esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 7 Conclusions  
In order to cope with more refined target localization results, this study analyzes error transfer using the Taylor series expansion method by establishing a coordinate system combined with spatial chi-square coordinate transformation. We derive latitude and longitude information of the target point based on the UAV position, attitude information, the pod heading and pitch angles, and the pod's position. In order to reduce the positioning error, this study introduces total standard deviation, which is calculated by integrating the standard deviation of each error source. Accordingly, we synthesize and assign the error to the data and propose the CCDBO algorithm, which introduces a vertical and horizontal crossover strategy and the 3\(\sigma\) criterion to further improve accuracy. This treatment significantly reduces the localization deviation due to random errors and improves the overall accuracy of UAV target localization. Through a Monte Carlo simulation method, the positioning process of the target point is simulated, and the processed results demonstrate the superiority of this method. The flight experiments also verify the reliability of the simulation results, proving that the method proposed in this study can effectively improve the accuracy of UAV target localization in practical applications. Future research should be devoted to exploring a more precise error allocation mechanism, especially to determine the specific influencing factors of each error source through experiments or data analysis to achieve personalization and refinement. In conclusion, this method can not only further improve the accuracy of UAV target localization but also provide more solid technical support for future autonomous navigation and the mission execution of UAVs in complex environments.  
Conceptualization, Y.L. and Q.H.; methodology, Y.L.; software, Y.L. and Y.Z.; validation, S.S., Y.L. and W.Y.; resources, Q.H.; writing--original draft preparation, Y.L.; writing--review and editing, Q.H.; project administration, Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (61640308) and Natural Science Foundation of Hubei Province (2023 AFB900).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Cd Determination and Physical Parameters  
Once the data profiles 2D-ERT obtained in situ were reviewed, one trial-pit was made to collect soil samples per soil boundary to quantify Cd. The soil parameters pH, SOM, P, Ca, and Fe were assessed from the soil samples collected at the trial pits. The Cd levels were then compared with the resistivities obtained from the same spots on the soil profiles. Pseudo-total Cd content was determined using inductively coupled plasma optical emission spectrometry (ICP-OES) (Thermo Scientific ICAP 6500 duo, Waltham, MA, USA) following the previous study [1]. A method recommended by the Environmental Protection Agency (EPA 3050B) was used as the conventional pseudo-total digestion method [19]. The soil samples' pH, SOM, and the ionic content of Ca, Fe, and P were also analyzed. Soil pH was determined with a field pH meter (YSI 556 Handheld Multiparameter Instrument, Yellow Springs, OH, USA) on saturated soil paste (soil to water 1:1), measuring at each boundary of soil surrounding a cacao root in the trial pits. Phosphorous (P) concentration was determined according to the standard Olsen method [17]. The determination of the micronutrients Ca and Fe was also achieved using ICP-OES. The SOM was measured as organic carbon content, following the Walkley Black method [20]. This protocol is based on the reduction of Cr\({}_{2}\)O\({}_{7}\)-\({}^{2}\) using 100 mg of a composite soil sample, adding 10 mL of 1N K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) and gently mixed. Then, 5 mL of 95% H\({}_{2}\)SO\({}_{4}\) was added slowly over 3 h. After the exothermic reaction, 35 mL of deionized sterile water were added, and the matrix was left to stand overnight. One mL of the supernatant was used for a reading in a spectrophotometer (FastTrack(tm) UV Vis, Mettler Toledo, OH, USA), at 585 nm wavelength. The measurements were performed in triplicate and data are expressed as the average of the replicates.
# Dynamic Optimization of Buckling Problems for Panel Structures with Stiffening Characteristics
## 3 Optimization of the Panel with Ribs and Frames
### Results of Optimization  
#### 3.3.1 Improvements to MMCs  
Usually, when using the MMC method for optimization, areas with components are filled with substance, while areas without components are considered to be empty. But when optimizing the panel with ribs and frames, the ribs are connected to the panel. If only the layer where the ribs are located is optimized and the force transmission path of the panel is ignored, the result will be inaccurate. Therefore, when optimizing, the constraints of the design domain are expanded from a plane to space; that is, the two-dimensional MMC method is used to optimize the three-dimensional model. Figure 12 shows the design domain and non-design domain of the panel. From Figure 12, it can be seen that the ribs are in the design domain, while the panel belongs to the non-design domain.  
When optimization is carried out, the components describing the ribs move within the design domain, forming two different thicknesses of shell elements. The following will provide methods for applying different thicknesses of elements in MMCs.  
The elastic matrix in X-FEM analysis can be expressed in the following form:  
\[\mathbf{D^{e}}=E^{e}\mathbf{D^{f}} \tag{35}\]  
Here, \(\mathbf{D^{f}}\) is the matrix that reflects the elastic relationship of the material, which is only related to the Poisson's ratio of the material. Therefore, Equations (33) and (34) can be written as  
\[\mathbf{K}^{e}=E^{e}\!\int_{\Omega}\mathbf{B^{T}}\mathbf{D^{f}}\mathbf{B}d\Omega=E^{e}t\!\int_{ A}\mathbf{B^{T}}\mathbf{D^{f}}\mathbf{B}dA \tag{36}\]  
\[\mathbf{M^{e}}=\int_{\Omega}\rho^{e}\mathbf{N^{T}}\mathbf{N}d\Omega=\rho^{e}t\!\int_{A}\bm {N^{T}}\mathbf{N}dA \tag{37}\]  
Here, \(t\) is the thickness of the element. \(A\) is the area where the element is located. For the four nodes of the panel elements, \(H(\mathcal{K}^{s})=0\) and \(t=t^{p}\). For the four nodes of the rib elements, \(H(\mathcal{K}^{s})=1\) and \(t=t^{r}\). As for the elements located between the panel and ribs, their thickness can be calculated by the following equation:  
\[t^{m}=t^{r}\frac{\left(\sum_{i=1}^{n}\left(H\left(\mathcal{K}^{s}_{i}\right) \right)^{q}\right)}{n} \tag{38}\]  
According to Table 1, \(t^{p}=1\) mm and \(t^{r}=4\) mm. \(E^{e}\) and \(\rho^{e}\) can be calculated according to Equations (33) and (34).  
For different parts, the stiffness matrix and mass matrix of different elements in the model mesh can be obtained by using different thicknesses, [PERSON]'s moduli and densities. Then, by Equation (9), the natural frequency of the panel with ribs and frames can be calculated.
# Exposure and Health Effects of Bacteria in Healthcare Units: An Overview
## 3 Results
### Characteristics of the Selected Studies
#### 3.1.3 Prevalent Bacteria  
Several studies reported the variability of prevalent bacteria in hospital settings. For example, a gynecology theatre from a hospital in Italy [38] presented the highest bacterial load of 261.3 \(\pm\) 131.3 CFU/dm\({}^{2}\)/h, with a prevalence of _Pseudomonas_ spp. (25.8%), and the ophthalmology theatre had the highest concentration of microbial pathogens (38.9%), being 22% of _Pseudomonas_ species and 18.7% of _Bacillus_ spp. [38]. In other hospital wards and operating theatres [39], the bacterial concentration ranged from 1.27 \(\times\) 10\({}^{4}\) to 17.83 \(\times\) 10\({}^{4}\) CFU/dm\({}^{2}\)/h.  
In an Italian hospital [40], the group of workers with the highest number of infections by \(H\). _pylori_ were physicians (16 out of 47) and nurses (18 out of 45). Both groups worked in the endoscopy unit; nurses (30 out of 75) worked in direct contact with patients but did not work in the endoscopy unit.  
In terms of prevalent bacteria found in the air, the most mentioned was _Escherichia coli_, found in patients on a ward [34] in a Pakistan hospital [41] and in operating beds in Uganda [38]. Additionally, _Pseudomonas aeruginosa_ was reported in the air of a hospital ward [34] in a Tehran teaching hospital [32], and in patients from a Pakistan hospital [41], in the gynecology theatre, main operating theatre, and on the instrument trolley. _Pseudomonas_ spp. showed a higher prevalence, even though the species were not specified [38]. _S. aureus_ were another common bacteria species found in patients from a Pakistan hospital [41], on the door handles of a Ugandan operating theatre [38], and in several wards in a Tehran hospital [32]. _Staphylococcus_ spp. was detected in the air from a university hospital in Greece [35], in a Ugandan operating theatre [38] and in primary healthcare units and hospital wards in Poland [37]. _Microoccus luteus_ was found in the female surgery and male surgery wards in an Iranian hospital [39] and in a Brazilian hospital [33]. Other bacteria were mentioned, such as _H. pylori_[40], _Staphylococcus epidermidis_, _Microoccus_, _Streptococcus Flavobacterium_ spp., _Acinetobacter calcoaceticus_, _Pantoea agglomerans_, _Enterobacter_ spp., _Klebsiella oxytoca_, _Branhamella catarrhalis_ and _Neisseria fuscescens_, _Arthobacter spp., _Brebacterium_ spp [34], _Clostridium difficile_[42], Gram-negative bacilli (XDR-GNB) [43], _Haemophilus influenzae_[41], _Bacillus_ spp. [38], _Staphylococcus epidermidis_, _Streptococcus_ spp., _Diphtheroid_ spp., _Microoccus roseus_, _Bacillus subtilis_. [39], mesophilic bacteria, airborne actinomycetes, _S. saprophyticus_, _S. warneri_[37], _S. haemolyticus_ and _S. hominis_[33], _Acinetobacter luoffii_, _Salmonella typhimurium_ and _Klebsiella pneumonia_[32].
## 4 Results and Discussion
### Optimal Scale of Spatial Grid and Optimal Data Discretization Method
#### 4.2.2 Optimal Data Discretization at Optimal Grid Scale  
The geographic detector is good at processing type variables, and numerical variables need to be converted into type variables, that is, discretization. Four commonly used data discretization methods were selected: the natural break method, the equal interval method, the quantile method, and the k-means clustering method [67]. The \(q\) value of the geographical detector was used as the index to measure the classification method. The results of different discrete methods are shown in Table 5. The larger the \(q\) value, the better the discrete effect [40]. For the pick-up ridership in the morning peak hours, the drop-off ridership in the morning peak hours, the pick-up ridership in the evening peak hours, and the drop-off ridership in the evening peak hours of ride-hailing in Chengdu, among the 12 factors of the built environment, the highest proportion of the maximum \(q\) value was obtained using the quantile method, accounting for 50%, 75%, 75%, and 75%, respectively. The quantile method was regarded as the optimal data discretization method, which is different from the conclusions of other studies that the natural break method is the optimal discrete method [13].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Variables**} & \multirow{3}{*}{\begin{tabular}{c} **Discretization** \\ **Method** \\ \end{tabular} } & \multicolumn{4}{c}{**Pick-up**} & \multicolumn{1}{c}{**Drop-off**} & \multicolumn{1}{c}{**Pick-up**} & \multicolumn{1}{c}{**Drop-off**} \\  & & **Ridership in** & **Ridership in** & **Ridership in** & **Ridership in** \\  & & **the Morning** & **the Morning** & **the evening** & **the evening** & **the evening** \\  & & **Peak Hours** & **Peak Hours** & **Peak Hours** & **Peak Hours** \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Distance to the \\ nearest subway \\ station \\ \end{tabular} } & NB & 0.13 & 0.13 & 0.13 & 0.11 \\  & EQ & 0.08 & 0.04 & 0.06 & 0.07 \\  & QU & 0.13 & 0.17 & 0.15 & 0.13 \\  & K & 0.13 & 0.13 & 0.12 & 0.11 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Road network \\ density \\ \end{tabular} } & NB & 0.12 & 0.06 & 0.10 & 0.10 \\  & EQ & 0.07 & 0.03 & 0.05 & 0.06 \\  & QU & 0.13 & 0.07 & 0.10 & 0.12 \\  & K & 0.13 & 0.06 & 0.09 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results for the \(q\) values of the geographical detector.  
Figure 7: Comparison of 90% quantile of \(q\) values for explanatory variables under different grid sizes.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 2 Probability Distributions
### Generalized Extreme Value Distribution (GEV)  
The GEV distribution was introduced for the first time by [PERSON] in 1955 [11] for the analysis of extreme values. It is also known as the Fisher-Tippett distribution.  
Depending on the sign of the shape parameter, this can be transformed into the Weibull (\(\alpha<0\)), Frechet (\(\alpha>0\)) or Gumbel (\(\alpha=0\)) distributions [11; 13; 14].  
\[f(x)=\left(1-\frac{\alpha}{\beta}\cdot(x-\gamma)\right)^{\frac{1}{\alpha}-1} \cdot\frac{1}{\beta}\cdot\exp\left(-\left(1-\frac{\alpha}{\beta}\cdot(x- \gamma)\right)^{\frac{1}{\alpha}}\right) \tag{5}\]  
\[F(x)=1-\exp\left(-\left(1-\frac{\alpha}{\beta}\cdot(x-\gamma)\right)^{\frac{1 }{\alpha}}\right) \tag{6}\]  
\[x(p)=\gamma+\frac{\beta}{\alpha}\cdot(1-(-\ln(1-p))^{\alpha}) \tag{7}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale and the position parameters; \(x\) can take any values of range \(x<\left(\gamma+\frac{\beta}{\alpha}\right)\) if \(\alpha>0\), and \(x>\left(\gamma+\frac{\beta}{\alpha}\right)\) if \(\alpha<0\).
# Hyperspectral Image Classification with Imbalanced Data Based on Semi-Supervised Learning
## 5 Discussion
### Limitations  
There were a few limitations to this study. The excellent performance of NearPseudo can be achieved under certain conditions. A large amount of unlabeled data is required for efficient processing in NearPseudo. With the availability of a significant amount of unlabeled data, suitable samples can be readily selected to complement the imbalanced data. In this case, \(q\) can be set to a large value, and the performance of NearPseudo will improve accordingly. Contrarily, a lack of sufficient unlabeled samples for selection will limit the performance of our proposed method.  
Generally, in most flight campaign experiments, a large number of unlabeled remote sensing images are relatively easy to access. From the flight campaign experiment in the Xiongan New Area, the coverage area by the aircraft was 1320 km\({}^{2}\), and a large number of remote sensing images with high spectral and spatial resolution could be acquired. However, the coverage area of ground investigations accounts for a very small fraction of airborne remote sensing images. Thus, most airborne remote sensing data are not equipped with the corresponding ground-truth labels. This is because ground-true labels acquisition consumes substantial amounts of resources and effort. Specifically, in the classification of hyperspectral remote sensing images, the categories are often matched to the species level, which increases the difficulty of collecting ground-truth labels.  
In this study, the Xiongan dataset was chosen as a sample dataset to evaluate the performance of NearPseudo. This dataset was chosen because it could provide a large number of samples as unlabeled data to validate our proposed method. Compared to other publicly available benchmark datasets (e.g., Pavia data, Purdue Indian Pines, etc.), the Xiongan dataset possesses a high resolution and larger sample size. However, there are still some limitations with this dataset. For example, the same species are clustered together, and there is not much crossover between different species. Therefore, we will aim to collect more data from different areas and sources and improve the proposed method in subsequent analyses.
# A Scalable Spatial-Temporal Correlated Non-Stationary Channel Fading Generation Method
## 3 Scalable and Real-Time Hardware Generation Method
### Spatial Correlated Fading Generation Based on Efficient Matrix Operation
To tackle this issue, a time-division multiplexing (TDM)-based method for lower triangular matrix multiplication is developed, which can further reduce hardware consumption. The scheme of the proposed method is given in Figure 4, the lower triangular matrix \(\hat{L}_{8,8}\) and the vector \(\text{vec}[\hat{\mathbf{G}}_{8,8}]\) are multiplied by the time-division multiplexing architecture, and 32 second-order lower triangular matrix-vector multiplications and 496 second-order square matrix-vector multiplications can be converted into 1 second-order lower triangular matrix-vector multiplication and 31 second-order square matrix -vector multiplications, which can greatly reduce resource consumption. As shown in the figure, \(\text{vec}[\hat{\mathbf{G}}_{8,8}]\) as the input matrix, it is multiplied and accumulated by the lower triangular matrix \(\hat{L}_{8,8}\) in a time-division-multiplexed architecture, and the result \(\text{vec}[\hat{H}_{8,8}(t)]\) is obtained as the output matrix. For complex multiplication operations, the consumption of multiplier resources can be further reduced by optimizing the hardware structure, as follows  
\[ \begin{split}&\left(a+bj\right)\cdot\left(c+dj\right)=\left( ac-bd\right)+\left(bc+ad\right)j\\ &=\left[c(a+b)-b(d+c)\right]+\left[c(a+b)+a(d-c)\right]j\end{split}  \tag{34}\]  
As shown in Figure 5, we implement complex multiplication based on the hardware architecture of the data selectors. From Equation (34), it can be known that the traditional complex multiplication architecture requires four multipliers and two adders or subtractors, and the other method requires three multipliers and five adders or subtractors, both of which consume more real multiplier logic resources. In Figure 5, the improved method optimizes the complex multiplication structure, improves the utilization efficiency, reduces the occupied area, and further reduces the hardware circuit cost. Figure 4: Lower triangular matrix operation based on time-division multiplexing. It can be seen from the figure that _sel_ is used as a selection signal to control MUX_1 to output \(a+b\), \(b\), and \(a\); control MUX_2 to output \(c\), \(d+c\), and \(d\)-\(c\); and control MUX_3 to output \(b\times(d+c)\) and \(a\times(d-c)\). The improved complex multiplier structure uses only one multiplier unit, making it work in every clock cycle, which can greatly reduce the multiplier resources, save the occupied area, and reduce the hardware cost.
## 3 DePaaS: Architecture and Design
### Technical Feasibility  
A review of the characteristics of DePaaS architecture indicates that the architecture is comprehensive, modular, layered and uses APIs to interact with other modules and storage. It practices good practices of design including encapsulation, loose-coupling, and high-cohesion. The architecture is extensible as new modules can be added with relative ease. The architecture abstracts extensible concepts allowing for evolution of the platform. For example: Model Tuning not only contains details of tunable parameters for models included in DePaaS, but also enables easy inclusion of tunable parameters that could be identified in the future.  
DePaaS could be implemented as a SaaS on a cloud platform-as-a-service such as Amazon Web Services (AWS). DePaaS would implement a multi-tenant architecture, with each application having N-tier architecture within. Alternatively, each application could be deployed as a microservice and be orchestrated with a managed Kubernetes service such as Amazon EKS.  
The front-end application could be implemented using NodeJS--a language which can address large volumes of users landing on the platform. SDP models can be implemented using Python.  
Each module of DePaaS needs to communicate with other modules. For example: the _model uploader_ module needs to interact with _model validator_. Typically, such an interaction takes place by passing messages from one module to another. Considering the volume of messages passed between modules, message queues such as RabbitMQ could be used to manage communication between modules.  
DePaaS needs a persistence layer to store user profiles, SDP models, defect datasets, SDP project details, intermediate and final results of SDP runs, model performance details, defects metadata and user feedback. Persistence facility could be implemented using multi-tenant database available on a cloud platform such as Postgres on AWS.  
DePaaS needs a few services such as email, file-upload, billing, reporting, etc. Such services need not be implemented within the scope of DePaaS but could be availed from the hosting environment. Typically, providers of such services expose public, restful APIs which can be consumed to avail those services.  
The architecture of DePaaS built as a SaaS would be horizontally scalable as new modules could be added and new features could be added to existing modules. DePaaS would also be vertically scalable as it is deployed on the cloud--a scalable infrastructure.