esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Evaluation of the Accuracy of Determining the Position Coordinates Using the USBL System  
Determining the horizontal coordinates using USBL system is based on the measurement of two parameters: direction \(\alpha\) (yaw) and the distance between the Transceiver and the Transponder \(r\) (Figure 2).  
The mean error \(M_{xy}\) and the mean error ellipse (i.e., the length of the \(a\) and \(b\) axes) of determined horizontal coordinates \((x,\ y)\) using the USBL system can be calculated by applying the law of mean errors propagation [26; 27; 28]. Knowing the functions of the individual test result:  
\[\alpha=\text{arctg}\frac{x}{y} \tag{1}\]  
\[r=\sqrt{x^{2}+y^{2}} \tag{2}\]  
the equations of position lines mean error can be represented by:  
\[\sigma_{l(\alpha)}=\sigma_{\alpha}/\sqrt{\left(\frac{\partial\alpha}{\partial x }\right)^{2}+\left(\frac{\partial\alpha}{\partial y}\right)^{2}} \tag{3}\]  
\[\sigma_{l(r)}=\sigma_{r}/\sqrt{\left(\frac{\partial r}{\partial x}\right)^{2}+ \left(\frac{\partial r}{\partial y}\right)^{2}} \tag{4}\]  
that enables determination of:  
\[M_{xy}=\frac{1}{\text{sin}\theta}\sqrt{{\sigma_{l(\alpha)}}^{2}+{\sigma_{l(r)} }^{2}}=\sqrt{\left({\sigma_{\alpha}\cdot r}\right)^{2}+{\sigma_{r}}^{2}} \tag{5}\]  
\[a=\sigma_{\alpha}\cdot r \tag{6}\]  
Figure 2: The principle of determining the horizontal coordinates \((x,\ y)\) using USBL system.  
\[b=\sigma_{r} \tag{7}\]  
where \(\theta\)--the intersection angle of the position lines. In the case of USBL it always equals \(90^{\circ}\); \(\sigma_{\alpha}\)--\(\alpha\) (yaw) mean measurement error; \(\sigma_{r}\)--\(r\) (distance) mean measurement error; \(r\)--distance between the Transceiver and Transponder; \(a\)--the length of the major axis of the mean error ellipse; \(b\)--the length of the minor axis of the mean error ellipse.  
Based on the dependence Equation (5), information provided by the manufacturer, and the values of the mean errors of measurement, \(\sigma_{\alpha}=3^{\circ}\) i-\(\sigma_{r}=0.2\) m, an area map has been compiled on the accuracy of position coordinates, determined by the Tritech USBL \"MicronNav System\" (Figure 3) [24].  
Figure 3 shows that the mean errors of position coordinate increases with the measured distance. It reaches 26.2 m at a distance of 500 m. This is the distance limit at which the USBL \"MicronNav System\" can work [24].  
Figure 4 shows the mean error ellipse of position coordinates determined in the same direction, in one hundred meter intervals.  
It shows clearly that the coordinate error increases substantially in a perpendicular direction to the measurement direction with increasing distance. However, it is constant in direction of the measurement being carried out, and is only 0.2 m.  
Figure 4: Changing the parameters of the mean error ellipse as a function of measured distance.  
Figure 3: Mapping the accuracy of the coordinate position of determined USBL system (describes mean error \(M_{xy}\) in meters).
# Signaling in the Tomato Immunity against _Fusarium oxysporum_
## 4 Materials and Methods
### Plant Material and Fungal Inoculation  
Tomato cultivars Momor (MOM) and Movione (MOV), which are isogenic lines of Moneymaker tomato (_Solanum lycopersicum_ L.), were acquired from the Tomato Genetics Resource Center, UC Davis (accessions LA2828 and LA3472, respectively). MOV plants contain the \(I\) gene which confers resistance to _Fol_ race 1 resulting in an incompatible interaction, while MOM plants lack the \(I\) gene, thus being unable to recognize the fungus which results in a compatible interaction and the development of disease [8].  
Seeds were surface sterilized with a 1:1 mixture of commercial sodium hypochlorite and distilled water and were placed in wet vermiculite and irrigated with Hoagland solution. A total of 36 MOM and 36 MOV plants were used for each experiment. Two-week-old plants were pulled up from the vermiculite and inoculated. Half of them were mock-inoculated with water and the rest were infected with _Fol_ by immersing the injured roots of two-week-old plants in the fungal spore suspension for 5 min according to [4]. Then, tomato plants were transferred into hydroponic conditions in Hoagland solution and oxygen supply under controlled conditions with a 16 h photoperiod at 5000 lux, a temperature of 25 \({}^{\circ}\)C/20 \({}^{\circ}\)C, and a relative humidity of 70%. Hypocotyls and leaves were sampled independently after 3, 7, and 14 days post-inoculation (dpi), homogenized under liquid nitrogen, and stored frozen at \(-80\) \({}^{\circ}\)C for later analysis.  
MOM and MOV plants were visually inspected for the evaluation of symptoms, and the disease index [17] was scored from 72 biological replicates at 3, 7, and 14 dpi. This time course was selected since susceptible infected MOM plants totally collapsed at 14 dpi, therefore choosing regular intervals of one week (7 and 14 dpi) and half a week (3 dpi). Three independent experiments were performed and data from a representative one are shown.
# Research Progress on Curved Plates in China: Applications in Architecture
## 3 Acoustic Characteristics of Curved Plates
### Principle of Surface Acoustic Design
Reflection effect of an incident plane wave in the local groove: (**e1**) structure schematic diagram, (**e2**) diagram of secondary-source radiation generated from independent grooves, and (**e3**) diagram of wavefront generated by multiple local secondary-source radiation [60], where the incident sound wave is not drawn and the asterisk indicates the secondary sound source. widely used in the acoustic design of a theater. The effect of acoustic diffusion processing also depends on the size of the diffuser, and only when the size and appearance of the acoustic wave are diffused can the ideal diffusion effect be achieved. Therefore, [PERSON] [60] discussed the influence of the form and size of a diffuser on the diffusion effect, and research showed that the effect of a cylindrical diffuser is better than that of a triangular diffuser (Figure 2d). The diffusion effect of the diffuser increases with increasing sound frequency and diffuser size. Therefore, to ensure the diffusion of low-frequency sound, a diffuser should be of a certain size. Cylindrical diffusers have great advantages. If the size and shape design of a cylindrical diffuser are too simple, the reflection wavefront will interfere and affect sound diffusion. Therefore, to obtain the ideal diffusion effect over a wide frequency range, several diffusers of different sizes and shapes should be combined (Figure 2e). Currently, many concert halls design acoustic diffusion walls according to quadratic residue in number theory [62]. When a reflection surface is broken (i.e., by a relief) or has grooves of different depths (Figure 2(e1)), acoustic energy can be effectively diffused. The principle is that when a sound wave reaches a wall, the sound wave propagates into the groove and groove surface to generate reflections, forming a secondary sound source (Figure 2(e2)). The time difference between the two contact interfaces leads to reflected sound waves of different phases, forming an irregular wavefront (Figure 2(e3)), which are superimposed together into local nondirectional reflections, and this type of nondirectional reflection benefits sound diffusion. On the whole, a large number of irregular concave and convex grooves form diffusion reflections of sound overall [63], which has better diffusion characteristics than conventional diffusers with general shapes and can obtain ideal diffusion effects over a wide frequency range [64]. Therefore, maximum length sequence (MLS) diffusers [65] and quadratic residue diffusers (QRDs) [66] were derived as supplements to architectural design. Overall structural designs of large sizes are combined with the undulation of the surface of the diffusion body to obtain a great sound diffusion effect [67], which is widely used in practical theater design. Therefore, the application of curved-surface acoustic characteristics is emphatically described in the following section based on typical domestic theaters.
## 3 Results
### Explainability Plots for the Optimal Prediction Model  
The optimal model for this set of experiments was the one based on the LightGBM algorithm. It achieved an overall accuracy score of 85.82%, a sensitivity score of 86.92%, and a specificity score of 81.48%. Figure 3 depicts [PERSON]'s effect sizes for each feature, and Figure 4 displays the relative summary plot using the SHAP values for every feature used as input.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **SVM** & **CatBoost** & **LightGBM** & **AdaBoost** \\ \hline Sensitivity & 91.59\% & 86.91\% & 87.85\% & 90.65\% \\ Specificity & 44.44\% & 66.67\% & 66.67\% & 40.74\% \\ Accuracy & 82.09\% & 82.84\% & 83.58\% & 80.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Stratified CV-10 metrics for each ML model—without oversampling.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **SVM** & **CatBoost** & **LightGBM** & **AdaBoost** \\ \hline Sensitivity & 85.05\% & 86.92\% & 86.92\% & 84.11\% \\ Specificity & 77.78\% & 77.78\% & 81.48\% & 77.78\% \\ Accuracy & 83.58\% & 85.07\% & 85.82\% & 82.84\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stratified CV-10 metrics for each ML model—after oversampling.  
Figure 3: [PERSON]’s effect size for each input feature sorted by magnitude of effect.  
Figure 2: Confusion matrices of the ML prediction models; (**a**) depicts SVM, (**b**) depicts Catboost, (**c**) depicts LightGBM, and (**d**) depicts AdaBoost.  
In Figure 3, the features are ranked based on their impact on the prediction result. The higher the impact, the higher the feature appears in the plot. More specifically, the number of deficiencies index feature has the highest impact on the prediction result, followed by the Wisconsin index and the max diameter index. On the other hand, the sex of the subject is characterized by the lowest overall impact.  
In a similar tone, in the summary plot of SHAP values (Figure 4), the features are also sorted in order of magnitude of effect. Positive SHAP values indicate that the particular range of values for a specific feature leads the prediction towards the MG class. For instance, when the value of the feature for the number of deficiencies is high, it contributes towards an MG classification. Instead, when the maximum diameter index has high values, it contributes towards an S classification (i.e., adenoma).
## 3 Passively Augmenting Mobile Devices
### Leveraging Built-In Sensors and Elements
#### 3.2.3 Motion Sensors  
Most mobile devices have at least one kind of built-in motion sensor (e.g., accelerometer, gyroscope, etc.). They are perfect to continuously control the parameters of sound synthesizer and have been used as such since the beginning of mobile music (see Section 1.3).  
Augmentations can be made to mobile devices to direct and optimize the use of this type of sensor. This kind of augmentation can be classified in two main categories:  
* augmentations to create specific kinds of movements (spin, swing, shake, etc.),
* augmentations related to how the device is held.  
Figure 7 presents a \"sound toy\" where a mobile device can be spun like a top. This creates a slight \"Leslie effect\", increased by the passive amplifier. Additionally, the accelerometer and gyroscope data are used to control the synthesizer running on the device. This instrument is featured in the teaser video of the workshop presented in Section 3.4.  
Another example of motion-sensor-based augmentation is presented in Figure 8 and described with more details in Section 3.4. It features a smart-phone mounted on a bike wheel where, once again, the gyroscope and accelerometer data are used to control the parameters of a synthesizer running on the device. Similarly, a \"rolling smart-phone\" is presented in Figure 8 and described in Section 3.4. Mobile3D contains a series of templates and functions to make this kind of augmentation.  
Augmentations leveraging built-in sensors related to how the device is held are presented in more detail in Section 3.3.  
Figure 7: Mobile-device-based top creating a “Leslie” effect when spun.
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 3 Performance Evaluation
### Performance with Ordered Channel Gain
\begin{array}{ll}S_{1}&=\mathbb{P}(|\rho_{1J}|^{2}\geq\zeta,|\rho_{2J}|^{2}< \frac{T_{2}}{\alpha\beta K_{2}})\\ &=2\int_{0}^{\frac{T_{2}}{\alpha\beta K_{2}}}f_{|\rho_{2J}|^{2}}(y)dy\int_{y}^{ \zeta}f_{|\rho_{1J}|^{2}}(x)dx\\ &=2\int_{0}^{\frac{T_{2}}{\alpha\beta K_{2}}}\bigg{(}1-\epsilon_{1J}\sum_{k=0}^ {\infty}\frac{(m_{1J})x_{1J}^{k}}{(k!)^{2}\kappa_{1J}^{k+1}}\gamma(k+1,\zeta \kappa_{1J})\bigg{)}f_{|\rho_{2J}|^{2}}(y)dy\\ &=2\epsilon_{2J}\sum_{k=0}^{\infty}\frac{(m_{2J})x_{1J}^{k}}{(k!)^{2}\kappa_{ 2J}^{k+1}}\gamma(k+1,\frac{T_{2}}{\alpha\beta K_{2}}\kappa_{2J})-2\int_{0}^{ \frac{T_{2}}{\alpha\beta K_{2}}}_{Q}\underbrace{e^{-\kappa_{2J}y}\epsilon_{2J 1}F_{1}(m_{2};1;\
u_{2J}y)\epsilon_{1J}\sum_{k=0}^{\infty}\frac{(m_{1J})x_{1J }^{k}}{(k!)^{2}\kappa_{1J}^{k+1}}\gamma(k+1,\zeta\kappa_{1J})}_{Z(y)}dy\\ &=2\epsilon_{2J}\sum_{k=0}^{\infty}\frac{(m_{2J})x_{2J}^{k}}{(k!)^{2}\kappa_{2J }^{k+1}}\gamma(k+1,\frac{T_{2}}{\alpha\beta K_{2}}\kappa_{2J})-\frac{T_{2}\pi}{ \alpha\beta K_{2}N}\sum_{n=1}^{N}\sqrt{1-x_{n}^{2}}Z(y_{n,2}),\end{array}  \tag{23}\]  
where \(x_{n}=\cos(\frac{2n-1}{2N}\pi)\) and \(y_{n,2}=\frac{(x_{n}+1)T_{2}}{2\alpha\beta K_{2}}\). Consequently, we obtain the expression of \(Pr_{2}=Pr_{1}+S_{1}\).
## 4 Case Study: NSS Data from a UK University
### Phase 1: Textual Analysis with Machine Learning  
#### 4.1.1 Classification of Open-Ended Responses  
It should be noted that, in this study, the statement categorization was the performance with an individual sentence, rather than with the whole response. This considers that a single response may consist of statements in multiple categories. For example, Figure 5 shows a response that falls into four problem statement categories. Accordingly, these responses were broken down to 10,328 sentences.  
About 20% of sentences were randomly selected and manually annotated against the annotation scheme (Table 1). As 10% had been done at Stage 1, this annotated another 10% and produced 2000 labeled data. The annotation in both stages was undertaken by two senior lecturers at the institute from which the NSS data came. The unlabeled data consisted of the remaining 8328 sentences, which were automatically analyzed later by text mining.  
Furthermore, we trained (with 70% of the labeled data, i.e., 1400 sentences) the text-mining component with the following algorithms: Random Forest (RF); Multinomial Naive Bayes (MNB); and Support Vector Machines (SVM). Their prediction accuracies (against a held-out 30% of the labeled data) are shown in Figure 6.  
Figure 5: Example of a single student response consisting of multiple problem statement categories.  
Figure 6: Predictive accuracy (%) of the Multinomial Naive Bayes (MNB), Random Forest (RF), and Support Vector Machines (SVM) algorithms when applied to the evaluation sample of the NSS dataset.  
Overall, the SVM algorithm yielded the best average accuracy (50% macro-average across all classes), although its performance improvement over MNB (+1.3%) was low. It should be noted that the accuracy obtained by the three algorithms varied across the 10 problem statement categories. For example, the 'Placement and employability' category showed the best predictive accuracy of all the categories with the RF algorithm, achieving a robust performance of 92.5%. The high performance in this category could be explained by the fact that many student responses relevant to 'Placement and Employability' contained the word 'placement', which was used by the underlying algorithm as a discriminating feature to categorize such problem statements accurately. Two other categories, namely 'Overall dissatisfaction' and 'Student life and social support', achieved a very low accuracy of approximately 2-14% and 17-20%, respectively, attributed to the limited number of training examples available in these two categories.  
As a result, the trained SVM component was chosen to categorize the responses in the unlabeled data. Figure 7 shows the distribution of the statements in each category. Although the classification accuracy of the trained SVM was not high for all classes, we still consider this useful for increasing the amount of silver-standard labeled data that we are able to provide to our topic-modeling algorithm. The results shown in the next section indicate that the topic modeling algorithm was able to produce coherent outputs when using our automatically labeled data.
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 1 Introduction
In 2021, China successfully launched a new altimetry, satellite-HaiYang-2D (HY2D), which is a marine operational satellite that can provide precise ocean dynamic environmental information for the warning and forecasting of marine disaster, continuously measuring the sea surface height and sea surface wind and conducting marine scientific research. In order to guarantee the reliability of satellite orbits for radar altimetry mission, HY2D is equipped with a GPS and BDS receiver and carries a laser retro-reflector array for satellite laser ranging (SLR). Therefore, this paper adopts spaceborne GPS and BDS data to conduct research about the POD of HY2D, mainly including the model strategies used in POD processing, Global Navigation Satellite System (GNSS)-derived orbit analysis, and the validation of SLR residuals for HY2D. The relevant research results can lay an important foundation for the development of subsequent altimetry satellites, and that in turn can lay an important foundation for the development of a spaceborne BDS receiver. This paper is structured as follows. Section 2 introduces materials and mainly consists of general information about the HY2D satellite and data collection. Section 3 mainly presents the POD method and strategies. All results and discussion obtained based on the above methods and strategies are given in Section 4. Finally, conclusions are given in Section 5.
# Simulation of Gross Primary Productivity Using Multiple Light Use Efficiency Models
## 5 Conclusions  
In this study, five LUE models and the multi-model ensemble method were evaluated using measured daily carbon flux and meteorological data in the Heihe River Basin. The measured carbon flux data indicate that the GPP varied greatly among the different ecosystems in the Heihe River Basin from upstream to downstream. The cropland and wetland in the middle stream had the highest carbon uptake capacities. Performance of the LUE models indicated that the LUE models contain uncertainties resulting from model parameters and model struct. Single LUE model performance is not robust in different vegetation types. The LUE models without parameter calibration exhibited great inconsistencies in GPP simulations. The BMA method is an effective tool to integrate multi-model ensemble output. The RMSE between the BMA-simulated GPP and EC-measured GPP was the smallest. The use of the BMA method allows for the combination of multiple LUE models to yield a high-accuracy GPP.  
**Author Contributions:** Conceptualization, [PERSON] and J.Z.; formal analysis, [PERSON] and J.Z.; writing--original draft preparation, X.W.; writing--review and editing, X.W., J.Z. and J.R.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant No. 41771466), and the Special Fund for the Key Program of Science and Technology of Qinghai Province (Grant no. 2017-SF-A6).  
**Data Availability Statement:** All data and codes used in this study are available by requesting the corresponding author ([EMAIL_ADDRESS]).  
**Acknowledgments:** This work was supported by the Special Fund for Key Program of Science and Technology of Qinghai Province (Grant no. 2017-SF-A6). We would like to thank the National Tibetan Plateau Data Center ([[http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)]([http://data.tpdc.ac.cn](http://data.tpdc.ac.cn)) (accessed on 8 October 2018)) for providing the carbon flux and meteorological data. We would also like to thank the Land Processes Distributed Active Center (LP DAAC) for providing MODIS reflectance product MOD09A1 collection. We thank LetPub (www.letpub.com (accessed on 8 March 2021)) for its linguistic assistance during the preparation of this manuscript. We thank the three anonymous reviewers for their constructive suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methodology
### Integrated Methodology for the Elaboration of Geospatial Exposure Databases
#### 2.3.5 The Position of the Building Within the Urban Block and Urban Block Compactness  
The position of a building within an (aggregate) urban block is an important variable in the structural behavior of buildings subjected to an earthquake [65]. The proposed methodology involves three phases: (a) the delimitation of urban blocks, (b) the characterization of the urban blocks, and (c) the classification of buildings by their relative position in the block.  
The delimitation of the urban blocks can be performed in GIS either by manual digitization or GIS analysis. In the case study of San Jose, the urban blocks were defined in QGIS 3.16.16 by subtracting the urban area, the area of the streets, roads, and train lines, and river feature layers downloaded from OpenStreetMap through OverPass API [61]. In the second step, unbuilt and vegetated areas were identified by NDVI analysis of Sentinel-2 imagery and subtracted from previous results. Once delimited and topologically corrected, the urban blocks were characterized by calculating the degree of compactness, as the ratio between the built area and the total area.  
Finally, the buildings were classified as isolated and non-isolated. Isolated buildings were identified by a QGIS model involving the buffering of the buildings. To consider a building as separate from the one next to it, the minimum separation was set at 1 meter. This distance was determined based on the precision with which the footprints were obtained, since inaccuracies in the outline of the footprint may occur with distances of less than 1 m, which in turn could lead to errors in the results.
## 6 AOG Downtime Reduction and Implementation Results
### Seasonal Trends of AOG Incidents
This analysis highlights ARIMA's strengths in predicting general trends during stable periods but also its limitations in accounting for sudden, extreme changes in operational conditions. The analysis of AOG incidents begins with daily incident data accumulation. The compulsory tools for finding the ARIMA model's parameters include the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), as illustrated in Figure 19.  
The ACF plot illustrates the relationship between AOG incidents and their first differences, helping to identify trends or seasonality in the data. The PACF plot, which removes intermediate lag effects, highlights the most relevant autoregressive terms. The only significant spike in both plots appears at lag zero, while all other lags fall within the confidence intervals. This indicates that apart from its immediate correlation, there is no strong or statistically significant dependency on the data. Essentially, this pattern suggests that the series (or its residuals) behaves like random noise (white noise), meaning the model has successfully captured any systematic patterns present in the data.