esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Use-Case Definition
### Road RADAR Signature  
Road signature [27] is a crowd-sourced localization service for autonomous vehicles to detect the relative position of other vehicles and objects in their environment for accurate and reliable localization. As represented in Figure 5, the road signature is crowd-sourced  
Figure 4: GRANDPA Consensus Finalized Chain [26].  
or vehicle-sourced in the vehicle surrounding comprising lane markings, gas stations and guard rails within a decimeter range of the vehicle. The vehicle generates RADAR and video sensor data while in motion, which are collated at determined intervals via the Telematics Control Unit of the vehicle to the cloud of RADAR OEM. RADAR OEM observes all these received data of RADAR and video to extract the object details and regenerate the environment based on the data. Video and RADAR sensor data complement the rendering of a localization environment. As discussed earlier, the RADAR OEM, e.g., Continental or Bosch, integrate these data with map data to produce an updated and globally consistent map. The generated road RADAR signature high-definition resolution map, which is offered as a subscription service back to the vehicle user community, comprises three layers:  
* Localization layer: Vehicle position is determined based on its driving lane and merged with RADAR road signature, including the video localization map. Also, the object information in the vicinity is compared with the information from the other sensors to determine relative position.
* Planning Layer: This layer provides driving planning information comprising the road course, traffic sign and speed limit, including bends and gradients.
* Dynamic Layer: Traffic information, including deadlocks, construction work, maintenance or parking space availability, is provided.
## 4 Discussion
### Limitations  
XRF is known to underestimate concentrations when soil moisture content exceeds 40% [6]. We did not have an instrument that could provide rapid moisture content measurements. As a result, XRF readings taken on damp soil were noted (N = 5 for in situ, and N = 9 for ex situ-bulk). This may result in an underestimation of Hg in some samples. Due to moisture content, SHL was unable to sieve 17 samples and instead analyzed the bulk material. One solution to avoiding analyzing bulk material with the AAS could be to allow more time for soil samples to air dry in cool areas. Mercury has an evaporation rate of 0.33 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{2}\) per hour at 25 \({}^{\circ}\)C, which under the correct conditions, would allow enough time for the soil to dry without losing significant amounts of Hg [20]. The same drying method could be applied for ex situ-bulk XRF analyses. Future field activities could include the use of a soil moisture sensor for in situ analysis, noting areas where soil moisture is above the 40% threshold [6].  
The number of QA/QC samples in the false negative analysis is low for both depths: 0-2 cm (N = 12), and 2-10 cm (N = 24). This low sample count possibly skewed the false negative percentages. Collecting more QA/QC samples at this depth could reduce the false negative rate.
## 5 Experimental Results
### Multi-Objective-Based Experiments  
In this section, we compare the performance of our proposed algorithm FGGA to the basic grouping genetic algorithm (GGA) and the multi-objective memetic algorithm (MMA) of [5]. Without loss of generality, the GGA algorithm uses linear weighted sums for the three optimization objectives, and the weights of the three optimization objectives are respectively taken as \(1/3\). The comprehensive comparison experiment still uses the data of the above experiments for testing. The three sets of algorithms were run 20 times, and the best deployment plan in each set was selected for comparison. The best deployment plans obtained by the three sets of algorithms were recorded as \(\pi_{FGGA}\), \(\pi_{GGA}\), and \(\pi_{MMA}\). In order to comprehensively compare the three optimization goals, we calculate the fitness value of the solution, which is obtained according to Equations (17)-(20). The fitness value is used to weigh the benefits and drawbacks of the solution. The closer the fitness value is to one, the closer the solution is to the ideal state, and the closer it is to zero, the worse the solution.  
It can be seen from Figure 12 that the best virtual machine deployment plan obtained by the FGGA algorithm has the largest fitness value. The overall performance of the FGGA algorithm in the multi-objective optimization of power consumption, resource waste, and SLA violation rate is better than the group genetic algorithm GGA and the multi-objective scheduling algorithm MMA. A comprehensive analysis of the single-objective comparison experiment and the multi-objective comparison experiment shows that the FGGA algorithm has no outstanding effect in each single-objective optimization. Its performance is better than other algorithms in the multi-objective simultaneous optimization performance. It shows that our proposed FGGA algorithm performs well in multi-objective optimization.
## 2 Instrument Design
### Block Diagram
#### 2.1.2 Power Detector Assembly  
The power detector assembly (PDA) depicted in the block diagram (Figure 2) determines the performance of the radiometer. The PDA is symmetrical in respect to the two frequency channels implemented. The LSB and the USB outputs of the MA are fed to Planar-Doped Barrier diode detectors that are terminated resistively with 10 k\(\Omega\) for best linearity and minimum insensitivity to temperature variation. For the estimated input power range \(P_{\text{PDA}}\) (Table 1), the detectors operate well within their square-law regime. Therefore, the detector output voltage is directly proportional to \(P_{\text{PDA}}\) with a voltage sensitivity of \(>\)0.5 mV \(\upmu\)W-1.  
The detector output voltages are amplified using instrumentation amplifiers with voltage gains of approximately 850, and finally low-pass filtered with a cut-off frequency of 400 Hz at -3 dB. Buffer amplifiers (AMP4a/b) are used to drive the 16-bit analog to digital converter (ADC), operating at the nominal sample rate of 1,600 Hz. However, sampling at 800 Hz is also feasible to reduce data volume and incurs only a small loss of radiometric sensitivity. After this point, processing is carried out by the on-board Instrument Controller (IC), which is part of the temperature and power control (TPC) unit described in Section 2.1.4.
# Fragility Assessment and Reinforcement Strategies for Transmission Towers Under Extreme Wind Loads
## 4 Wind-Induced Response Under Strong Wind
### Transmission Tower Collapse Analysis  
To further investigate the failure mechanisms of the transmission tower under extreme wind conditions, Table 3 summarizes the tower's top displacement and maximum stress values under various wind speeds, specifically for a 45\({}^{\circ}\) wind direction--the most critical loading case identified earlier. The results consistently indicate that the highest stress concentrations occur in the main structural members of the third tower section. A detailed schematic of these primary material rods is provided in Figure 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wind Speeds**} & \
\begin{tabular}{c} **Tower Top** \\ **Displacement** \\ **/mm** \\ \end{tabular} &
\begin{tabular}{c} **Maximum** \\ **Stress/MPA** \\ \end{tabular} & \multirow{2}{*}{**Maximum Stress Location**} \\ \hline
25 & 256.0 & 183.0 & The third section main material \\
27 & 267.2 & 189.9 & The third section main material \\
30 & 327.5 & 220.2 & The third section main material \\
33 & 458.2 & 259.2 & The third section main material \\
34 & 562.3 & 286.5 & The third section main material \\
35 & 602.3 & 326.4 & The third section main material \\
36 & 650.9 & 347.5 & The third section main material \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transmission tower displacement and maximum stress under different wind speeds (45\({}^{\circ}\)).  
Figure 11: Results of transmission tower at different wind angle (v\({}_{10}\) = 33 m/s).  
Figure 13 presents the stress distribution in the No. 1 to No. 4 main structural rods as a function of increasing wind speed. For wind speeds below 30 m/s, the stress in these rods gradually increases, with the No. 1 rod bearing the greatest load along the tower leg. However, as wind speeds exceed 30 m/s, the stress distribution becomes more uniform across the No. 1, No. 2, and No. 4 rods, indicating a redistribution of internal forces. A critical threshold is observed at a wind speed of 37 m/s, where a sudden surge in top displacement occurs, as illustrated in Figure 14. This marks the onset of large-scale deformation and structural instability. At this wind speed, the No. 1, No. 2, and No. 4 rods simultaneously enter the plastic deformation phase, as shown in Figure 15, initiating yielding and leading to plastic instability in the tower's primary load-bearing elements. This transition results in a rapid loss of structural stiffness and load-bearing capacity, ultimately triggering the collapse of the transmission tower.  
Figure 14: Variation curves of tower top displacement.  
Figure 12: Position of No.1-No.4 rod in Section 3.
# Review of Responsiveness and Sustainable Concepts in Cellular Manufacturing Systems
## 3 Review of the Responsiveness Approaches
### Robust Design  
Robust design approaches attempt to design cellular manufacturing systems that are able to handle dynamic changes, by taking these changing conditions into account in the model's design.  
[PERSON] et al. [37] in their paper highlighted how the efficient execution of a dynamic manufacturing system depends on various factors. They introduced human errors in the design of the cells as a factor. The proposed approach for cells in a dynamic environment for several consecutive periods included three objectives: maximizing the grouping effectiveness, minimizing the total costs, and minimizing total non-interest workers.  
[PERSON] et al. [19] addressed the problem of cell formation and how the design of the layout of the cell formed. They proposed a semi-robust cellular approach, which is able to cope with the continuous change of product mix and demand. The layout of the facility does not change from one period to another, but rather the locations of the cell pick-up/drop-off points change. The mathematical model developed to support the proposed approach is a multi-objective mathematical programming model. A genetic algorithm is necessary to solve the complex mathematical model obtained.  
[PERSON] et al. [38] studied the design of cellular manufacturing systems distributed in multiple plants in dynamic conditions developing a mixed-integer nonlinear programming model. The mathematical model included different objectives: the sale revenue and total costs of machine operation, machine overhead, inter-cell material handling, inventory holding, outsourcing, machine installation/uninstallation, products transportation, dispersing machines, establishing plants, and forming cells. The model obtained is NP-hard, therefore, genetic algorithm and grey wolf optimization were proposed to solve the problem.  
[PERSON] et al. [39] studied an ant-based algorithm that addresses the formation of product/part families and remanufacturing cells. The numerical results highlighted the  
The analysis of the responsiveness works proposed in the literature highlights the following issues. The mathematical models proposed to support the dynamic reconfiguration of the cellular manufacturing systems are characterized by relevant computational complexities that limit their potential application in real industrial cases where the number of machines, jobs, and equipment are significant. This reason leads to the proposal of alternative methods, such as the genetic algorithm, that can be relevant in terms of solution time. The introduction of reconfigurable machines allows the cellular manufacturing systems to be scalable, reducing time and costs to reconfigure and adapt the cells to the dynamic changes of demand. Some works proposed hybrid cells that include dedicated, flexible, and reconfigurable machines. This allows for the minimization of the costs of the reconfiguration. Virtual cells can be a promising approach, but the literature did not explore their potential in recent years with the development of industry 4.0.
# Long-Range Acoustic Communication Using Differential Chirp Spread Spectrum
## 5 Experiment
### Sea Trial  
A sea experiment was conducted in October 2018 in the East Sea, South Korea. The experimental configuration was constructed as shown in Figure 9, and the experiment was conducted from a vertical line array (VLA) of about 90 km. The water depth was \(\sim\)1500 m. The experimental configurations are shown in Table 5. The source, a Neptune T-161 projector, was located \(\sim\)200 m below the sea surface and transmitted signals over the 1.8 kHz centered frequency [31]. The 16-element receiving hydrophones were located 179-221 m away under a sea surface buoy. This is a vertical line array (VLA) with an inter-element spacing of 2 m. The collected acoustic data, as well as some of the collected oceanographic data, were self-recorded, providing the VLA coordinates by continuous real-time monitoring via a telemetry buoy; this is represented in Figure 9a. The map in Figure 9b shows the geography of the East Sea. In Figure 9b, the circle is the receiver, the star shape represents the position of the transmitter and the line drawing between the star and circle is the chip route measured by a global positioning system (GPS).  
Figure 8: The spectrogram (**a**) of the transmitted signal; (**b**) received signal on the 1 st experiment; (**c**) received signal on the 2 nd experiment; (**d**) received signal on the 3 rd experiment.  
Before checking the results of the sea experiment, the channel was measured. The CIR is shown in Figure 10. The channel has one big multipath, which was reached before the main path. The delay was about 50 ms and there was about \(-3.24\) Hz Doppler.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline Sampling frequency & 16,384 Hz & LFM interval & 5 s \\ Carrier frequency & 1900 Hz & Guard interval & - \\ Data rate & 4 bps & Number of bits & 114 bits \\ Bandwidth & 200 Hz & Preamble & 255 bits \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters for the sea experiment.  
Figure 9: Sea experiment configuration: (**a**) configuration view from the side; (**b**) configuration view from above.  
The spectrogram of the signal received from the sea experiment is shown in Figure 11. In the sea experiment, the SNR was measured as very low, about \(-6.51\) dB. The reason that the SNR in the channel impulse response looks better than that of the lake experiment is the effect of the increase in the matched filter output value according to the length of the LFM. In the experiment, the BER for the modified conventional method was \(1.83\times 10^{-4}\), and the BER for the proposed method was \(0\) BER. It was thereby confirmed that the proposed method for long-distance communication shows good improvement on performance.  
Figure 11: The spectrogram of (**a**) the transmitted signal and (**b**) the received signal.  
Figure 10: The sea-measured channel at \(90\) km: (**a**) CIR; (**b**) delay-Doppler spread spectrum; (**c**) power delay profile; (**d**) Doppler power spectrum.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 3 Speech Databases and the Extraction of Features  
### Captured Emirati-Accent Speech Corpus  
This task involved the communication of the Emirati-emphasized speech database (Arabic database) by 30 local Emirati speakers from each gender, between the ages of 14 and 55 years. Eight Emirati expressions, which are widely spoken by the UAE public, were uttered by speakers. Eight sentences that would be spoken in six stressful talking conditions were used. These talking conditions were \"neutral, angry, slow, loud, soft, and fast\". The tone of each talking condition was expressed nine times, inserting 2 to 5 s gaps between utterances. The speakers were asked to speak these sentences on the spot, not giving them the chance to practice the sentences, in order to prevent fake outcomes. The overall number of recorded utterances was 7560 ((15 speakers \(\times\) 2 genders \(\times\) first 4 sentences \(\times\) 9 duplicate sentences in the neutral environment for the training session) + (15 speakers \(\times\) 2 genders \(\times\) last 4 sentences \(\times\) 9 replications/sentence \(\times\) 6 talking conditions for the testing session). For more details, please check Section 4.1 [30].
## 4 Results
### Network Model Analysis
The gain over topology \(TP_{1}\) is greater than the one observed for \(TP_{4}\). The reason for this behavior is that lower dense topologies offer fewer routing alternatives, and so the proposed algorithm is not able to balance the accumulated exposure. Furthermore, the results also evince that there is not a clear impact of the number of traffic flows (simultaneous number of sources). Taking into account that the exposure induced by the whole network should be directly related to the overall transmitted power, it is interesting to ascertain the performance of the proposed algorithm in terms of the overall transmitted power. This is important, especially since we are comparing it to a solution that seeks exactly the minimization of this parameter. Figure 9 shows the average value of the overall required transmission power. It is worth recalling that, in the case of the _MinPower_ solution, the transmitted power does not change during the experiment, since the routes remain unchanged, while this might not be the case in the _EMFaware_ algorithm. In this sense, when a change of route is enforced in the proposed solution, the required transmission power might be different. The results in Figure 9 evince that, although there is a slight penalization regarding the overall transmitted power, the increase is rather small, and it even becomes negligible for the scenarios with lower densities (\(TP_{3}\) and \(TP_{4}\)). For both solutions, we can clearly see that the smaller the density, the higher the required power, due to the longer distances that would be required in order to connect the source nodes to one of the available gateways. Figure 8: Mean value of the reached exposure per experiment. We represent the values yielded by the _EMFaware_ and _MinPower_ algorithms with markers and bars, respectively. Figure 7: Joint probability distribution of accumulated exposure for topology \(TP_{1}\), accounting for the values obtained in every node. (**a**) Two sources. (**b**) Four sources.
## 3 Results and Discussion
### EGFP Knockout by Direct RNP Delivery  
Next, we delivered Cas9-sgRNA into cells using the NNA. HeLa cells that stably expressed _EGFP_ were used for the assay (Figure 3A). The NNA, on which RNPs were adsorbed, was moved towards the cells using an array manipulator while being observed with an inverted microscope. Following insertion, the NNA was oscillated for 1 min. Some cells did not express EGFP after 48 h of culturing (Figure 3B). Although the mock treatment of cells inserted by bare NNA showed that 4 \(\pm\) 1% (average \(\pm\) SD) of the total cells showed deficient _EGFP_ expression, non-treated reporter cells were also found to include 4% of EGFP negative cell. This indicated that the insertion of the nanoneedle had no effect on gene expression. When RNPs were delivered using the NNA, the ratio of _EGFP_-depressed cells was 32 \(\pm\) 11%. On the other hand, _EGFP_ depression efficiency of lipofection was 57 \(\pm\) 3%, which was rather high compared to that obtained for NNA. Although the same amount of RNP solution, containing 15 pmol Cas9 and 10 pmol sgRNA, was used in both delivery processes, the amount of  
Figure 2: Adsorption of Cas9-mEmGFP on the cell surface and insertion of NNA into the cells. (**A**) Confocal laser scanning microscopes (CLSM) images of HEK293 cells that were incubated with Cas9-mEmGFP with or without sgRNA. Scale bar: 50 μm. (**B**) Side view of HEK293 cells immediately following the addition of the Cas9-mEmGFP or RNP complex with sgRNA. Scale bar: 10 μm. (**C**) Side view of HEK293 cells 22 h following the addition of Cas9-mEmGFP or RNP. Scale bar: 20 μm. (**D**) Confocal XY slice image of HEK293 cells expressing DsRed2-NES for the visualization of the cytosol. The NNA was adsorbed with Cas9-mEmGFP interacting with sgRNA and inserted into HEK293 cells. Scale bar: 10 μm. (**E**) Confocal XZ cross section images of the nanoneedle insertion process into a cell. The red rectangles indicate nanoneedles inserted into the cell. Scale bar: 10 μm.  
RNP adsorbed onto the NNA surface was approximately 0.1 pmol, as stated above. We surmised that such a large difference in knockout efficiency was due to the difference in the effective amount of RNP delivered to cells.
# Spiral-Resonator-Based Frequency Reconfigurable Antenna Design for Sub-6 GHz Applications
## 2 Antenna Design
### Parametric Analysis and Equivalent Circuit
#### 2.2.2 Equivalent Circuit Model  
The impedance mismatch issue is a common challenge when using a common feed line for a reconfigurable antenna. To address this problem, an equivalent circuit model is often employed. This model offers a valuable approach for resolving the impedance mismatch by introducing a lumped element for different parts of the antenna. To determine the correct value for the lumped element, it is crucial to accurately model the antenna. In this study, detailed equivalent circuits are utilized for ON-ON and OFF-OFF states in Figure 2. Each circuit elements are calculated separately for the corresponding resonance based on Equation (4). By using Equation (4), each value is calculated and inserted into the equivalent circuit of the proposed antenna given in Figure 2.  
Based on the provided equivalent circuit models, Figure 3 outlines that the reflection coefficient of the antenna design has been analyzed and compared using two different software tools: CST and Advanced Design System (ADS). By comparing the reflection coefficients obtained from these two software tools, the performance of the antenna design is validated through equivalent circuit models of ON-ON and OFF-OFF states.
## 3 Results and Discussions
### FTIR  
Figure 3a represents the FTIR spectra of the TiO\({}_{2}\) nanoparticles in the range of 450-4000 cm\({}^{-1}\). The band between 450 cm\({}^{-1}\) and 800 cm\({}^{-1}\) was attributed to Ti-O stretching and Ti-O-Ti bending vibrations. The band at 512 cm\({}^{-1}\) and 721 cm\({}^{-1}\) confirms the formation of the TiO\({}_{2}\) nanoparticles with the anatase phase [23]. The band located at 1209 cm\({}^{-1}\) was assigned to the Ti-OH bending [24; 25]. The strong band at 1740 cm\({}^{-1}\) is due to the absorption of water molecules present in the atmosphere [26]. The weak band at 34,163 cm\({}^{-1}\) as shown in Figure 3b corresponds to the hydroxyl groups (O-H) present on the surface of the TiO\({}_{2}\) catalyst which contribute to enhancing the photocatalytic activity [23].