esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Affecting Factors
### Seasonal Changes
#### 2.2.2. Canopy Level  
Recent studies have indicated that the changes in the patterns of seasonal and inter-annual PRI were correlated with chlorophyllic and carotenoid pool sizes and structural properties [76; 123; 124; 107]. Structural changes of the canopy caused by sustained water stress [125; 126; 120; 123] or a varying leaf area index (LAI) [127] over the season led to PRI variability and loss of the seasonal relationship between PRI and RUE. PRI calculated from pure crown reflectance for fruit trees under different irrigation regimes varied with xanthophyllic pigment contents and not with vegetation structure or chlorophyllic content [128]. The sensitivity of PRI to physiological indicators of stress when calculated for entire canopies, however, was considerably lower for dark soil [128]. PRI has shown sensitivity to both the zenith angle and relative azimuth angle [83]. Differences in the correlations between PRI and RUE for different types of vegetation can be due to differences in the canopy structure and shadow fraction, however, when the canopy is detected from only one angle (as with MODIS) [4], highlighting the importance of the effect of the canopy structure on PRI. PRI was also sensitive to changes in the structure and physiology during the soybean growing season caused by elevated CO\({}_{2}\) and O\({}_{3}\) concentrations [127]. Canopy PRI values were higher during the growth phase than other phases [83; 129] and did not efficiently track RUE variability during the ripening stage [129]. Canopy PRI values were higher on cloudy days with low irradiance than on sunny days [130], and were correlated most strongly with RUE under clear or slightly overcast skies [123]. For some vegetation such as _Elaeganus umbellate_, a drought-tolerant invasive species, PRI decreased slightly during water stress because of the plant's special physiological and morphological characteristics [122].
## Appendix A  
The coordinates of \(m^{th}\) RIS element is calculated as follows (see Figure 4):  
\[p_{m}^{RIS}=p^{RIS}+\Gamma^{-1}\Big{(}\theta^{UAV}\Big{)}\begin{bmatrix}l(m) \\ w(m)\\ \end{bmatrix}, \tag{1}\]  
where  
\[l(m)=d_{l}\bigg{(}Mod(m-1,M_{l})-\bigg{(}\frac{M_{l}-1}{2}\bigg{)}\bigg{)};\]  
\[w(m)=d_{w}\bigg{(}\bigg{(}\frac{M_{w}+1}{2}\bigg{)}-\bigg{[}\frac{m}{M_{l}} \bigg{]}\bigg{)};\]\[\Gamma\Big{(}\varrho^{R{U}{U}}\Big{)}=\begin{bmatrix}\cos\big{(}\varrho^{U{U}{U}{U} \big{)}&-\sin\big{(}\theta^{U{U}{U}{U}}\big{)}&0\\ \sin\big{(}\varrho^{U{U}{U}{U}}\big{)}&\cos\big{(}\theta^{U{U}{U}{U}}\big{)}&0 \\ 0&0&1\end{bmatrix};\]  
\(M_{l}\) and \(M_{w}\) are the number of elements along with the length and width of the RIS, respectively; and \(d_{l}\) and \(d_{w}\) denote the length and width sizes of the elements, respectively. Also, \(\Gamma\big{(}\theta^{U{U}{U}}\big{)}\in\mathbb{R}^{3\times 3}\) transforms the coordinates from the global reference frame to the local reference frame of the UAV, and \(\theta^{U{U}{U}}\) denotes the UAV heading with respect to the x-axis.  
Appendix B Assuming that 2D coordinates of the \(T_{x}\) is taken as the origin of the local reference frame, i.e., \(p^{Tx}=\begin{bmatrix}0&0&z^{Tx}\end{bmatrix}^{\top}\in\mathbb{R}^{3}\), the azimuth angle of the \(\mathrm{T_{x}}-\mathrm{RIS}\) path, is measured clockwise from the x-axis to the line between RIS position (i.e., \(p^{RIS}\)) and the \(\mathrm{T_{x}}\) position, and is obtained as follows (see Figure 4):  
\[\varphi^{T\_RIS}=\cos^{-1}\Big{(}x^{RIS}/\|[(x^{RIS}),(y^{RIS})]^{\top}\|_{2} \Big{)}; \tag{2}\]  
The elevation angle between the horizontal plane and \(\mathrm{T_{x}}-\mathrm{RIS}\) LoS link is obtained as  
\[\zeta^{T\_RIS}=\tan^{-1}((z^{Tx}-z^{RIS})/\|[(x^{RIS}),(y^{RIS})]^{\top}\|_{2}); \tag{3}\]  
Similarly, the azimuth and elevation angles of the reflected signal from RIS can be obtained as  
\[\varphi^{RIS\_R}=\cos^{-1}\Bigg{(}\frac{\big{(}x^{R_{x}}-x^{RIS}\big{)}}{\Big{\| [(x^{BS_{Rx}}-x^{RV}),(y^{BS_{Rx}}-y^{RV})]^{\top}\Big{\|}_{2}}}\Bigg{)}; \tag{4}\]  
\[\zeta^{RIS\_R}=\tan^{-1}\Bigg{(}\frac{\big{(}z^{Tx}-z^{RIS}\big{)}}{\Big{\|}[ (x^{Rx}-x^{RIS}),(y^{Rx}-y^{RIS})]^{\top}\Big{\|}_{2}}\Bigg{)}. \tag{5}\]  
Appendix C Supposing that the UPA of the mMIMO BS is aligned in the YZ plane (see Figure 5), the coordinates of the \(n^{th}\) antenna of the mMIMO BS is obtained as follows:  
\[p_{n}^{BS}=p^{BS}+\begin{bmatrix}d_{c}\Big{(}Mod(n,N_{c})-\frac{1}{2}\Big{)} \\ d_{r}\Big{(}\Big{\lceil}\frac{n}{N_{c}}\Big{\rceil}-\frac{1}{2}\Big{)}\end{bmatrix}, \tag{6}\]  
where \(N_{r}\) and \(N_{c}\) denote the number of horizontal and vertical antennas of the mMIMO BS; \(d_{r}\) and \(d_{c}\) denotes the horizontal and vertical sizes of each antenna (including the antenna spacing).
## 3 Performance Analysis
### Average Bit Error Rate  
There are many modulations that can be used in the hybrid RF-FSO system, they have a large difference in systematic performance due to their different characteristics, such as power efficiency, bandwidth efficiency, and simplicity, the most intuitive way to evaluate the quality of a modulation method is the BER of the system.  
The PPM-BPSK-SIM combines PPM and BPSK-SIM together, the symbol is modulated by a PPM encoder, then the parallel signal is transmitted to the BPSK modulator [27]. The conditional BER for various PPM is \(P_{LPPM}=\frac{1}{2}erfc\left(\frac{1}{2\sqrt{2}}\sqrt{\gamma^{\frac{1}{2}} \log_{2}L}\right)\)[28; 29; 30; 31], for L-ary pulse position modulation (LPPM), one data symbol consists of L time slots, only one time slot is valid and the rest are zero, that is, L is the average length of the symbol [28; 31]. According to Equations (2) and (8) in reference [11], the definite relationship of the conditional BERs between LPPM and L-ary pulse position modulation-binary phase shift keying-subcarrier intensity modulation (LPPM-BPSK-SIM) can be derived. Utilizing \(Q(x)=\frac{1}{2}erfc\left(\frac{x}{\sqrt{2}}\right)\), the conditional BER of the LPPM-BPSK-SIM is derived as  
\[P_{LPPM-BPSK-SIM}=Q\Big{(}\frac{1}{4}\sqrt{\gamma L\log_{2}L}\Big{)}. \tag{6}\]  
The unconditional BER of various PPM-BPSK-SIM is obtained as  
\[P_{LPPM-BPSK-SIM}=\int\limits_{0}^{\infty}Q\Big{(}\frac{1}{4}\sqrt{\gamma L \log_{2}L}\Big{)}f_{\gamma}(\gamma)d\gamma. \tag{7}\]
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
## 2 Sensors and Monitoring Technologies
### Airflow Sensors  
Air velocity has a direct effect on the thermal comfort of occupants in buildings and can subsequently impact their ability to work, sleep, and carry out other daily activities [69; 70]. These velocities are directly proportional to the heat exchange between occupants and the air surrounding them and are generally lower indoors (typically \(<\) 1 m/s) as compared to outdoor environments.  
The most common type of sensors in use today to measure air flow inside and outside buildings are called anemometers; devices designed to measure the velocity or force of a gas in a confined flow (e.g., air flow in a duct) or unconfined flow (e.g., wind outdoors) in any particular direction, and are available in two main types, i.e., (1) hotwire anemometers and (2) vane anemometers. Hotwire anemometers are thermoelectric devices consisting of a thin wire sensing element (typically, tungsten [71] or nickel [72] due to their high material strength and high temperature coefficient of resistance) which is electrically heated to a specified temperature followed by cooling, which enables the measurement of the air velocity proportional to the cooling rate of the anemometer. Hotwire anemometers provide fast response times (0.1-5 s) but are extremely sensitive to dust, humidity, corrosion and fluctuations in temperature. As a result, these sensors have a relatively narrow measurement range (0.1-25 m/s) and are only suitable for measurements in indoor environments where air velocities tend to be low. In comparison, vane anemometers; devices that combine a wind vane (for direction of airflow) with a propeller-like device to measure air velocity and wind speed [73], have a much larger measurement range (up to 45 m/s), fast response times (up to 0.25 m/s) and rigid device structures, making them more suitable for deployment on the exterior of buildings and other harsh environments [52; 74; 75; 76; 50].
## 3 FDIA Detection Model Based on GAN-GRU and Transformer
### GAN-GRU-Based Data-Balancing Process
#### 3.1.1 Network Structure of GAN-GRU
The data-balancing processing model based on GAN-GRU is shown in Figure 3.  
Both the generator module and the discriminator constitute the structural underpinning of a GRU. To facilitate the mapping of GRU hidden states to the dimensions of the output sequence, a linear layer is introduced. This pivotal linear layer plays a key role in generating specific data through the generator and distinguishing between real and generated samples via the discriminator. During the forward propagation process, the input sequence is initially processed by the GRU layer, generating both an output and a final hidden state. Subsequently, the last time step within the output sequence is selected and directed to the linear layer for transformation, yielding the generated result. Finally, this generated result is conveyed as output. In the pursuit of enhancing the generator's performance, an approach involves the introduction of random Gaussian noise as input to the generated data. Throughout the GAN-GRU training process, the noise value size is systematically trained, with adjustments being made based on the monitored fluctuations in the loss values of both the generator and discriminator. These adjustments are instrumental in aligning the generated data distribution more closely with that of authentic data, thereby amplifying the generative ability of the generator. Consequently, this approach effectively elevates the output quality of the generator, ensuring that the generated data exhibit statistical properties and distribution characteristics that closely mirror those of real data. The previous discussion illustrates that, in the GAN model employing GRU as both the generator and discriminator, the processing of input sequences by the generator module is accomplished through the utilization of the GRU layer. The conversion of the resultant hidden states into the dimensions of the generated outcomes is carried out by means of a linear layer, leading to the production of specific data. Conversely, the discriminator module exhibits a similar architecture whereby authentic and synthetic samples are distinguished. This distinction is achieved by subjecting the input sequences to the GRU layer and subsequently mapping the resultant hidden states to the dimensions of the evaluation outcomes using a linear layer. This design facilitates enhanced generation of high-quality  
Figure 3: Sample balanced structure of false data based on GAN-GRU. samples and precise differentiation between samples, thus contributing to the overall performance and effectiveness of the GAN model.
## 3 Delaunay-TCOGIM Algorithm
### Steps of the Delaunay-TCOGIM Algorithm
In this instance, the number of region components is 2. The region interior is a class, and the triangle mesh cells of the interior components with the color 0 are to be deleted, whereas the triangle mesh cells with the color 1 are outside the region and should be left intact. (5) Constructing a regional internal mesh: For the mesh delineation after region identification, the nodes are all mesh boundary points, and there are no regional internal points in the mesh delineation. Therefore, creating some regional internal points is necessary to complete the accurate mesh delineation of the terrain region. In Delaunay triangulation, in combination with the improved TCOGIM algorithm with the B-W algorithm, the discrete points of the boundary are discretized in either a clockwise or anticlockwise order to generate the triangular cell set D and to determine whether the triangular cells therein are boundary triangles. The boundary triangles are then added to the triangular cell set TE, and these indicate the boundary edges b,b. Calculate the center of gravity G of the boundary triangular cells and employ the right-handed spiral rule to judge b,b. Retain the triangle if the direction is positive and reject it if the direction is negative. Calculate the area of all boundary triangles and calculate the average. Compare the area of each triangle in the triangular cell set TE with the average comparison value. If the triangle's area is less than the average, proceed to the next triangle; if greater than that, insert the center of gravity to apply the B-W algorithm to re-cluster the triangles. The average measure of the newly generated triangle cell mesh area and the average value of the boundary triangle area are evaluated in a cyclical manner until all generated triangles satisfy the necessary conditions. Figure 3 shows the exact procedure for constructing the regional internal mesh in this process. Evaluate the Delaunay mesh: The Delaunay criterion mentioned in Section II is the empty circle property and the maximum minimal angle property. It is necessary to evaluate the Delaunay criterion for the triangular mesh generated by the combination of the Delaunay-TCOGIM algorithm, and only the triangular mesh that satisfies the Delaunay criterion is a Delaunay triangular mesh. If the determination procedure yields a triangular geometry that is not Delaunay, the diagonal exchange method must be used to obtain Delaunay triangles. Figure 4 illustrates the diagonal exchange technique: Remove the common side BD of the non-Delaunay triangles to obtain the quadrilateral ABCD, then connect the other diagonal AC to obtain two new triangles. The flowchart of the specific Delaunay-TCOGIM algorithm is shown in Figure 5.  
Figure 3: Constructing the regional internal mesh process. Figure 4: Delaunay mesh determination. Figure 5: Delaunay–TCOGIM algorithm flow.
## 2 Numerical Calculation Methodology
### Grid Generation and Independent Validation  
To ensure the precision of the simulation, a hexahedral structured grid was used to discretize the fluid domain of the fan. Since the upstream domain, downstream domain, and extension pipe are stationary domains, while the front rotor and rear rotor are rotating domains, they were divided into separate grids. Figure 2 shows the grid distribution for each component in the calculation domain. To guarantee the accuracy of the flow field near the boundary layer, the grid near the rotor wall boundary layer was refined, and the average value of \(y+\) is less than 1.2.  
Five grid schemes were selected to verify the accuracy of the adopted grid division, with nodes ranging from 5.13 million in Scheme 1 to 13.55 million in Scheme 5. The total pressure and efficiency of the fan under the 1.0 \(Q_{\text{BEP}}\) condition (\(Q_{\text{BEP}}\) is the flow rate at the best efficiency point) served as indicators for evaluating the quality of the grids. Figure 3 illustrates the trend of total pressure and efficiency with changes in the nodes. When nodes exceed 10 million (Scheme 3), the relative error in efficiency and total pressure is less than 0.3%. Considering computational accuracy, the final numerical simulation was conducted with 13.55 million nodes (Scheme 5).
###### Abstract
GeoSTAR uses a \(\gamma\)-shaped array incorporating hundreds of antenna feeds and receivers to sample the spatial Fourier spectrum of the radiometric field [21; 22; 23; 24]. To reduce the number of SAIR antenna elements required, instead of the stationary array of GeoSTAR, a rotating thinned array concept GIMS was proposed by the National Space Science Center (NSSC), Chinese Academy of Sciences (CAS) [25; 26]. GIMS applies a rotating circular antenna array of 70 elements working in time-sharing mode to realize the required spatial Fourier spectrum sample at 50-60 GHz with moderate system complexity at the expense of longer observation periods [27]. In summary, at present, GMR, GeoSTAR, and GIMS are the three promising GEO-MW sounder projects currently being studied. Although the three GEO-MW sounder projects based on different technology approaches have similar spatial resolutions, it is unknown whether there are similar impacts of assimilation of their data on NWP models. NWP models and meteorological satellites have been the most important achievements in the field of meteorology in the past half-century, and their developments complement each other [28]. On the one hand, a large number of satellite observations from an increasing number of sensors have been assimilated directly into NWP models to improve forecasting performance; on the other hand, with the increasing accuracy of numerical weather forecasting, NWP models are also used for quality assessment, instrument design, and optimization of meteorological satellite observation systems. Among them, the observing system simulation experiment (OSSE) is one of the technical approaches [29; 30; 31; 32; 33; 34]. The basic idea of an OSSE is to assimilate synthetic observations derived from an NWP model state assumed to represent the truth and then determine the impact on analyses and forecasts. To compare the potential impact of assimilation of the microwave observation data from GMR, GeoSTAR, and GIMSwith the prediction of typhoons, a reanalysis-based GEO-MW OSSE using the Weather Research and Forecasting (WRF) model and WRF Data Assimilation-Four dimensional variational (WRFDA-4 DVar) assimilation system was conducted in this study. The observed brightness temperature of the 50-60 GHz oxygen absorption band with the three instrument configurations (GMR, GeoSTAR, and GIMS) was investigated. Typhoons _Hagibis_ and _Bualoi_ were selected as cases to analyze the impact of assimilation of the GEO-MW observations on typhoon track forecasts. Both typhoons were super typhoons in the Western Pacific, which have the greatest potential threat to China. The remainder of this paper is organized as follows. Section 2 describes the data used in this study and Typhoons _Hagibis_ and _Bualoi_. The OSSE framework and each of its components is detailed in Section 3. Section 4 details the methods used for simulating the three GEO-MW sounders and the comparison of their brightness temperature observation capabilities. The GEO-MW OSSE experimental setup and results are introduced in Section 5, with a summary presented in Section 6.
# Failure Response of Simultaneously Pre-Stressed and Laser Irradiated Aluminum Alloys
## 1 Introduction
On the basis of the experimental results, an empirical formula was derived, which relates the stretching stress and rupture temperature. The present study experimentally examined the failure behavior of aluminum alloys exposed to combined tensile loading and laser irradiation. The purpose of the laser damaging was to explore the influence of laser power density on the strength degradation and failure time (the time from the start of laser irradiation to the failure of the specimen) of the preloaded specimens. Additionally, it was aimed to explore and provide a comparative behavior of two different kinds of aluminum alloys under simultaneous tensile loading and laser heating. To accomplish these objectives, Al-6061 and Al-7075 aluminum alloys were subjected to simultaneous tensile loading and CW ytterbium fiber laser irradiations. The deformation behavior and failure time were recorded by the tensile testing setup and a high speed video camera, respectively.
## 4 Verification of the Analytical Solution
### In Situ Monitoring Data Validation of the Analytical Solution  
(1) Project overview  
Zhongzhou Avenue is an important north-south expressway through the center of Zhengzhou City. Two rectangular pipe jacking machines with different sizes were used in the Zhongzhou Road undercrossing project to construct the left motorway and the right pedestrian channel. The cross-sectional sizes of the tunnel on the left and right lines were 7.5 m \(\times\) 10.4 m and 4.2 m \(\times\) 6.9 m, respectively. Detailed engineering information can be found in previous work [41]. In this paper, the monitoring section D59 was selected for analysis.  
(2) Comparison of transverse ground surface settlement  
The transverse settlement curves of the ground surface obtained by analytical calculation and the in situ monitoring data are shown in Figure 12.  
As shown in Figure 12, the trend of calculated and monitored ground settlement was basically consistent. The maximum settlements of the analytical calculation and in situ monitoring results were 37.35 mm and 33.49 mm, respectively. At the same time, it can be found that the overall data of in situ monitoring were smaller than the values of analytical calculation, which is because the grouting compensation method was used in the construction to lift the stratum. Due to the different dimensions of the left and right tunnels, the final surface settlement curve was asymmetric, and the settlement in the right part was significantly lower than that on the left.
## 4 Simulation and Effect
### Energy Saving Operations  
The outdoor lamps in this park were investigated to turn on and off the lights according to different seasons and time control [19]. Due to the large diurnal time difference between the different seasons in the region, we consider the time of day outdoor lamps are on throughout the year and obtain the data sampling period from 17:00 p.m. to 6:00 a.m. of the next day. In addition, weather factors have a great influence on the measurement results, and in order to reduce errors, the average value within a week was used as the absolute ambient light level and pedestrian flow for each time of the day. Therefore, measurements were taken from 5 September 2022 to 11 September 2022, for a duration of 1 week. The data acquisition system in this paper consists of a light sensor and a pedestrian flow sensor, which are controlled by an ultra-low-power STM32103C8T6 microcontroller. The WIFI module is used as the transmission method, and the MQTT protocol is used to transfer the collected data to the Onenet platform for data acquisition. The distribution of ambient light levels and pedestrian flow is shown in Table 3.  
Table 3 shows the pedestrian flow and natural light data collected in the field at ZT Park from 5:00 p.m. to 6:00 a.m. the previous day, respectively. As pedestrian traffic changes all the time, it is unreasonable to use the traditional control method, which wastes a lot of  
Figure 11: Structure design of the lawn lamp.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time** & **Pedestrian Flow** & **Normalized Value** & **Environmental Illumination(lx)** & **Normalized Value** \\ \hline
17:00 & 159 & 0.8548 & 150 & 1 \\
18:00 & 174 & 0.9354 & 90 & 0.5999 \\
19:00 & 186 & 1 & 20 & 0.1333 \\
20:00 & 90 & 0.4838 & 10 & 0.0666 \\
21  
energy. Therefore, the algorithm proposed in this study is to adjust the luminous intensity of the lawn lamp adaptively according to external factors in order to solve the problems of the prior art. Referring to Equation (1), the measured inputs are normalized to be in the range of (0, 1) and then brought into the fuzzy control to obtain the dimming coefficients for each time period, and then the power for each time period is shown as follows.  
As shown in Figure 12, the blue curve represents the lighting power consumption without control, and the red curve represents the lighting power consumption when using fuzzy control. The results show that the energy saving efficiency of the control system proposed in this paper can reach 38%.