esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results Analysis
### Comparison  
In order to compare the results of the experimental measurements and numerical calculations, the scattered near-field amplitude and phase distributions for the plates and spherical targets are illustrated in Figure 14. Both measured and theoretical results showed that the reflected field of the plate had obvious characteristics of an amplitude hollow and a phase spiral distribution. Since the sphere diameter and the incident vortex beam spot size were close, the forward scattered amplitude was obviously diffracted, and the intensity was weaker than that of the reflected field. The results of the measurement and calculation agreed well, and some phase and amplitude differences may have been caused by non-perfect Bessel vortex waves and measurement errors. In the calculation, the incident wave source was selected as an ideal Bessel beam with non-diffraction characteristics, and the target was an ideal conductive/PEC sphere.  
In particular, a theoretical comparison was implemented for the target RCS obtained by the near-far field conversion method, as shown in Figure 15. Both the measurements and simulation showed that the RCS of the metal plate and FR4 dielectric plate had similar hollow distributions and side lobe characteristics. The theoretical scattering for the PEC sphere revealed the non-diffraction characteristics of the Bessel beam, while the fabricated non-ideal Bessel antenna in the measurement generated a bit of a diffracted source. This was the main reason for the RCS difference between the two results. The difference between the measurement results and the theoretical results was mainly due to the difficulty in achieving the physical characteristics of the perfect vortex beam such as the topological purity, half-cone angle, and non-diffraction distance. However, the scattered field of the vortex wave presented by both the theory and measurement had a similar characteristic distribution and the scattering law was basically the same, which has scientific significance in the field of OAM radar imaging and detection.  
Figure 14: Comparisons of the amplitude and phase distributions from the measurement and theory at an operation frequency of 20 GHz. (**a**,**c**) Measurement and (**b**,**d**) theory.  
Furthermore, the measurement of forward scattering places strict requirements on the target contour size, which, due to that too small target, will make part of the incident wave detectable and affect the scattering near-field results. Most importantly, near-far field conversion was an effective method to obtain the far-field RCS in most near-field experimental scenes, according to the comparative analysis.
## 3 Results and Discussion
### Glass Fiber Reinforcement of Geopolymers
The highest properties were achieved with a 16 mol solution. The 12 and 20 mol solutions did not provide such high results [50]. [PERSON] et al. study geopolymers and their mixture with glass fiber-reinforced Portland cement. Five types of specimens were prepared: one type of specimen made of pure geopolymer material, one with the addition of Portland cement, and three types of hybrid specimens with the addition of Portland cement reinforced with glass fiber with different volume contents of these fibers. All the samples were prepared with 12 mol NaOH solution. The samples were subjected to compression and flexural tests after 7 and 28 days of curing. During the tests, it was noticed that the compressive and flexural strength increased with the aging of the samples. The addition of 0.01% and 0.02% glass fiber by volume to the geopolymer/Portland cement mixture resulted in a decrease in compressive strength relative to the specimen without glass fiber addition, while the addition of 0.03% glass  
Figure 12: Flexural strength of E-glass fiber-reinforced geopolymers and geopolymers with cement addition. fiber by volume resulted in an increase in strength relative to the specimens without fiber addition. Based on the flexural strength tests, the flexural strength of the 0.01% and 0.02% glass fiber samples decreased, while the flexural strength increased for the 0.03% volume share samples relative to the hybrid without fiber addition. The decrease in strength was about 1% and 12% for the addition of 0.01% and 0.02% glass fiber volume share, respectively, concerning the hybrid, while the increase was 16% of the addition of 0.03% glass fiber volume share also concerning the hybrid without fiber addition [51].
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 3 Materials and Methods
### The Implementation of the Proposed Solution
#### 3.2.3 Implementing Functions in AWS Lambda
\begin{table}
\begin{tabular}{c p{227.6 pt}} \hline \hline
**Function** & **Description** \\ \hline _AddEmployeToken_ & This function is designed to register a Firebase token in the DynamoDB table named EmployeeTokens \\ \hline _AddStartEmploye_ & This function records the start of the workday for an employee in the EmployeesTimeSheet table. \\ \hline _AddEndShift_ & This function manages the end of the workday for an employee. \\ \hline _FetchEmployeTasks_ & This queries the EmployeesTasks table to obtain tasks associated with a specific employee. \\ \hline _FetchTimeSheet_ & This function queries the EmployeesTimeSheet table to obtain employee time sheets for a specific month and year \\ \hline _UpdateEmployeToken_ & This function is used to associate an employee ID with a connection token to the Firebase service stored in the EmployeeTokens table. \\ \hline _UpdateStatusTasks_ & This function updates the status of a task associated with an employee in the EmployeesTasks table. \\ _WebSocketConnectHandler_ & This function records the connection ID and the connection time to the WebSocket in a table called WebSocketConnections. \\ \hline _WebSocketDisconnectHandler_ & This function performs the opposite of the previous one, being used to remove from the WebSocketConnections table the record containing the ID of the closed connection. \\ \hline _UpdateSmartartAttchlII_ & This function handles various actions received by a WebSocket. It allows actions such as initiating connections, sending messages to specific connections, and managing the connection state. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The implemented functions. This comprehensive overview explores the implementation of various Lambda functions tailored to specific tasks within the system, including employee data management, work hour tracking, WebSocket connection handling, and task status updates. Each function is strategically developed to interact seamlessly with DynamoDB tables, ensuring the system operates efficiently and delivers the required functionalities.
## 3 Performance Evaluation
### Network Architecture  
The experimental network deployment used to measure the capabilities and performance of the different IEEE 802.11 standards is described in detail in Figure 2. The components of the Wi-Fi network are as follows:  
* Netgear AXE7800 is a tri-band access point that supports Wi-Fi 6 and 6E (IEEE 802.11 ax) and operates in the 2.4 GHz, 5 GHz, and 6 GHz bands. It ensures backward compatibility with older standards (Wi-Fi 4 and 5). Internally, it integrates the Broadcom BCM6715 + BCM6756 chipset combination.
* Asus RT-BE88U is an advanced access point that supports the Wi-Fi 7 standard (IEEE 802.11 be) and operates in the 2.4 and 5 GHz bands. Although the 6 GHz band is not available in this model, it enables access to advanced features such as 4K QAM and enhanced multi-link operations. It is powered by the Broadcom BCM4916 network processor along with the BCM6726 Wi-Fi 7 radio chipset.  
Figure 2: Network deployment infrastructure used for experimental validation of Wi-Fi performance.  
* The Raspberry Pi 4 Model B is responsible for running the iperf3 server to evaluate the performance of the Wi-Fi network. It is connected to the network via an Ethernet cable to the access point, as shown in Figure 2.
* The desktop computer with an Intel Core i7-10700 processor with a Gigabyte AORUS GC-WIFI7 network card, connected via a PCI-E port. This card supports Wi-Fi 4 to Wi-Fi 7 and operates in three bands (2.4, 5, and 6 GHz). Internally, it uses the Qualcomm QCNCM825 chipset, designed to provide full IEEE 802.11 be functionality.  
The use of COTS hardware in this study aims to evaluate the performance of Wi-Fi in representative and reproducible scenarios. Although these devices are not designed for extreme industrial environments, they incorporate the same chipsets as many industrial-grade solutions, enabling comparable results at the MAC and physical layers.
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 3 System Calibration
### Stitching Technique  
After the calibration process and distortion correction, the measured sample can be composed using the stitching technique. The stitching technique consists of combining two consecutive measurements while taking into account the pixel size of the camera, the magnification of the system, the displacement between measurements, and the correction of perpendicularity between the linear x-axis and y-axis linear stages. Knowing these values, it is possible to combine both measurements with a sigmoid function; this makes the overlapped zone of two consecutive measurements continuous, acting like a weighted sum. The common part of both images is defined as follows:  
\[c=\frac{\text{image shape}\cdot\text{displacement}}{\text{pixel size}\cdot\text{sensor length}} \tag{18}\]  
\[f=\text{image shape}-c \tag{19}\]  
where \(c\) and \(f\) are the first and last pixels taken on the x-axis for the first and second images, respectively. If \(g_{a}\) is the first input image and \(g_{b}\) is the last, then their domain in the common part of the images is  
\[g_{ac}(x,y):x\in[c,\text{image shape}], \tag{20}\]  
\[g_{bc}(x,y):x\in[0,f]. \tag{21}\]  
Then, as said before, the common part of the composed image is a weighted sum along the x-axis using a sigmoid function:  
\[g_{c}(x,y)=g_{ac}(x,y)\cdot\sigma(x,y,k)+g_{bc}(x,y)\cdot\sigma(x,y,-k) \tag{22}\]  
where \(\sigma(x,k)\) is a common sigmoid function, as seen in Equation (23). Because the sum of both sigmoid functions is 1 in the full domain where it is applied, there is no inconsistency between the intensity values in the resulting stitched image.  
\[\sigma(x,y,k)=\frac{1}{1+e^{-k\cdot x}} \tag{23}\]  
Thus, the final image is the concatenation of the first image without the common part of both measurements, the weighted sum of the common part, and the rest of the image of the second image without the common part.  
Figure 10: Overlapped zone of two consecutive measurements of the calibration mask without distortion correction (**left**) and with distortion correction (**right**).  
\[G(x,y)=\left\{\begin{array}{ll}g_{x}(x,y)&\text{if}\quad x\leq c\\ g_{c}(x,y)&\text{if}\quad c\leq x\leq c+f\\ g_{b}(x,y)&\text{if}\quad c+f\leq x\leq 2c+f\end{array}\right. \tag{24}\]  
Repeating this technique iteratively allows a full row to be combined with the same process. Using the same technique, it is also possible to combine two consecutive rows. In the following chapter, a full measurement is shown.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF), as a direct indicator of vegetation photosynthesis, offers a more accurate measure of plant photosynthetic dynamics than traditional vegetation indices. However, the current SIF satellite products have low spatial resolution, limiting their application in fine-scale agricultural research. To address this, we leveraged MODIS data at a 1 km resolution, including bands b1, b2, b3, and b4, alongside indices such as the NDVI, EVI, NIRv, OSAVI, SAVI, LAI, FPAR, and LST, covering October 2018 to May 2020 for Shandong Province, China. Using the Random Forest (RF) model, we downscaled SIF data from 0.05\({}^{\circ}\) to 1 km based on invariant spatial scaling theory, focusing on the winter wheat growth cycle. Various machine learning models, including CNN, Stacking, Extreme Random Trees, AdaBoost, and GBDT, were compared, with Random Forest yielding the best performance, achieving R\({}^{2}\) = 0.931, RMSE = 0.052 mW/m\({}^{2}\)/nm/sr, and MAE = 0.031 mW/m\({}^{2}\)/mm/sr for 2018-2019 and R\({}^{2}\) = 0.926, RMSE = 0.058 mW/m\({}^{2}\)/nm/sr, and MAE = 0.034 mW/m\({}^{2}\)/nm/sr for 2019-2020. The downscaled SIF products showed a strong correlation with TanSIF and GOSIF products (R\({}^{2}\) > 0.8), and consistent trends with GPP further confirmed the reliability of the 1 km SIF product. Additionally, a time series analysis of Shandong Province's wheat-growing areas revealed a strong correlation (R\({}^{2}\) > 0.8) between SIF and multiple vegetation indices, underscoring its utility for regional crop monitoring.  
solar-induced chlorophyll fluorescence; random forest; downscaling; gross primary productivity; wheat growth analysis +
Footnote †: journal:  
0000-0002-1531-1900][PERSON]  
0000-0002-1531-1900][PERSON]  
0000-0002-1531-1900][PERSON]  
0000-0002-1531-1900][PERSON]
# Quantum Neural Networks Approach for Water Discharge Forecast
## 2 Methodology and Data Series  
### Computational Experiment  
The experimental environment is based on a high-performance computing system with an AMD RYZEN5 5500 processor (AMD, Santa Clara, CA, USA) and 24 GB of memory, ensuring computational efficiency and stability. In terms of software configuration, Anaconda was utilized as the package management and environment configuration tool, facilitating dependency management and version control of various software libraries. The experiments were conducted using Python 3.12, which offers enhanced performance optimization while maintaining compatibility.  
For quantum computing simulations, we employed Qiskit, an open-source framework developed by IBM specifically designed for quantum computing research and applications. Qiskit provides a comprehensive suite of functionalities, including quantum circuit construction, quantum algorithm simulation, and hardware backend integration, effectively supporting the modeling and computational processes involved in quantum neural networks in this study. Furthermore, we optimized simulation parameters within the Qiskit environment to enhance computational efficiency. We leveraged scientific computing libraries such as NumPy and SciPy to improve numerical stability and computational performance. The computational environment established in this study ensures the effective implementation of quantum intelligence methods while providing a robust technical foundation for efficient model training.
## 4 Proposed Video Latency Measurement Technique
### Timestamp Sender  
Timestamp Sender is used for generating a visual event, which is captured by the camera's sensor. Each event is accompanied with the data containing the timestamp of the event and event ID. The event ID is a number which is incremented for each generated event. This number is displayed in the application window which is captured by the IP camera. The purpose of the event ID is to indicate the quality of the video for the end user. The timestamp is captured from windows system time with 1 ms resolution. The application acts as a server in a way that keeps a pre-defined UDP port open for client registration. Any client that sends a request packet to the port will be registered and will keep receiving the visual event data periodically. The described functionality is shown in Figure 4.
## 6 Conclusions  
In this paper, we addressed the problem of the reduction of the number of users with low throughput caused by the movement of HAPS. To do so, a novel method named DRLEA was proposed. Different from the PSO and conventional RL methods, the proposed DRLEA method can adjust the antenna parameters without any searching overhead. We used the throughput of users for the reward calculation instead of using the received SINR. Using throughput for reward calculation will increase the computational overhead but it can make the DRLEA learn not only the SINR of users but also the location information of that. In other words, the proposed DRLEA method can improve the user's SINR and bandwidth at the same time. Moreover, the proposed DRLEA combined EA with DRL to avoid sub-optimal solutions. A novel loss function was designed to train the DNN with the historical optimal solution to avoid the sub-optimal solutions.  
Through simulations, we demonstrated that the proposed approach clearly improves the throughput of the users at the lower end of the spectrum. Compared with approaches such as the PSO and the \(\mathcal{Q}\)-learning ones, the proposed DRLEA method achieves the best throughput performance under all the user distribution cases. Moreover, to prove the good robustness of the proposed DRLEA method, we use the proposed DRLEA, which is trained in the HAPS with a rotation scenario to control the antenna parameters under the shifting scenario. Through simulations, we demonstrated that, compared with the other three methods, which are trained under the HAPS with shifting, the proposed DRLEA method can achieve comparable throughput performances even without re-training.  
Conceptualization, S.Y.; methodology, S.Y.; validation, S.Y., M.B. and T.O.; formal analysis, S.Y. and M.B.; writing--original draft preparation, S.Y. and M.B.; writing--review and editing, S.Y., M.B.,T.O., Y.S., W.T., K.H. and A.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JST SPRING, Grant Number JPMJSP2123. These research results were obtained from the commissioned research (No. 05701) by the National Institute of Information and Communications Technology (NICT), Japan.  
**Data Availability Statement:** Not applicable.  
Figure 10: CDF of UE throughput performance under the HAPS with shifting.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Materials and Methods
### Data Processes and Numerical Results
According to Figures 3 and 5, the number of tracked satellites and the number of satellites used in \begin{table}
\begin{tabular}{l l} \hline \hline
**Items** & **Models/Methods** \\ \hline PPP model & Conventional PPP ambiguity-float model using IF combination \\ Observation used & dual-frequency code and phase observations \\ Processing mode & Kinematic PPP \\ Signals & GPS: L1, L2; GLONASS: R1, R2; BeiDou: B1, B2; Galileo: E1, E5 \\ Solutions & G, GR (G+R), GRE (G+R+E), GRC (G+R+C), and GREC (G+R+E+C) \\ Cut-off elevation angle & 10\({}^{\circ}\) \\ Processing interval & 1 s. (1 Hz) \\ Software & GrafNav (Waypoint/Novatel) [33] \\ Solution strategy & Multi-Pass (Forward-Backward-Forward) \\ Reference frame & ITRF14 \\  & IGS Final (G); CODE-COD Final (GR); \\ Used orbit and clock files & CODE-COM Final (GREC)-MGEX; \\  & GFZ Final (GREC)-MGEX; WHU Final (GREC)-MGEX \\ PCO and PCV & igs14.atx \\ Bias & Differential Code Biases (DCB) \\ Ionospheric delay & ionosphere-free (IF) linear combination (L3) \\  & a priori: UNB (University of Brunswick) model \\ Tropospheric delay & wet component estimated with Extended Kalman Filtering (EKF) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Used methods and models for Precise Point Positioning (PPP) processing. kinematic PPP processing is similar. However, the PDOP values have slightly changed. As seen from the right side of Figure 5, the average PDOP values are 2.5 for GPS, 1.8 for GPS+GLONASS, 1.4 for GPS+GLONASS+BDS, 1.4 for GPS+GLONASS+Gailleo, and 1.2 for GPS+GLONASS+BDS+Gailleo combinations, respectively. In the PPP processing, the change of PDOP values means that some code and phase observations or satellites are excluded from the adjustment process as an outlier because of GNSS un-modelled error sources. If the PDOP value is increasing, then the satellite number or observation number is decreasing, which means that 3D position accuracy is reduced. According to Figure 5, it is seen that every new satellite system added to the GPS system decreases the PDOP value. Under normal conditions, if PDOP values are decreasing, 3D position accuracy is expected to increase. However, different results may occur in PPP processing due to un-modeled GNSS error sources like cycle-slip and multipath. In addition, the accuracy of precise products, and the positions of satellites in the sky (distributions), used in the PPP processing, affect the accuracy of the results. Figure 5: The number of used satellites (**left**) and calculated PDOP values (**right**) in kinematic PPP processing for G, GR, GRC, GRE, and GRCE combinations from top to down, respectively.
## 3 Results
### Reaction Mechanisms of NH\({}_{3}\) Adsorption on TMG Adsorbed Si(100) Surface
The corresponding molecular structures are labeled (a)–(d) for adsorption on the Si–NH\({}_{2}\) surface and (e)–(h) for adsorption on the Si\({}_{2}\)–NH surface, as shown at the bottom of the figure. reaction pathways show that NH\({}_{3}\) adsorbs onto both types of surfaces via similar reaction mechanisms, resulting in comparable nitridation kinetics. In the ALD precursor such as a TMG molecule, the Ga atom is the core of the molecule connected to the methyl groups (the basis of the molecule). In the adsorption step, only the Ga atoms are required, while the methyl groups must be eliminated. In developing a new precursor for low temperature processes, the ease of decomposition of these methyl groups should be considered. Furthermore, the decomposition of the molecule must not contaminate the film. In addition, the residual precursor (the remaining species) should be highly reactive with the NH\({}_{3}\) molecules when they adsorb on the surface. Using a co-reactant to help in the dissociation of these basis groups should be investigated. The activation energies for ALD-GaN thin-film growth under various conditions in this study are summarized in Table 1. This table compiles the activation energies of TMG and NH\({}_{3}\) molecules adsorbed on different surfaces during the ALD process, along with comparative values from previous reports. The data show that when TMG molecules react with a surface, they decompose significantly more easily than in the gas phase, as reflected by the high activation energy of up to 3.27 eV for gas-phase decomposition. Additionally, the adsorption of TMG and NH\({}_{3}\) on different surfaces leads to variations in activation energy, indicating differing requirements for process temperatures. Therefore, tuning the surface adsorption sites of the substrate directly influences both the reaction kinetics and the process temperature. From the table, it is evident that preparing the surface in the Si\({}_{2}\)-NH configuration reduces the activation energy from 2.00 eV to 1.11 eV (in the case where TMG is adsorbed by consuming a single surface site). This configuration also improves the reactivity during subsequent NH\({}_{3}\) adsorption. Interestingly, this approach to surface preparation could potentially be extended to other nitride thin-film ALD processes as well.