,j}\)) between all presently monitored sets of linked functionalities (\(Lf_{i,j}\)) is approximated, and the upper and lower limits intervals are revised. The \(D_{seg}\) represents the value of the feature segmentation threshold. The linked vehicles' characteristics can be defined as mentioned in (1). \[[Max\ T_{i}d_{\bar{\jmath}}\ (T_{i})-Min\ T_{i}d_{\bar{\jmath}}(T_{i})]>(D_{seg})\] (1)
* Step 3--The graph's linked features are discovered. Each related component, i.e., pair of attribute paths, represents a vehicle observation. Suppose all of the functionalities that comprise a factor are no longer recorded. The attributes are eliminated from the graph, and the vehicle hypothesis' attributes (speed vector, centroid position, and vehicle size) are calculated.
# Incorporation of Torsion Springs in a Knee Exoskeleton for Stance Phase Correction of Crouch Gait
## 3 Results  
### Passive Correction (Torsion Spring Only)  
For the normal stance phase of the gait cycle, the maxima and minima occur at 14% and 40%, respectively. As seen in Figure 9a, the maximum moment generated in the stance phase is 212,194.5 Nmm, and the minimum moment generated is 177,850.8 Nmm. The incorporation of torsion springs of 20 k Nmm/deg showed an overall reduction in the moments generated across all crouch gaits. CG2 (red curve) and CG4 (purple curve) showed the greatest overall improvements in the stance phase of the gait cycle, in terms of trend. The CG2 maximum stance phase moment occurred at 12% at 118,782.2 Nmm, while the minimum occurred at 36% at 106,115.8 Nmm. The CG4 maximum stance phase moment occurred at 12% with 93,155.4 Nmm, and the minimum occurred at 40%, at 75,131.9.  
Torsion springs at 30 k Nmm/deg significantly reduced moments in the stance phase in all crouch gaits except for CG 1 (blue curve) in Figure 9b. The peak moments trended toward the unimpaired (normal) gait curve in CG2 (red curve) and CG4 (purple curve). CG2 showed peak stance phase moments at 12% with 140,307.0 Nmm, and 36% at 127,834.0. The peak moments in CG4 occurred at 12% at 154,915.8 Nmm, and at 40%, at 105,117.9 Nmm. All crouch gaits, regardless of spring stiffness, require improvement in the swing phase of the gait cycle. A significant adjustment of peak moments and their occurrences in the stance phase of the gait cycle across all gaits are noticeable with both the 20 k Nmm/deg and 30 k Nmm/deg torsion springs when compared to each uncorrected crouch gait.  
Figure 8: Graphs of (**a**) the moments generated in the knee throughout the gait cycle; (**b**) total active and passive fiber forces generated in the knee extensors.  
Figure 9: Graphs of the moments generated in the knee throughout the gait cycle, using: (**a**) 20 k Nmm/deg torsion springs and (**b**) 30 k Nmm/deg torsion springs.
## 3 Results and Discussion
### Testing the ANN against the Full 3D Simulation Results

Table 1: The mean and maximum values of the relative error. Figure 4: Plot of ANN calculated log\({}_{10}\)(harmonic yield) against the values obtained with the complete 3D model for the following data subsets: (**a**) training, (**b**) validation, (**c**) testing and (**d**) overall data. macroscopic effects like propagation, phase matching, etc., then we can understand that the potential of a good prediction method goes far beyond the capabilities of an interpolation.
## 2 Model Setup  
DNA hybridization is another sensing application of a functionalized surface in the microchannel. An experimental device for the bio-recognition of DNA is used as the base case study (Case A-01). The experimental setup corresponds to the model in Tables 1 and 2--this microchamber comprising four reactive surfaces. The buffer fluid carries DNA strands with 15 bp (basis pairs) and goes through the microchamber, and passes the functionalized surfaces. The flow is on for the first 50 min (\(t_{1}\)) and then it is stopped for the next 100 min (\(t_{0}\)). Dimension of the microchamber, the buffer fluid conditions, and the hybridization properties are presented in Table 1.  
The primary purpose of this bioch is the analysis and recognition of DNA strands. Therefore, the detection process should be fast, reliable, and sensitive. In addition, the design must be suitable for simultaneous and parallel recognition. With this aim, three other types of designs are applied to the original case (A-01). Details of these cases are presented in Tables 2 and 3. In the first design, the flow volume on top of the functionalized surfaces is reduced to improve the sensing performance and reducing the detection time by applying a deformation in the geometry (cases H-02 and H-03). In the second design, a second outlet is added to the setup to improve one of the functionalized surfaces (cases S-04 and S-05). In the last design, the efficiency is enhanced with the help of electroosmotic flow (cases E-06 and E-07).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sign** & **Name** & **Value** \\ \hline bp & Number of essential pairs DNA strands & 15 \\ \(D\) & Diffusion coefficient (m/s) & \(7.5\times 10^{-10}\) \\ \(c_{0}\) & Inlet Concentration of targets or analytes (mol/m\({}^{3}\)) & \(4.6\times 10^{-6}\) \\ \(k_{on}\) & Adsorption coefficient at the wall (m\({}^{3}\)/(s-mol)) & \(125\) \\ \(k_{off}\) & Desorption coefficient at the wall or elution (1/s) & \(10^{-5}\) \\ \(b_{m}\) & Initial concentration in available hybridization sites (mol/m\({}^{2}\)) & \(9.064\times 10^{-8}\) \\ \(V_{av}\) & Average flow velocity (m/s) & \(10^{-3}\) \\ \(\sigma_{w}\) & The conductivity of the ionic solution (S/m) & \(1.1845\times 10^{-1}\) \\ \(\varepsilon_{r}\) & The relative permittivity of the fluid & 80.2 \\ \(\zeta\) & Zeta potential (V) & \(-0.1\) \\ \(V_{0}\) & The maximum value of AC potential (V) & 0.1 \\ \(\omega\) & Angular frequency of AC potential (Hz) & 50.265 \\ \(P_{2}\) & Pressure in the second outlet for case S-05 (pa) & \(-2\) \\ \(H\) & Height of the microchamber (m) & \(10^{-3}\) \\ \(H_{s}\) & Height of the volume above the biosensor for case H-03 (m) & \(2.5\times 10^{-4}\) \\ \(L\) & Length of the microchamber (m) & \(10^{-2}\) \\ \(W\) & Width of the micro (m) & \(10^{-2}\) \\ \(W_{in}\) & Width of the inlet (m) & \(1.25\times 10^{-3}\) \\ \(W_{out}\) & Width of the outlet (m) & \(10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental properties for hybridization of short DNA strands.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
## 4 Hardware Implementation
### Interference Suppression Algorithm  
This proposed algorithm takes the input data from the output of FFT using the same settings as the GNSS receiver and the output is the output spectrum without interference and the number of pins that were removed during the mitigation process. A flowchart of the interference suppression algorithm is shown in Figure 14. The removal method works in the range of 5 to 8 MHz according to the MAX2769 chip, which is a type of GPS receiver [42]. As mentioned in Section 3.4, the frequency resolution of FFT is \(\sim\)25.4 kHz. For the GPS L1 C/A code, we considered the removal of the interference in a 3 MHz passband around the centre frequency of the down-converted spectrum. By dividing 3 MHz over the frequency resolution, we obtained the number of points across this passband, which was \(\sim\)119. So, during the mitigation process, the positive and negative ranges of the frequencies were specified. The positive range ranged from 5 to 8 MHz and the negative range ranged from \(-\)8 to \(-\)5 MHz. Then, an array was created to contain the positive and negative range of frequencies. To remove the interference, a thresholding algorithm should be applied for the suppression of the interference. This threshold is one of the popular algorithms for mitigating or removing interferences. The threshold is a value where any value of the frequency exceeds a certain threshold, which will be flagged as interference to be removed.  
One of the difficulties in the thresholding algorithm is the setting of the value of the thresholding. If the threshold is set too high, the interference passes without mitigation. If the threshold is set too low, the original data is removed along with the interference. Therefore, the determination of the threshold should be accurate and valid. The threshold was calculated by multiplying four of the median with the input data according to [40]. Then, the threshold was set based on the result. Based on the different testing scenarios, we found that it the removal of jamming signals once they start to influence the GNSS signals is a good choice as in [40]. After this, a comparison of all values of the frequencies with the median value is provided. Any value that exceeds the threshold is considered interference. Then, they can either be nulled or replaced by zero. Finally, the output contains the values after the removal of the interference. The number of excised bins is equal to the length of  
Figure 14: Flowchart of the interference suppression algorithm.  
the interferences found according to the threshold. This method works relatively well and is sufficiently fast for real-time processing.
## 2 Materials and Methods
### Optical Flow Detection and Regression Modeling
#### 2.2.1 Pyramid L-K Optical Flow Feature Extraction
To provide a more intuitive representation of the data obtained from the optical flow acquisition method, the root-mean-square (RMS) value, median value, and average optical flow parameter of the corner point displacements are utilized as the reference parameters. Figure 4 indicates that the average displacements of the corner points under various window sizes significantly exceed the median displacements in each direction. This observation demonstrates that a considerable portion of the corner point displacements are small in the optical flow calculations, resulting in low median values. Consequently, the corner point acquisition process incorporates more interfering corner points with smaller displacements, which may not correspond to the actual flow measurements. Given that the threshold value of 100 may not be universally applicable to all flow states, the implementation of a dynamic threshold corner screening method becomes necessary. The flow state is annular and contains chaotic gas-liquid flow and bubbles. A larger window size is not conducive to the optical flow collection of small bubbles. Additionally, it may decrease the precision of the optical flow, resulting in larger errors in the calculations. Figure 5 depicts the current frame picture and the corner point collection. Figure 5 illustrates the distribution of corner points for a frame in both annular and elastic flow videos, both of which were captured using a 55\"55 px window. A constant-value corner sieving method was employed, resulting in fewer corners being captured for smaller flows. It is worth noting that capturing small bubbles in the elastic and annular flows is crucial in the gas-liquid optical flow acquisition process. The size of these small bubbles is approximately 1/24 of the height of the acquisition area, corresponding to a height of 45 pixels in an overall pixel height of 1080. Considering the relative height of the top image after pyramid transformation, which is approximately 6 px, a 5\"5 px window was chosen for optical flow calculation in this study.
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 3 Results and Discussion
### Prototype of the BIS Instrumentation System
In here, Bode and Nyquist plots is carried out per single frequency, and repeated until full frequency cycle has been reached. The procedure for measuring impedance using the developed BIS system including the estimated length of time required is described as below. 1. The first step is preparing for pre-acquisition; such as determining the current value, the frequency limit, number of sampling frequencies, and voltage gain. 2. Initialization and generation of sine-voltage at one frequency by the AD9850. The time required, including providing a delay time so that the signal is stable is around 200 ms.
3. The microcontroller takes 50 pieces of \(V_{\mathrm{MAG}}\) and \(V_{\mathrm{PHS}}\) from AD8302 for single-frequency operation and directly sent to the PC. On the PC the data is averaged to get the best data. The time required is around 600 ms.
4. Calculation the value of'mag (dB)' using Equation (15) and the value of '\(\theta\) (degree)' using Equation (16). Calculation the value of complex impedance magnitude, real part and imaginary part of complex impedance using Equation (17). Next, plotting the results on the Bode and Nyquist graphs provided. The time required is around 200 ms.
5. Return (looping) to point (b), until all frequencies stated in point (a) have been completed. For example, in our experiments we used the frequency range from 100 Hz to 100 kHz, and the sample frequencies were: 100 Hz, 150 Hz, 200 Hz,..., 1 kHz, 1.5 kHz, 2 kHz,..., 10 kHz, 15 kHz, 20 kHz,... 100 kHz (there are 54 sample frequencies). The time needed to measure complex impedances at a single frequency is around 1000 ms (1 s). Thus, the time needed to get the experimental data measured in one full frequency cycle is 54 s (less than 1 min).
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 3 Multi-Objective Optimization Deployment Model
### Determining the Multi-Objective Deployment Model
#### 3.2.2 Node Utilization Model  
Due to the limited energy and processing capacity of nodes, the number of nodes is also constrained. In practical applications, to maintain the normal operation of the entire network within a specified working time, it is often necessary to minimize the number of active nodes as much as possible and extend the lifespan of sensor nodes. Node utilization rate is defined as  
\[U_{\eta}=\frac{\sum e_{i}}{N} \tag{6}\]  
where \(e_{i}\) represents the number of active and effective nodes, and \(N\) represents the total number of sensor nodes. A smaller value of \(U_{\eta}\) indicates lower node utilization, indicating that there are more sleeping nodes, thus extending the network's lifespan. Conversely, with fewer sleeping nodes, the network's operational time is shorter.
# Measurement of Visible Radiation through a _Sansevieria cylindrica_-Based \"Living Sensor\"
## 3 Discussion
### Metrological Characterization and Discussions
This condition could occur for applications such as outdoor sensing for garden monitoring, and the prevention of disease and the upkeep of the health status of other plants, or for possible low-cost, disseminnable mimetic devices or measurement systems for smart cities, environmental monitoring, and cultural heritage (i.e., indoor and outdoor microclimate measurements). In fact, in this latter scenario, the European standard, UNI 10829 [49], evinces that the range of operation of the proposed device and its resolution allow us to measure the thresholds in order to preserve the works of arts, both indoors (artificial light) and also outdoors (natural light). Focusing our attention on influences, in the case of controlled environments and indoor areas such as smart homes and museums, the temperature variations are often much lower, leading to improvements in performance and resolution. It should also be noted that the hysteresis detected does not lead to a major deterioration in performance, having measured a maximum voltage variation of 10.3 mV at 55 \({}^{\circ}\)C, which represents an implausible temperature for the aforementioned applications. The living sensor also presents an interesting insensitivity to relative humidity, in fact, hypothesizing a variation of ambient relative humidity between 30% to 90%, an output voltage variation, considering the worst case presented in Figure 7, of about 2.1 mV which represents a weak voltage variation and lower with respect to the noise level detected. Therefore, the resolution cannot be affected.
## 2 Materials and Methods
### Electrochemical Impedance Spectroscopy (EIS) and Cyclic Voltammetry (CV)  
We characterized the fabricated microelectrodes using electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). EIS and CV were performed in phosphate buffered saline (PBS) with a potentiostat/galvanostat (PGSTAT128N, Metrohm Autolab). Platinum (Pt) was used as the counter electrode (CE), and silver/silver chloride (Ag/AgCl) was used as the reference electrode (RE). The frequency range of the EIS measurements was 0.1 Hz to 10 kHz. Each electrode in the 8-channel electrode array was measured, and the average impedance and standard deviation were calculated.