esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Test Validation
### Experimental Results and Discussion
#### 4.3.2 Identification in the Second Stage  
According to the identification results of the first stage, the difference in the BMC of edge-b was calculated, as shown in Figure 17. In specimen-A, measured point 3 on edge-b showed a significant difference from other points. Similarly, in specimen-B, point 4 on edge-b exhibited a prominent BMC difference. The average and standard deviation of difference in the BMC for two models were calculated; then, the damage locations were identified according to Equation (7). The results are listed in Table 7.  
When the damage severity exceeded 4%, significant changes in boundary modes and BMC allowed for the exact identification of both the damaged edges and their specific locations. It should be pointed out that the damage location can be identified through the change in the curvature of the boundary modal displacement when the damage reaches a certain severity. Whereas, in other forms of the glass curtain wall, such as the point-supported or exposed frame-supported glass curtain wall, the minimum severity that can be identified will change and needs to be further verified by experiments.
## 2 Materials and Methods
### Evaluation of the Antimicrobial Activity of Achillea millefolium Extract
#### 2.5.4 Evaluation of Biofilm Formation Inhibition  
The extracts' capacity to inhibit biofilm formation was assessed using a modified version of the protocol originally described by [PERSON] et al. [17]. Briefly, microbial suspensions were incubated in flat-bottomed 96-well microplates containing 100 \(\upmu\)L of Mueller-Hinton broth supplemented with various extract concentrations ranging between 0.25 and 16 mg/mL. Following incubation at 37 \({}^{\circ}\)C for 24 h, non-adherent cells were carefully removed, and the wells were gently rinsed with sterile distilled water. Biofilms adhering to the surface were stained using 0.1% crystal violet solution for 15 min. Excess stain was removed by washing gently with water, and the remaining bound crystal violet was solubilized using 95% ethanol with brief agitation. The absorbance of each well was quantified at a wavelength of 595 nm utilizing an Infinite M200 PRO microplate reader (Tecan, Mannedorf, Switzerland). Each experiment was performed in triplicate. The biofilm inhibition was expressed in percentage terms using the formula provided by [PERSON] [18].  
%biofilm inhibition = (control OD value \(-\) treated OD value/control OD value) \(\times\) 100
# The Utilization of Artificial Neural Network Equalizer in Optical Camera Communications +
## 6 Conclusions  
We proposed an ANN-based equalization technique for a CP 4-PAM based OCC system. An experimental setup was developed to demonstrate non-flickering communications using a single light-emitting diode with a transmission rate of \(R_{b}\) of 24.4 kbps. The quality of received signals was measured based on the eye-diagram opening, eye linearity, and the BER. We demonstrated the ability to mitigate the intersymbol interference and hence to transmit a signal with an acceptable BER (below the FEC limit) for \(N_{\text{pps}}\) of 20, and 30  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ISs Resolutions**} & \multicolumn{3}{c}{\(R_{b}\) **(bps) at \(N_{pps}\) = 26**} & \multicolumn{3}{c}{\(R_{b}\) **(bps) at \(N_{pps}\) = 20**} \\  & \multicolumn{3}{c}{**(i.e., w/o Equalization)**} & \multicolumn{3}{c}{**(i.e., with Equalization)**} \\ \cline{2-5}  & \(R_{f}\) **= 30 fps** & \(R_{f}\) **= 60 fps** & \(R_{f}\) **= 30 fps** & \(R_{f}\) **= 60 fps** \\ \hline
1200 \(\times\) 1800 & 3794 & 7588 & 5040 & 10,080 \\
1500 \(\times\) 2100 & 4486 & 8972 & 5940 & 11,880 \\
1800 \(\times\) 2400 & 5178 & 10,357 & 6840 & 13,680 \\
2100 \(\times\) 3000 & 6563 & 13,126 & 8640 & 17,280 \\
2400 \(\times\) 3000 & 6563 & 13,126 & 8640 & 17,280 \\
3300 \(\times\) 4200 & 9332 & 18,665 & 12,240 & 24,480 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effective \(R_{b}\) at different ISs resolutions at the FEC limits.  
Figure 11: The BER measurements as a function of \(N_{\text{pps}}\) for the proposed system with and without equalization and for \(T_{\text{exp}}\) of 2 ms.  
for unequalized and equalized systems, respectively. An improvement of \(\sim\)66% in the eye linearity was achieved using a single LED, and a typical commercial camera with equalization technique was achieved. The limitation of the proposed system was assessed by the system complexity, including the associative memories needed for the look-up table training data as well as the IS resolution, gap-time and exposure time, and reading time.  
Conceptualization: O.I.Y., N.B.H. and Z.G.; investigation: O.I.Y. and N.B.H.; project administration: Z.G., S.Z., L.N.A. and H.L.-M.; software: O.I.Y. and N.B.H.; writing--original draft preparation, O.I.Y.; writing--review and editing, N.B.H., Z.G., S.Z., L.N.A. and H.L.-M. All authors have read and agreed to the published version of the manuscript.  
One of the authors ([PERSON]) is funded by the Northumbria University PhD scholarship. The work is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no 764461 (VISION), and the EU COST Action on Newfocus (CA19111).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Wavelength Selection for Satellite Quantum Key Distribution
## 4 QKD Performance
### Adaptive Optics
Results from the mission include individual actuator displacements to a precision of 12 nm and correcting wavefront errors in space to a \(<\)100 nm RMS error which is similar to performance from ground measurements. The DeMi mission demonstrates operation of MEMS DMs after surviving the harsh conditions of the launch and operation in space.
## 3 Results and Discussion
### Microstructure  
Figures 12 and 13 show examples of the microstructure of the sheet metal and drawpieces as a function of the heat treatment. In all cases, the presence of precipitates was observed. In the case of the material subjected to solutionizing only, their amount was the smallest. No significant changes in the microstructure were observed as a result of the incremental forming process. Additional heat treatment after incremental forming contributed to an increase in the amount of precipitates, mainly in the case of the material deformed in the solution-heat-treated state (Figures 12b and 13b,c).  
Figure 11: Change in wall thickness of a square pyramid as a function of the tempering state of the sheet metal.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Temper Designation**} & \multicolumn{3}{c}{**Measured Wall Thickness at Selected Points, mm**} \\ \cline{2-5}  & **1 (5 mm)** & **2 (25 mm)** & **3 (50 mm)** & **Wall Thickness According to Sine Law, mm** \\ \hline O & \(1.45\) & \(1.50\) & \(1.53\) & \(0.72\) \\ W & \(1.47\) & \(1.52\) & \(1.57\) & \(0.85\) \\ T4 & \(1.47\) & \(1.54\) & \(1.60\) & \(0.88\) \\ T6 & \(1.48\) & \(1.57\) & \(1.62\) & \(0.97\) \\ \hline \end{tabular}
\end{table}
Table 8: Wall thickness values of the square pyramids at specific measurement points.  
Figure 12: Microstructure of aluminium alloy sheets: (**a**) EN AW-6082-O, (**b**) EN AW-6082-W, (**c**) EN AW-6082-T4 and (**d**) EN AW-6082-T6.  
Figure 13: Microstructure of the wall of the drawpiece made of (**a**) EN AW-6082-T6 alloy, (**b**) EN AW-6082-W alloy naturally aged after forming, (**c**) EN AW-6082-W alloy artificially aged after forming.  
The solution heat treatment was carried out to dissolve the phases containing Cu and Mg formed during the solidification process, homogenize the alloying elements and spheroidize the eutectic silicon particles. It consists in heating the alloy to the maximum dissolution temperature of the second alloying element above the solvus line and heating at this temperature for a period sufficient to allow the alloying elements not dissolved in aluminium to pass into the solid solution. With the increase in the supersaturation temperature, the solubility of the elements in the matrix increases, which increases the hardness of the alloy [46]. During ageing following solution heat treatment, coherent phases associated with the matrix are obtained, and the excess components present in the solution are precipitated. This leads to alloy strengthening. In Al-Si-Mg alloys, ageing starts with the formation of spherical Guinier-Preston zones consisting of enriched Mg and Si atoms. These zones expand and elongate into a coherent \(\beta^{\prime\prime}\) phase having a needle-like shape. The peak yield strength of the alloy increases with the increase in Mg concentration in the alloy, with the increase in the yield strength value being linear [42].
# A pH Monitoring Algorithm for Orifice Plate Culture Medium
## 3 Algorithm Principle and Result Analysis
### Analysis of HSV and pH Function Model
#### 3.2.2 Curve-Fitting Analysis between the Average of Hue and pH  
Image acquisition of the 6-hole orifice plate was carried out at different placement positions. Upon calculating the first moment of h of 6-bit orifice image, the results were sorted into holes 1-6; the calculation results of the first moment of h of the captured image are shown in Table 3.  
Figure 7: (**a**) Trend graph of the average hue in HSV color space; (**b**) trend graph of variance in HSV color space; (**c**) trend graph of slope in HSV color space.  
After fitting the above data, the resulting model formula is:  
\[\mathrm{pH}=7.567\times e^{(-0.860)\times H_{mean}} \tag{3}\]  
The fitting curve is shown in Figure 8. Since the pH value of cell culture fluid shows a tendency of step-down, \(H_{mean}\) does not change when it is in a certain range.  
The sum of squares of the errors of the corresponding points of the fitted and original data were first calculated. The closer the SSE is to 0, the better the model selection and fit, and the more successful the data prediction is. The resulting SSE was calculated as follows:  
\[\mathrm{SSE}=\sum_{i=1}^{n}w_{i}(y_{i}-\hat{y_{i}})^{2}=0.024 \tag{4}\]  
When SSE was calculated, the values of SSE were smaller. This indicated that the model selection was appropriate, and the fit was quite good.  
Next, the means of the sums of squares of the point errors corresponding to the predicted and original data are calculated. MSE was calculated as follows:  
\[\mathrm{MSE}=\frac{1}{n}\sum_{i=1}^{n}w_{i}(y_{i}-\hat{y_{i}})^{2}=0.0008 \tag{5}\]  
The MSE is small, indicating that the fit data and the original data have less deviation from corresponding points.  
Figure 8: Fitting diagram of H-Mean and pH cubic curve.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**pH**} & \multicolumn{5}{c}{\(H_{mean}\)} \\ \cline{3-8}  & & **Position 1** & **Position 2** & **Position 3** & **Position 4** & **Position 5** \\ \hline
1 & 7.03 & 0.079131 & 0.079114 & 0.079093 & 0.077901 & 0.078625 \\
2 & 7.17 & 0.069579 & 0.067355 & 0.067878 & 0.068542 & 0.066463 \\
3 & 7.36 & 0.038364 & 0.030121 & 0.038853 & 0.035652 & 0.039785 \\
4 & 7.42 & 0.023946 & 0.020722 & 0.025150 & 0.023590 & 0.026532 \\
5 & 7.46 & 0.014058 & 0.011945 & 0.015283 & 0.013090 & 0.013878 \\
6 & 7.50 & 0.008604 & 0.006950 & 0.010457 & 0.009035 & 0.010730 \\ \hline \hline \end{tabular}
\end{table}
Table 3: \(H_{mean}\) and pH value measured by pH meter of orifice plate images of 6-well liquid, collected at different positions.
## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Stand-Alone Applications of Satellite Remote Sensing for Infrastructure Monitoring
#### 5.3.1 Roadways
Furthermore, [PERSON] et al. [88] demonstrated through an InSAR cost/benefit analysis that the use of SAR-based approaches can improve the effectiveness of the overall monitoring system and reduce total costs involved. The authors conclude that the PSI outcomes are viable indicators to predict the condition of road infrastructure assets. More recently, [PERSON] et al. [89] demonstrated the feasibility of a fully automated GIS analysis of PSI time-series to detect critical PS points located on motorways at the network level. A Sentinel-1 dataset from 2016 to 2019 was used to analyse the Los Angeles highway and freeway network, whereas historical ERS/Envisat datasets (from 1992 and 2010), COSMO-SkyMed datasets (from 2008 to 2014) and Sentinel-1 datasets (from 2014 to 2020) were utilised to analyse the Italian motorway network. In general, these studies demonstrate that the PSI technique can be an effective monitoring tool for roadways and highways. It has been noticed that if the PSs density is not high, or if stable reflectors are not recognised on the road asset even with high-resolution data, the MT-InSAR results could still be used by asset managers to prioritise assets in case ground-based monitoring systems are installed. This application is currently under development, and several new processing techniques, such as the Quasi-PS technique [72] or the distributed scatterers, have proven effective to detect partially coherent PS points. The installation of corner reflectors, or artificial persistent scatterers, is an alternative solution to tackle this problem in infrastructure with a scarcity of PSs [91]. Corner reflectors are cheaper to utilise than on-site and ground-based monitoring equipment, and they can be installed in strategic sections of the structure to produce effective reflections. This allows more efficient recognition of these targets as PSs during the processing phases.
## 4 Experiments and Results
### Influence of Heat Waves on DIC Measurement Results  
Then, the screw bar behind the specimen was rotated and the in-plane displacement to the disc was loaded. DIC was used to measure the in-plane displacement of the disc. The measurement results with and without the heat waves were compared. First, in the absence of the heat source, the micrometer screw bar behind the specimen was rotated to make a slight rotation of the disc. The angle of the rotation was 0.2 degrees. The image before the rotation is the reference image, and the rotated specimen image is correlated with the reference image. The measured displacement field is shown in Figure 12.  
The components of the displacement field in the U and V directions are shown in Figure 13. The displacement fields are expressed in pixel values to facilitate the precision analysis.  
Next, the image taken without the heat source and rotation was used as the reference image. The specimen was kept still, the heat source turned on, and the temperature of the hot plate adjusted to 300 \({}^{\circ}\)C. After the environment was stable, the micrometer screw bar was rotated to rotate the disc, and the angle of the rotation was still 0.2 degrees. Then, 100 images at a frequency of 1 Hz were taken, the result of the DIC measurement of the images taken at the 50 th second is shown in Figure 14. The components of the displacement field in the U and V directions are shown in Figure 15. It can be seen that under the influence of heat waves, the measurement results of the DIC were severely distorted.  
Figure 11: The average main distortion at each temperature.  
Figure 12: Displacement field measured without the influence of heat waves.  
The 100 displacement fields were time-averaged to remove random disturbances, and the result is shown in Figure 16. It can be seen that the result improved, but it is still not ideal due to the main disturbance not having been removed.  
Figure 14: Displacement field measured with the influence of heat waves.  
Figure 13: Displacement fields obtained from the DIC measurement without the influence of heat waves: (**a**) Component in U direction; (**b**) component in V direction.  
Figure 15: Displacement fields obtained from DIC measurement with the influence of heat waves: (**a**) Component in U direction; (**b**) component in V direction.
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 4 Non-Linear Time-History Analysis and Performance of DDIS
### Seismic Performance of Structure Equipped with DDIS  
The time-domain dynamic performance of structures equipped with the DDIS and displacement-dependent damper (as a special case of the DDIS without the inerter) are investigated by applying the DDIS and displacement-dependent damper to the SDOF structure described in Section 3.2.  
The values of \(\kappa\) and \(\lambda\) of the damper and DDIS and the value of \(\mu\) of the DDIS are all set to 0.50. A suite of ground motions, 10 LS waves, SS waves, and HS waves are generated using the Kanai-Tajimi spectrum mentioned in Section 4.1. In addition, El Centro 1940 N-S record is employed as a natural ground excitation. The peak ground acceleration of the input ground motion is scaled to a common value of 0.1 g. The average \(\gamma_{Dis}\), \(\gamma_{Acc}\), and \(\gamma_{E}\) determined by the analyses for the artificial records and El Centro ground motion are presented in Table 1. The results show that for different types of excitations, 10% to 15% of the vibrational energy, as determined by \(\gamma_{E}\), is filtered through the DDIS to the primary structure. This indicates that the DDIS considerably reduces the amount of energy that the primary structure needs to dissipate. Furthermore, the displacement and acceleration of  
Figure 14: Hysteretic curve of DDIS and VDIS under El Centro in the early stage of the seismic response.  
Figure 13: Displacement responses of the structures with the DDIS and velocity-dependent damping inerter system (VDIS) under (**a**) El Centro, (**b**) low-frequency seismic (LS) excitation, (**c**) severest seismic (SS) excitation, and (**d**) high-frequency seismic (HS) excitation.  
the DDIS-fitted structure are suppressed to a low level (reduction ratios ranging from 30% to 60% compared to the uncontrolled case).  
Considering the SS and El Centro waves as examples, the displacement response curves of the structures fitted with the displacement-dependent damper and DDIS are shown in Figure 15. The DDIS more effectively reduces the structural displacement compared with the displacement-dependent damper with a DDE identical to that used in the system. To explain the greater displacement reduction ability of the DDIS from the perspective of energy dissipation, Figure 16 shows the hysteresis curves of the DDEs of the displacement-dependent damper and DDIS. The values of the corresponding DDE deformation enhancement ratio, given by  
\[\rho=\frac{\max(\text{DDE deformation})}{\max(\text{displacement of strucuture with DDIS})},\]  
are indicated in the figure. In agreement with the findings in Section 3.2.2, the DDE deformation of the DDIS is over 60% larger than the structural displacement. The amplified deformation is achieved by the damping enhancement mechanism of the DDIS, in the absence of which the deformation would be equal to the structural displacement, as for the structure with the conventional displacement-dependent damper. The mechanism of the damping enhancement achieved here is similar to that of a VDIS [1].
# A Novel Approach to Wearable Image Recognition Systems to Aid Visually Impaired People
## 2 Proposed Architecture  
### System Overview  
Our proposed cloud-based recognition solution is shown in Figure 1 below.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
The proposed system (as shown in Figure 1) is a wearable device based on the cloud server for image recognition. Its sensors include a micro camera, ultrasonic sensor, and infrared sensor. The system uses the Raspberry Pi as the local processor, connecting to the cloud server via Wi-Fi or 4G network. It takes advantage of the cloud server's powerful parallel computing power and huge storage capacity. All visual and voice processing algorithms that consume more CPU (Central Processing Unit) computing resources run in the cloud. Like the remote human brain, the cloud platform can efficiently process target information and feed the results back to the user.  
Specifically, after wearing the smart glasses, the users have access to scan the points of interest. Considering the safety of visually impaired people, this program will be running until it shuts down. Scanning points of interest adopts the fusion recognition scheme of ultrasonic sensor and infrared sensor. When the infrared sensor detects someone in front of the user, there will be a ringtone prompt. The user can touch the button to start the recognition process, and the camera will capture the front image. The server will extract and identify the faces, objects and text information that may be contained in the images uploaded by the client. The server's recognition result will be transmitted to the client and then converted to voice feedback to the user through TTS (Text To Speech) technology.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 4 Results  
The FastFix algorithm was initially tested using stationary receivers in a known locations (Dunedin, New Zealand). In this test, 118 four-millisecond samples of GPS L1-band data were digitized and stored. From these, the FastFix algorithm (described in the previous section) was applied.
## 6 Existing Routing Protocols
### APTEEN (Adaptive Threshold TEEN)  
APTEEN [9] is an improved version of TEEN which has all the features of TEEN. It was developed for hybrid networks and captures both periodic data collection and reacts to time critical events. APTEEN supports queries like:  
* Historical analysis of past data values
* A snapshot of the current network view.
* Persistent monitoring of an event for a period of time.  
Figure 2: Clustering in LEACH Protocol.  
In each round, after deciding the cluster head, the cluster head broadcasts the following parameters:  
* Attributes (interested physical parameters),
* Thresholds (hard threshold value and soft threshold value),
* time schedule (time slot using TDMA) and
* count time (maximum time period between two successive reports sent by a node).  
It allows the user to set threshold values and also a count time interval. If a node does not send data for a time period equal to the count time, it is forced to sense and retransmit the data thus maintaining energy consumption. Since it is a hybrid protocol, it can emulate a proactive network or a reactive network depending on the count time and threshold value. Figure 3 shows TEEN and APTEEN. It has the disadvantage that additional complexity is required to implement the threshold function and count time features.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 2 Satellite Missions, Projects and Data  
As one of the 12 distributed and discipline-specific DAACs [22], managed by the NASA's Earth Observing System Data and Information System (EOSDIS) [23], GES DISC [9] archives and supports datasets applicable to several NASA Earth Science focus areas including: atmospheric composition, water and energy cycles and climate variability. Datasets are also applicable to carbon cycle and ecosystem. As of this writing, GES DISC has archived ~140 million data files with the data volume of ~3157 TB. Since 2010, over 3 billion data files and over 31,159 TB of data have been distributed. Due to space limitation, only brief summaries of satellite missions and projects that are related to tropical meteorology and climatology are presented. More detailed information can be found in their mission or project websites.