esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analytical Application
#### 3.2.1 Optimization of the Recommended Procedure  
The relationship between solution pH and the absorbance of [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O at \(\lambda_{\max}\) = 434 nm was examined at various pH values using HCl/NaOH and acetate buffer (CH\({}_{3}\)COOH/ CH\({}_{3}\)COONa). The results in Figure 9 show that the maximum absorbance of [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O occurred at pH 4-6 in acetate buffer. The absorbance is less at pH \(\leq\) 3, because the protonation equilibrium of the reagent shifted to the left as displayed in Scheme 4 producing protonated form (H\({}_{2}\)L). Therefore, the deprotonated form of the reagent (HL) is required for complex formation. The absorbance of the Cu(II)-AMT complex decreased dramatically above pH 6, a behavior most likely attributed to the hydrolysis of Cu(II)-AMT and the formation of colorless forms of copper(II) such as hydroxo species. Thus, the acetate buffer with pH 4-6 was chosen for further experiments.  
Figure 8: Chemical structure of [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O complex. L represents AMT molecules with NH group.  
Figure 9: Influence of solution pH on the absorbance of [Cu(L)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O at \(\lambda_{\max}\) = 434 nm. Copper(II) concentration = 2 \(\mu\)g mL\({}^{-1}\); [AMT] = 1.20 \(\times\) 10\({}^{-4}\) M.  
The absorbance of [Cu(_L_)(NO\({}_{3}\))(H\({}_{2}\)O)\({}_{2}\)]\(\bullet\)H\({}_{2}\)O at \(\lambda_{\rm max}\) was significantly influenced by a change in AMT concentration from \(4.0\times 10^{-4}\) to \(6.7\times 10^{-5}\) M at constant Cu\({}^{2+}\) concentration (Figure 10). The absorbance of the complex was constant at [AMT] = \(6.5\times 10^{-5}\) to \(2.2\times 10^{-4}\) mol L\({}^{-1}\), but increased at concentrations above \(2.2\times 10^{-4}\) mol L\({}^{-1}\). A small decrease in absorbance was seen above \(2.8\times 10^{-4}\) mol L\({}^{-1}\), possibly because excess chromogenic reagent reduced the extent of complex formation by increasing the acidity of the solution [35]. Thus, the AMT concentration was fixed at \(2.8\times 10^{-4}\) mol L\({}^{-1}\) for all measurements.
## 2 Materials and Methods
### Mapping and Identification of DEGs  
The raw data were deposited in the NCBI SRA database under the PRJNA903215 accession number. Raw sequence reads were mapped to the human reference genome (GRCh38) ([[https://www.ncbi.nlm.nih.gov/genome/guide/human/](https://www.ncbi.nlm.nih.gov/genome/guide/human/)]([https://www.ncbi.nlm.nih.gov/genome/guide/human/](https://www.ncbi.nlm.nih.gov/genome/guide/human/)) (accessed on 10 October 2022)) using BBMap ver. 39.00 with the default parameters. HTSeq ver. 2.0.1 was used to quantify the gene expression for each gene using the GRCh38 general transfer format (GTF) (Homo_sapiens.GRCh38.108.chr.gtf) derived from Ensemble [31]. The number of mapped reads in each transcript was used to identify DEGs using DESeq2 ver. 1.38.3 in DEbrowser v1.24.1 [32]. We compared the GP extract-treated HaCaT cells with those treated with distilled water (control). The number of reads was normalized using the MRE normalization method without any correction. By applying a fold change (FC) of more than four times and \(p\)-values less than 0.01, we identified DEGs for each comparison. For the GP-all comparison, all nine datasets from the GP extract-treated samples were compared with the three control datasets.
## 6 Discussion
### The Influence of Different Risk Assessment Weights and Models on the Results
#### 6.2.1 Discussion on Weight Optimization  
When evaluating multiple risk factors, it is imperative to consider the interplay and allocation of weights among these risks. Different risk assessment models may make varying assumptions and employ diverse methods in assigning weights to risk factors, thereby potentially influencing the evaluation results. Therefore, when evaluating multiple risk factors, it is necessary to fully consider the correlation between risks. If conditions permit, different variable weight methods should be compared for weight optimization results, so as to select a better method to obtain more accurate evaluation results, for example, the gray correlation degree based on combination weight [43], the variable weight combination model based on XGBoost-LSTM [44], the combination weighting method based on game theory [45], etc.
## 2 System Architecture and Device
### Hardware Setup  
The system is based on the LinkIt ONE development board manufactured by MediaTek Inc. (Hsinchu, Taiwan) and Seeed Studio (Shenzhen, China) [21]. It is designed specifically for the Internet of Things and wearable devices. The greatest strength of this interface is that it has PC-independent, Wi-Fi-based direct connection to the code. Apart from this, it is loaded with MT5931 Wi-Fi components, MT3332 global positioning system (GPS) components, GSM, general packet radio service (GPRS), Bluetooth, Audio, a micro secure digital (SD) card and most Arduino UNO-compatible pins as shown in Figures 4 and 5. Another point worth noticing is that even though the development environment of LinkIt ONE is Arduino integrated development environment (IDE)-based, it features an ARM7 EJ-S-cored, MT2502 microcontroller (Aster) instead of a common AVR controlling system.  
As illustrated in Figure 4, the photoresistor is connected to a 220 \(\Omega\) limiting resistor in a serial circuit to protect the LED signaling light. The output of the photosensitive resistor divider containing a digital potentiometer MCP41010 and a photoresistor in parallel is connected to pin B of transistor 9031. Finally, Pin C of transistor 9031 is connected to pin D2 of LinkIt ONE, leading to signal magnification.  
Figure 4: The photosensitive resistor divider.  
Figure 3: System Architecture diagram of the novel electronic lock.  
The MCP41010, shown in Figure 5a, is a digital potentiometer that is controlled using a serial peripheral interface (SPI). It is fully adjustable using 256 steps from approximately zero to 10 k\(\Omega\) or about 39 \(\Omega\) per step.  
Digital potentiometer applications can be divided into two categories: rheostat mode and potentiometer (or voltage divider) mode. In the rheostat mode, the potentiometer is used as a two-terminal resistive element. The unused terminal should be tied to the wiper, as shown in Figure 5b. Reversing the polarity of the A and B terminals will not affect operation.  
Using the device in this mode allows control of the total resistance between the two nodes. The total measured resistance would be the least at code 00H, where the wiper is tied to the B terminal. The resistance at this code is equal to the wiper resistance, typically 52 \(\Omega\) for the 10 k\(\Omega\) MCP41010 device. For the 10 k\(\Omega\) device, the least significant bit (LSB) size would be 39.06 \(\Omega\). The resistance would then increase with this LSB size until the total measured resistance at code FFH would be 9.985 k\(\Omega\). The wiper will never directly connect to the A terminal of the resistor stack.
## 2 Developed 3-D Profilometry Using Dual Sensing
### Measuring Principle
#### 2.2.2 Dual-Sensing Technique  
As mentioned above, when the projected light beam hits the tested wafer surface, the reflected light beams may contain both diffuse and specular components (see Figure 2). The diffuse components are mainly formed by reflected light beams from micro structures on wafer surface with high surface roughness. Each reflected beam may interfere with other beams. However, there is no light interference due to low light coherence. In the optical design, the diffuse light intensity is reflected at a wide reflecting angle and can be detected by a vertical sensing camera. On the other hand, specular light is generated by the reflection of incident light from an object with a shiny reflective surface on a flat wafer surface having very low surface roughness. According to the law of reflection, specular light can be detected by an optical objective located at a reflectance angle equal to that of the incident light.  
To capture both diffuse and specular components simultaneously, a dual-sensing optical system is developed. In the configuration, a camera (10) is dedicated to capturing diffuse components with another camera (11) designated for capturing specular components. To reconstruct the 3-D shape of the measured surface, the reflected fringe images representing both diffuse and specular beams are simultaneously captured using the proposed system, illustrated in Figure 3. By controlling the adequate duration of light exposure required by the individual cameras, two deformed fringe images with high contrast can be detected. The entire 3-D shape of the measured target surface can be effectively synthesized from the two individual images detected and reconstructed by accurate pixel spatial mapping between two sensors. Details of the proposed measuring system are illustrated by the flowchart shown in Figure 4.  
In the proposed technique, the 3-D shape region detected and established from diffuse light can be well synthesized with the 3-D shape region detected and established from specular light. The two surface regions detected can be further merged seamlessly using a camera calibration procedure.  
Figure 4: Flowchart of the proposed measuring technique.  
A special artifact target is designed and employed as a special mapping target. Two cameras can be calibrated and merged into the same measuring field of view (FOV) on the tested surface. Details of the proposed calibration procedure are illustrated by the flowchart shown in Figure 5.
# Influence of Atmospheric Pollutants and Weather Conditions on Agricultural Productivity in Italy
## 2 Materials and Methods
### Meteorological Analysis  
The satellite recorded a decrease in annual rainfall but an increased concentration of millimeter-scale rainfall. Although the cities of Venice, Padua, and Ferrara had very similar rainfall levels in 2017-2018, the city of Padua has recorded a significant increase in rainfall since 2019. From Figure 9 it is possible to see the maximum peak in 2021 in Palermo. Bari has kept the millimeters of rain constant over the years, having only a small increase between the years 2017 and 2018. The city of Palermo recorded an almost constant trend  
Figure 8: Yield production of corn in Padua and Venice during the period 2012–2021.  
Figure 7: Yield production of common wheat in Ferrara and Padua during the period 2012–2021.  
until 2020 with a maximum in 2021, showing a difference of about 1000 units compared to the previous year. Padua and Venice have similar values since they are very close, but Venice seems to record a higher mm of rainfall (Figures 9 and 10).
## 2 Materials and Methods
### Spectral Correction  
During the spectral collection process, the spectra acquired may contain noise due to uncertainties in the instrument and the environment. Therefore, it is necessary to perform calibration on the collected spectra to ensure data accuracy and reliability. To calibrate the diffuse transmittance spectra (\(T_{s}\)) and diffuse reflectance spectra (\(T_{r}\)), the following calibration formulas are used.  
Diffuse transmittance spectrum calibration (\(T_{s}\)):  
\[T_{s}=\frac{R_{s}}{R_{w}} \tag{1}\]  
where \(T_{s}\) is the calibrated diffuse transmittance spectrum; \(R_{s}\) is the raw diffuse transmittance spectrum; and \(R_{w}\) is the white reference spectrum (collected using the standard reflectance white board).  
Diffuse reflectance spectrum calibration (\(T_{r}\)):  
\[T_{r}=\frac{R_{raw}-R_{dark}}{R_{white}-R_{dark}} \tag{2}\]  
Figure 2: Diffuse reflectance spectra collection device.  
where \(T_{r}\) is the calibrated diffuse reflectance spectrum; \(R_{\rm raw}\) is the raw diffuse reflectance spectrum; \(R_{\rm dark}\) is the dark spectrum (collected with the detection fiber covered); and \(R_{\rm white}\) is the white reference spectrum (collected using the standard reflectance white board).  
By using the above calibration formulas, noise introduced by instrument and environmental factors was effectively eliminated, thereby improving the quality and accuracy of the spectral data.
## 4 The NLOS Communication Scheme Based on CP/CS-SC-FDE  
### CP-SC-FDE VS CP/CS-SC-FDE  
In this subsection, the reason behind the generation of LISI in UAV NLOS communication is shown. Figure 3 shows the FFT regions of channel estimation and equalization in the LOS channel and the NLOS channel based on CP-SC-FDE. As is shown in part 2 of Figure 3, through a multipath channel, the received signal is a delayed superposition of the transmitted signal. In UAV LOS communication, corresponding to the blue CIR in part 1 of Figure 3, the PwMP of the multipath channel is the first path. As is shown in part 3 of Figure 3, \(u_{\max}=u_{C}\). Moreover, the FFT regions for channel estimation and equalization contain only information from the preambles, i.e., the preambles and the multipath channel satisfy cyclic convolution. Therefore, the receiver can achieve channel equalization correctly.  
However, in UAV NLOS communication, corresponding to the red CIR in the part 1 of Figure 3, the PwMP of the multipath channel is not the first path. Assume that  
\[h_{1}=\max\{h_{l}|0\leq l\leq L-1\}. \tag{11}\]  
Then,  
\[u_{\max}=u_{C+1}, \tag{12}\]  
Figure 3: FFT region in LOS and NLOS channel based on CP-SC-FDE.  
which is shown in part 3 of Figure 3. In UAV NLOS communication, time synchronization occurs with an unavoidable time offset (TO). Moreover, the FFT region for channel estimation will introduce ISI from the later symbols of preambles, i.e., LISI, causing the preambles and the multipath channel not to satisfy the cyclic convolution. The channel equalization will be wrong.  
Therefore, for UAV NLOS communication, we design a communication scheme based on CP/CS-SC-FDE, the block of which is shown in Figure 2. Figure 4 shows the FFT regions of channel estimation and equalization in the LOS channel and NLOS channel based on CP/CS-SC-FDE. As is shown in part 2 of Figure 4, even if the time synchronization occurs with an unavoidable TO in UAV NLOS communication, the channel estimation region will not introduce LISI as long as the length of the CS is larger than the TO. So, the preambles and multipath channel still satisfy the cyclic convolution. Moreover, the channel equalization will be right.
## 1 Introduction
### Cretan Power Grid  
Electricity generation and fossil fuel consumption in Crete in 2022 has been reported by the Hellenic Electricity Distribution Network Operator (HEDNO) SA [27]. Total electricity generation in Crete during 2022 was 20,784.7 MWh, while most of it was generated in thermal power stations fueled by heating oil and diesel oil. The share of RES in electricity generation was slightly higher than 23%. According to HEnEx data [28], in 2023, the island's annual energy demand was 3.14 TWh, 1.48 TWh of which were produced using the island's conventional power plants, 701.7 GWh from RES production, while the rest were imported through the interconnection. The proposed methodology receives as input the load and means of energy demand hourly values of Crete for 2023. An alternative priority chain of load satisfaction and RES production is proposed, based on which the optimal RES expansion and hydrogen system introduction is determined for an independent, sustainable, and carbon-neutral energy system. The potential of storing power on the island is discussed, and the CO2 emissions that are caused by the usage of fossil fuels in the production of electricity are estimated. In the following section, the requirements for net zero CO2 emissions due to electricity use in Crete are stated.  
The current operational scheme is dictated by the participation of the island's electricity producers (both RES and power plants) and providers in the HEnEx, with the interconnection facilitating the transmission of purchased quantities. This research explores an alternative model of an independent grid, demonstrating a method to increase the use of RES in energy production. This approach aims to reduce carbon emissions and fossil fuel consumption in Crete, offering a replicable model for other islands. This paper's methodology presents a comprehensive analysis of the economic and environmental aspects of an innovative project design using the most recent data from 2023. The analysis employs a methodological model that utilizes techniques to analyze and simulate large-scale real-world data, including hourly values for an entire year. This approach provides targeted insights into the real-time energy management of a microgrid, capturing the actual dynamics of both energy consumption and production throughout the year. This holistic understanding of microgrid performance allows us to coherently assess its economic and environmental dimensions, paving the way for more informed decision-making.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 4 Numerical Results
### Wire Antenna Mounted on a Realistic Ship  
Another example is a wire antenna mounted on a realistic ship model. The magnitude of the antenna is 1 \(Idl(Am)\). The length of the realistic ship is 16.48 m and the height is 4.78 m, as shown in Figure 11. The antenna works at a frequency of 2 GHz. The mast of the ship connected with the antenna is totally allocated as the transition region. The remaining part of ship (BT region) is modeled by mesh size of 1\(\lambda\) for the iterative MLFMA-MADBT method.  
The far-field radiation patterns in the XOZ cut-plane calculated by the MLFMA, the MLFMA-PO and the proposed iterative MLFMA-MADBT methods are displayed in Figure 11. Compared with the MLFMA method, the proposed method can reduce the mean and standard deviation of the MLFMA-PO method from 6.93 to 4.95, from 9.54 to 7.51, respectively. Furthermore, the proposed method can keep good accuracy even with extremely coarse mesh for the flat platform.  
Furthermore, for the wire antenna mounted on realistic ship model, the number of mesh elements, memory requirement and the computation time of the three methods are given in Table 2. Compare to the MLFMA method, the proposed iterative MLFMA-MADBT method can reduce the requirement for the memory from 81.46 GB to 0.64 GB and computation time from 4882.03 s to 229.01 s. AS for the MLFMA-PO method, the memory requirement of the MLFMA-PO method is about 5 times of the proposed method and about 10 times on the time consumption. It is obvious that the proposed method not only has high predominance in memory usage but also uses the least time among these three methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Mesh Elements**} & \multirow{2}{*}{**Memory (MB)**} & \multirow{2}{*}{**Time (s)**} \\ \cline{2-2} \cline{4-5}  & **MLFMA** & & & \\ \hline MLFMA & 3,250,756 & / & 81,458.10 & 4882.03 \\ MLFMA-PO & 21,384 & 359,071 & 3102.34 & 2178.81 \\ MLFMA-MADBT & 21,384 & 32,084 & 650.33 & 229.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation time and memory requirements of different methods for the wire antenna mounted on realistic ship model.  
Figure 11: The model and far-field radiation patterns in XOZ plane of the wire antenna mounted on a realistic ship. (a) Model (b) Comparison result.
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 5 Grasping Stage
### Position-Force Hybrid PID Controller
#### 5.3.1 Position Control  
A discrete PID algorithm is used to implement the position PID in the ROS. This algorithm replaces the derivative term using a backward difference method and the integral term using a rectangular integration method. The discrete form of position algorithm for PID is given as:  
\[u_{p}(t)=u_{p}(t-1)+[K_{p_{p}}+\frac{K_{p_{p}}T_{s_{p}}}{T_{i_{p}} }+\frac{K_{p_{p}}T_{d_{p}}}{T_{s_{p}}}]e_{p}(t)\\ +[-K_{p_{p}}+\frac{K_{p_{p}}T_{s_{p}}}{T_{i_{p}}}+\frac{2K_{p_{p} }T_{d_{p}}}{T_{s_{p}}}]e_{p}(t-1)+\frac{K_{p_{p}}T_{d_{p}}}{T_{s_{p}}}e_{p}(t -2) \tag{3}\]  
where \(u_{p}(t)\) is the control input, \(K_{p_{p}}\) is the proportional gain, \(T_{i_{p}}\) is integral time, \(T_{d_{p}}\) is derivative time or rate time, and \(e(t)\) stands for the error between the reference and process output. The values of \(K_{p_{p}}=0.000001\), \(K_{i_{p}}=\frac{K_{p_{p}}T_{s_{p}}}{T_{i_{p}}}=0.0001\), and \(K_{d_{p}}=\frac{K_{p_{p}}T_{d_{p}}}{T_{s_{p}}}=0.001\) were chosen using the Ziegler-Nichol tuning rule. \(T_{sp}\) is the sampling period and \(e_{p}(t)=Ref_{pos}-Pos_{\omega(t)}\) is the position error.  
In this case, the position reference is the center of the gripper. When the position error is sufficiently small \(e_{p}(t)\leq h\) with \(h=\pm 2\%Ref_{pos}\), a change is sent to the force PID controller.  
Figure 5: Control scheme of the hybrid PID controller.
## 3 Results  
In this study, the stability of the signal acquisition system was assessed. The test involved examining a precision resistor with a known value of 1 k\(\Omega\) across the frequency range of 1 Hz to 500 kHz. The results indicate that the maximum deviation falls within the range of 993 \(\Omega\) to 1.009 k\(\Omega\), with a corresponding standard deviation value of 3.0117, as illustrated in Figure 3.  
Figure 4 shows the SNR values derived from 208 measurements across 5 experiments. Each measurement involved 500 repetitions, and the SNR was calculated using Formula (1). All experiments consistently showed an SNR index exceeding 30 dB. Specifically, experiments conducted in a homogeneous environment (saltwater) (Figure 4A,B,D) exhibited a higher average SNR index compared to those in a non-uniform field (ground-meat environment) (III, V) (Figure 4C,E). This underscores the effectiveness of the signal acquisition system in facilitating additional experiments and assessments.  
Figure 3: Stability assessment of the signal reception system at \(Z=1\) k\(\Omega\) across various frequencies from 1 Hz to 500 kHz.  
Figure 4: The SNR values across five experiments performed on a 16-electrode phantom, each conducted 500 times: (**A**) Experiment I; (**B**) Experiment II; (**C**) Experiment III; (**D**) Experiment IV; (**E**) experiment V.
## 2 Methodology  
### Static Aeroelastic Response Equation  
The basic equation of static aeroelastic response analysis is generally stated as follows [12]:  
\[[K_{ff}-\overline{q}Q_{ff}]u_{f}+M_{ff}\vec{u}_{f}=\overline{q}Q_{fx}u_{x}+P_ {f} \tag{1}\]  
where \(K_{ff}\) is the structure stiffness, \(\overline{q}\) is the dynamic pressure, \(u_{f}\) is the displacement vector, \(M_{ff}\) is the structure mass matrix, and \(P_{f}\) is the external loads. \(\overline{q}Q_{ff}u_{f}\) is the aerodynamic increments generated by structural elastic deformation, and \(\overline{q}Q_{fx}u_{x}\) is the aerodynamic force generated by the deflections of aerodynamic control surface and rigid body motion. The elastic aerodynamic derivatives and trim parameters were obtained from this formula.  
Figure 1 shows a typical iteration process for the static aeroelastic coupling computation, which includes three main steps: static aerodynamic solution, structural solution, and coupling solution. The static aerodynamic solution refers to solving the flow field equations and static aerodynamic equations to obtain the pressure and velocity distribution in the flow field. The structural solution refers to solving the structural mechanics equations to obtain the displacement and stress distribution in the structure. The coupling solution refers to using the flow field pressure and velocity distribution as boundary conditions to iteratively solve the flow field equations and structural mechanics equations to obtain the final coupled solution. The figure also shows the convergence criteria for the iterative computation and the flow control of the iteration process. The entire computation process requires multiple iterations until the convergence criteria are met.