esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Failure Probability Analysis  
To develop an analysis for the probability of failure, three assumptions were made related to statistical distribution. The first assumption was related to wood bat SoG. Since MLB implemented an allowable SoG range of \(\pm 3^{\circ}\), the wood requires sorting out which pieces of wood are outside of the range. It was assumed that a randomly selected professional-grade bat was equally likely to be any value within the allowable range, which means it can be represented as a uniform distribution. Further, the SoG was assumed to be uniform throughout the length of the bat. The second assumption was a normal distribution related to where the ball impact was located. Impact locations ranged from the 0.0 in (0.0 cm) location (tip of the barrel of the bat) to the 14.0 in (35.56 cm) location (inside-pitch scenario). By assuming a normal distribution where the 7.0 in. (17.78 cm) location is the mean, all data could be symmetrically incorporated into the statistical analysis. It should be noted that it is known that the sweet spot (at or very near to the vibrational node in the barrel) of the bat is located between the 5.5 and 6.0 in. (13.97 and 15.24 cm) locations. However, there are no data available to support that these spots are more likely to be impacted more often than other points on the bat. A distribution of how the impact locations are weighted is shown in Figure 6. The third assumption was that 80-100% of the maximum swing and pitch speed combinations consisted of three standard deviations (3\(\sigma\), 99.7%) of all impact velocities.  
Figure 5: Finite element model outcomes compared to high-speed video images: (**a**) NF, (**b**) SPF, and (**c**) MPF.  
Figure 6: Probability distribution of ball impact location relative to bat position.  
Within this third assumption, there is a 90 mph (145 kph) baseball pitch speed, and a 90 mph (145 kph) linear bat swing speed, at the 2.0 in (5.1 cm) location rotating around a point, measured at 6 in (15.2 cm) from the base of the knob. The study is limited to baseballs with no initial spin. Using the relationship between linear and rotational kinematics in Equation (1), it was assumed that the bat was swung at the same angular speed (of the bat swing), regardless of where the ball impacted the bat,  
\[\omega=\frac{\upsilon}{r} \tag{1}\]  
where \(\omega\) is the angular velocity, and \(v\) is the linear velocity of a point at a distance, \(r\), from the center of rotation. Using this relationship, a linear bat speed was calculated for every impact location down the length of the bat. Using a Z-score and Z-table lookup, the probability was calculated for each of the tested impact velocities. As the resolution of the finite element simulations was 5 mph (8 kph), data points just outside the \(\pm 3\sigma\) values were included in the analysis, creating a certainty of at least 99.7%. Coupling the changing swing speed with the pitch velocity, with respect to impact location, created a moving combined swing and pitch speed window that accounted for 3\(\sigma\)+ of all possible impact velocities.
## 2 Materials and Methods
### Collecting and Processing Multispectral Images  
At 60 days after crop emergence (DAE), we performed a flyover using the Sensefly eBee RTK, a fixed-wing remotely piloted aircraft (RPA), equipped with autonomous takeoff, flight plan and landing control. The objective was to collect spectral information from six different sorghum hybrids. For this task, the RPA was equipped with a Parrot Sequoia multispectral sensor. The Sequoia sensor is a multispectral camera widely used in various agricultural activities. It utilizes a sunlight sensor combined with an additional 16-megapixel RGB camera for recognition purposes. This setup allowed us to obtain accurate and comprehensive data on the spectral characteristics of sorghum hybrids, contributing to a more detailed and meaningful analysis of our study. The adopted multispectral sensor has a horizontal angle of view (HFOV) of 61.9 degrees, a vertical angle of view (VFOV) of 48.5 degrees and a diagonal angle of view (DFOV) of 73.7 degrees. The flight took place at 09:00 (local time) in the morning, in a cloudless condition and at an altitude of 100 m, with a spatial resolution of 0.10 m. The aerial survey was carried out using RTK technology, which made it possible to position the sensor to collect the images with an accuracy of 2.5 cm. The images were mosaicked and orthorectified using Pix4 Dmapper.  
Radiometric calibration of the sensor was carried out with a factory-calibrated reflective surface. Parrot Sequoia has a brightness sensor that allows the acquired values to be calibrated. The wavelengths (SB) acquired by the sensor were: blue (475 nm, B_475), green (550 nm, G_550), red (660 nm, R_660), Rededge (735 nm, RE_735), and NIR (790 nm, NIR_790) according Figure 2. Once the SB data had been acquired, it was possible to calculate the vegetation indices (VIs) described in Table 1.  
Figure 1: Location of the experimental area in Chapadão do Sul-MS, Brazil. Hibryds: 50A60 (H1), ADV1221 (H2), JB1330 (H3), NTX202 (H4), RANCHEIRO (H5) and SLP20K6D (H6).
# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 3 Measurement of Fruit Size  
### Current (Manual) Methods  
The size of fruit on-tree can be assessed non-invasively using calipers for lineal dimensions or sizing rings for circumference measurements. Allometric relationships can allow the estimation of fruit weight from such measurements (Table 5). However, such measurements are typically made some weeks before harvest and as fruit growth continues until harvest it is necessary to establish a growth model for the prediction of harvest size or weight (Table 5). In commercial use, the estimated fruit weight distribution is typically converted to a commercial box weight distribution, given a relationship between a range of fruit mass and a given box size.  
Manual estimates of fruit size of fruit on-tree require a sampling strategy, with attention to both variation between trees and to variation within a tree. The SUR sampling methodology used by [PERSON] et al. (2019) provides one approach.
## 2 Experimental Methods and Materials
### Data Analysis  
The overall experimental design and data analysis pipeline of this study is briefly illustrated in Figure 1. Following data collection, all spectral analysis was carried out in Matlab R2014b (The Mathworks, Natick, MA, USA). Prior to data analysis, the spectra were truncated to only include the 700-1800 cm\({}^{-1}\) range, as our preliminary analysis (data not shown) of the entire range (400-2300 cm\({}^{-1}\)) suggested that the 400-700 and 1800-2300 cm\({}^{-1}\) spectral regions for CBEx 785 and 1064 nm instruments, and spectral regions of 350-700 and 1800-2000 cm\({}^{-1}\) for the SORS data on the Resolve, had no significant discriminatory contribution to the model, and therefore were excluded from analysis.  
All Raman spectra were then scaled using standard normal variate (SNV) [28]. In preliminary studies, we also assessed extended multiplicative scatter correction (EMSC) [29], but this resulted in poorer predictions (data not shown) so we chose to use SNV. As each instrument had built-in automatic baseline correction algorithms, further correction prior to scaling was not necessary. Principal component analysis (PCA) was applied for exploratory analysis [30]. PCA is an unsupervised method to visualize sample distribution in the multivariable space, reduce the dimensionally of a data set, check for any trends and clusters that could be linked to the percentage of adulterants in the samples, or to identify possible outliers [31; 32; 33].  
Partial least-squares regression (PLSR) [34; 35] is a popular multivariate regression method that is employed for quantitative calibration and we used this to predict the level of adulterant from the Raman spectra of the oils. In order to validate the model statistically, bootstrapping [1; 36] of the PLSR models was employed, which involved resampling of the spectral data with replacement. This process was used to generate two data sets randomly: one is termed the training set and is used to calibrate the PLSR model; the second is used as a test set which is employed to test the predictive ability of the model [37]. Plots were generated of known adulterant levels versus PLSR prediction and were used to assess prediction accuracy. In addition, we complied statistical metrics in terms of accuracy of measurements, linearity of predictions and the limit of detection (LOD) from these PLS models using methods described in [38; 39].  
With SORS, the data analysis is more complex and the company that manufactures the instrument has optimised the data processing for categorical analysis only; that is to say, the instrument is optimised for predicting the provenance of a substance belonging to one class or another. This is fundamentally different, and we found that this affected our ability to perform quantification. Thus, for quantitative analysis using SORS, we collaborated with the manufacturers to access the raw data then applied the chemometrics methods discussed above and this is visualised in Figure 1.
# Review of Radiation-Induced Effects in Polyimide
###### Abstract  
Polyimide (PI, Kapton-H(r)) films are widely utilized in the spacecraft industry for their insulating properties, mechanical durability, light weight, and chemical resistance to radiation. Still PI materials remain exposed to a combination of high-energy electrons, protons, and ultraviolet (UV) photons, particles primarily responsible for radiation-induced damage in geosynchronous Earth orbit (GEO), which drastically change PI's properties. This work reviews the effect of electron, proton, and UV photon irradiation on the material properties (morphology, absorption, mechanical properties, and charge transport) of PI. The different damaging mechanisms and chemical consequences that drive changes in the material properties of PI caused by each individual kind of irradiation will be discussed in detail.  
polyimide; geosynchronous Earth orbit (GEO); electron-irradiation; proton-irradiation; UV photons impingement; optical properties; charge transport; space weather +
Footnote †: journal: Accepted: 5 May 2019; Published: 16 May 2019
## 4 Performance Metrics for Path Loss Models  
Performance metrics for path loss models are essential in evaluating the accuracy and effectiveness of models used to predict signal propagation and attenuation in wireless communication systems [21; 35; 50; 51]. These models play a critical role in various applications, such as network planning, resource allocation, and the optimization of wireless communication systems. Some common performance metrics for path loss models include, e.g., mean square error (MSE), mean absolute error (MAE), mean error (ME), and R2 score [50; 51].  
These performance metrics help researchers, engineers, and network planners assess the suitability of a path loss model for specific applications. The choice of which metrics to use depends on the specific goals of the study and the nature of the path loss modeling problem. These common performance metrics are discussed below.
## 4 Discussion  
There is clear potential to integrate SAR data into Flood Foresight to improve the accuracy of the near real-time flood estimates produced by the model. The three examples described in this paper were each very small domains and used VHR SAR images, thus resulting in high levels of accuracy. However, VHR SAR data are rarely open access, and the reliance on LiDAR limits use of the method to urban regions of the globe that have been mapped by airborne LiDAR. It would obviously be more attractive to use global data sets that are easily accessible in an emergency. A suitable SAR candidate is Sentinel-1 because of its open access, the increased coverage of floods it provides globally using its wider swath, its high-resolution and preplanned acquisition capability, and the availability of georeferenced images in near real-time. Although perhaps not as accurate as using VHR SAR and LiDAR, the change detection method of [PERSON] et al. (2021) [48] using Sentinel-1 and the WorldDEM DSM is capable of detecting urban flooding. Future work will investigate the merging of Sentinel-1 flood extents with model-derived flood hazard maps. If the approach can be applied to Sentinel-1 imagery, an automated process for a merged model-observational approach with coverage across much larger swathes of the country would be achievable and a cost-effective option for use by flood-response organisations.  
Intuitively, merging modelled and remote observations of flooding has numerous benefits, some evidenced in the cases presented here. Although this study has shown an increase in accuracy and decreases in false negatives and false positives by combining SAR and model data, it is clear that there are high degrees of accuracy in the model that can provide valuable information to responders. The model has several advantages over the SAR due to its ability to provide near-real-time data 24/7/365 across the whole country. In addition, the locations in this study, although classed as 'urban', cannot be said to have a high degree of urban density. Towns and cities with higher density will increasingly suffer from shadow and layover issues affecting the ability to discriminate flood water in built-up areas. This will be exacerbated in instances where the coarser resolution Sentinel-1 data are used. In these cases, modelled inundation maps (such as those from Flood Foresight) are able to estimate flooded regions in the blind spots of the SAR. Expansion of the test areas to denser urban areas and across larger domains should therefore be explored further.  
This study has explored merging model data generated from telemetered streamflow data from in-situ river gauges. Integrating SAR data (when available) was shown to improve the accuracy of flood detection in all three cases shown (Table 4). However, there is also the potential to improve the forecasting performance of the model using data assimilation, as alluded to in Section 2.3.2 although this will pose challenges given the precomputed simulation library approach used in Flood Foresight.
## 4 Flexural Loading Test Results and Discussion
### Opening at Interface between PCa Concrete and Joint  
During the loading tests, the opening at the bottom of the interface between the PCa concrete and the joint of the specimens was measured by three Pi-shape gauges attached along each interface, as shown in Figure 5b,d. Each Pi-shape gauge could measure up to 5-mm opening displacement. Figure 8b shows the relationship between the load and the average opening displacement at the bottom of the interface. The figure includes an enlarged view of the initial stage of the load.  
The crack opening at the interfaces of the Type B and C specimens occurred at loads of 36 and 38 kN, respectively, whereas that of the Type A and D specimens occurred at a load of 10 kN. This difference stemmed from the fact that the tensile bond strength of the PVA-FR mortar to the PCa concrete was larger than that of the concrete in the joint. Between the Type B and C specimens, which had different overlapping lengths of the loop reinforcements, the behaviors up to 90 kN were almost the same, but the crack widths differed beyond this load. The crack opening of the Type B specimen, which had a longer overlapping length, was smaller than that of the Type C specimen at the same load. The Type C and D specimens had the same overlapping length of the loop reinforcements but different joint materials. The load of the Type D specimen, which had concrete in the joint, began to drop from 130 kN, whereas the load of the Type C specimen, which had PVA-FR mortar in the joint, gradually increased with the opening displacement. Furthermore, the load-opening displacement relationship (Figure 8b) was similar to the load-deflection relationship (Figure 8a). Thus, the crack opening at the bottom of the interface between the PCa concrete and the joint affected the deflection of the specimen under flexural loading.
## 3 Voltage Stability of a Multi-Infeed LCC-HVDC System
### Voltage Stability Factor (VSF)
The power stability concept about a single infeed HVDC system is discussed in [19]. The same concept is extended for multi-infeed HVDC in [30]. The concept uses the MPC technique and eigenvalue decomposition method to analyze the power stability. The MPC method provides only a single degree of freedom to control the stability, whereas the eigenvalue-based technique gives more freedom to control the system stability by using various parameters. To get a mathematical model relating the DC power and direct current of a multi-infeed system, it is assumed that no active and reactive powers are injected. Only the DC power is considered, which converts the AC/DC Jacobian matrix to a DC-power-DC-current-reduced Jacobian matrix. The MAP of each converter in a multi-infeed HVDC system is obtained by taking the parameters of all other converters as constant. The DC power and DC current would be in-phase for a positive eigenvalue and vice versa. Hence, for a stable DC power, the eigenvalue of the DC-power-DC-current-reduced Jacobian matrix must be greater than zero.
## 4 Performance Evaluation  
We aim to utilize the advantages of the hexagonal time-frequency lattice structure to decrease the symbol duration in communication systems with short-bursts, e.g., IoT devices. In the proposed hexagonal multicarrier scheme, we apply the polyphase decomposition to reduce computational complexity. Thus, the proposed method can support less symbol duration with fewer calculation processes. In the following subsections, the computational complexity and latency analysis of the proposed hexagonal waveform in Figure 2 are given.
# Hierarchical Parallel Evaluation of a Hamming Code
## 2 Preliminaries  
### Hamming Code  
Given are \(n=2^{m}-1\) bits of data, for some \(m\geq 2\). According to Hamming scheme, they are indexed from \(1\) to \(n\), and the bits at positions with indices that are powers of two serve as parity bits, so that in fact only \(n-m\) bits carry data. The \(i\)th parity bit--which will be stored at position \(2^{i-1}\) for \(i=1,2,\ldots,m\)--will be set so that the XOR (exclusive or) of some selected bits will be zero. For the parity bit at \(2^{i-1}\), the selected bits are those whose indices--when written in standard binary notation--have a \(1\)-bit in their \(i\)th bit from the right. That is, the parity bit stored in position \(2^{0}=1\) is the XOR of the bits in positions \(3\), \(5\), \(7\),..., the parity bit stored in position \(2^{1}=2\) is the XOR of the bits in positions \(3\), \(6\), \(7\), \(10\), \(11\),..., and so on.  
An easy way to remember this procedure is by adding a fictitious first \(0\)-bit, indexed \(0\), and then scanning the resulting binary string as follows. To get the first parity bit (the one stored in position \(2^{0}=1\)), compute the XOR of the sequence of bits obtained by repeatedly skipping one bit and taking one bit. This effectively considers all the bits with odd indices (the first of which, with index \(1\), is the parity bit itself). In general, to get the second, third,..., \(i\)th parity bit, the one stored in position \(2^{i-1}\), for \(i=1,2,\ldots,m\), compute the XOR of the sequence of bits obtained by repeatedly skipping \(2^{i-1}\) bits and taking \(2^{i-1}\) bits.  
Consider as a running example \(n=15=2^{4}-1\) bits, of which only \(15-4=11\) contain data, and assume the data is the standard eleven-bit binary representation of 1483--namely, 10111001011. This will first be stored as a string in which the bit positions indexed from left to right by powers of two are set to \(0\), and the eleven information bits are filled in the other positions. Adding the \(0\)-bit at position \(0\) yields  
\[0\ \boldsymbol{0}\ \boldsymbol{0}\ 1\ \boldsymbol{0}\ 0\ 1\ 1\ \boldsymbol{0}\ 1\ 0\ 0\ 1\ 0\ 1\ 1, \tag{1}\]  
where the zeros in the positions reserved for the parity bits have been emphasized. The parity bits in positions \(1\), \(2\), \(4\), and \(8\) are then calculated, in order, by XORing the underlined bits in the following lines,  
\[0\ 0\ 0\ 1\ 0\ 0\ 1\ 1\ \boldsymbol{0}\ 1\ 0\ 0\ 1\ 0\ 1\ 1\,\]  
which yields, respectively, the bits \(0\), \(1\), \(1\), \(0\). The final Hamming codeword is therefore  
\[0\ \boldsymbol{0}\ \boldsymbol{1}\ 1\ \boldsymbol{1}\ 0\ 1\ 1\ \boldsymbol{0}\ 1\ 0\ 0\ 1\ 0\ 1\ 1. \tag{2}\]
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 3 Smart Grid Cyber-Physical Attacks  
The traditional power grid is a geographically distributed system, which mainly suffers from infrastructure exposure to the external environment throughout all weather conditions [8]. However, with the development of ICTs, the smart grid vulnerabilities have been expanded into the cyber-physical aspect.  
Smart grid control systems have inherited the shortcomings from both the traditional IT infrastructure and the deployment of a Distributed Phaser Measurement Unit (PMU), which means traditional IT attacks like DoS attacks, false data injection, and Malware Injection are affecting smart grids. On the other hand, smart grid control systems are suffering from cyber-physical attacks, which are aimed at critical physical devices that operate in the control system. Once the critical devices in power systems like generators are cyber-physically attacked, it may cause irreversible damage to the devices.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Stability of ISBs
#### 3.3.2 Long-Term ISB
Then, the TCM RTK can be obtained to avoid  
Figure 5: Time-series, probability density function (PDF) and quantile against the quantile (QQ) plot of both phase and code ISBs between B1C/L1 (**a**), B1C/E1 (**b**), B2a/L5 (**c**), and B2a/E5a (**d**) with the observations from DOY 29 to 70, 2021 between the baseline GODN-GODS with identical types of receivers. The magenta line in the PDF of ISB denotes the empirical normal PDF. The \(x\)-axis of the QQ plot denotes the quantile of the standard normal distribution, while the \(y\)-axis denotes the quantile of our sample distribution as shown in the time series. The dotted line in the QQ plot denotes the empirical normal distribution of the sample against the standard normal distribution, while the scatters denote the real sample distribution. the waste of pivot satellites. Importantly, we found the phase ISBs maintained zero-mean characteristics for all the frequency pairs over the entire period, because the phase ISBs may seriously impact ambiguity resolution in TCM. Once we ignore the influence of phase ISBs, they may destroy the integer nature of the DD ambiguity so that ambiguity resolution becomes difficult.