esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Ortho Vertical Dataset Processing for Best Fitting Axial-Symmetric Model
\begin{table}
\begin{tabular}{c c} \hline
**Polynomial Degree** & **Polynomial Equation** \\ \hline
6 & 596.3 – 9.748e-02x – 5.892e-05x2 + 2.244e-08x3 – 3.099e-12x4 + 1.951e-16x5 – 4.691e-21x6 \\
5 & 672.0 – 2.305e-01x + 1.887e-05x2 + 1.642e-09x3 – 3.054e-13x4 + 1.156e-17x5 \\
4 & 715.4 – 2.892e-01x + 4.391e-05x2 + 2.963e-09x3 + 7.463e-14x4 \\
3 & 650.6 – 2.264e-01x + 2.588e-05x2 – 9.708e-10x3 \\
2 & 461.7 – 1.096e-01x + 6.178e-06x2 \\ \hline \end{tabular}
\end{table} 
Table 3: Coefficients for polynomial curves of the 2 nd to 6 th degree. Figure 6: Polynomial curves of increasing degree (from two to six) fitting the obtained set of data describing the “distance to the center/cumulative uplift”. Upon increasing the degree of the polynomial regression curve, the RMSE values became lower and the adjusted R\({}^{2}\) stabilized (Table 2). Equations are shown in Table 3. Explanation of polynomial curves: (**a**) 2 nd degree; (**b**) 3 rd degree; (**c**) 4 th degree; (**d**) 5 th degree; (**e**) 6 th degree. On the basis of the above-described considerations, the predicted uplift values for each point were calculated based on the best fitting fourth-degree, as shown in Equation (3):  
\[\text{y}=7.463\text{E-}14\text{x}^{4}-2.963\text{E-}09\text{x}^{3}+4.391\text{E-} 05\text{x}^{2}-2.892\text{x}+715.4 \tag{3}\]  
where  
\[\text{x}=\text{r}\] (distance to the deformation center);  
\[\text{y}=\text{z}\] (vertical uplift value in the EGMS ortho-vertical dataset).
# How _Pseudomonas nitroreducens_ Passivates Cadmium to Inhib Plant Uptake
###### Abstract  
Cadmium (Cd) has been widely used in industry applications, leading to water and soil contamination. This study investigated the potential ability of _Pseudomonas nitroreducens_ (11830) to perform the biosorption of cadmium from aqueous solution and soil. The biosorption characteristics were described using equilibrium isotherm and kinetic studies. The Langmuir adsorption isotherm indicated a better fit with the experimental data (R\({}^{2}\) = 0.980), with a maximum capacity of 160.51 mg/g at 30 \({}^{\circ}\)C in an initial aqueous solution of 300 mg/L Cd\({}^{2+}\). The experiments followed a pseudo-second-order kinetics model (R\({}^{2}\) > 0.99), especially at a low initial concentration. The biosorption mechanisms involved were determined through scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive spectroscopy (EDS) and protein analysis. The SEM and TEM figures showed that the morphology of cells changed before and after the adsorption of Cd, and the EDS confirmed that Cd was absorbed on the surface of the cell. The analysis of proteins indicated that the protein species increased after the stimulation of Cd, which further confirmed the biosorption mechanism. A pot experiment confirmed that 11830 could passivate the cadmium in soil and reduce its uptake and utilization by _Houtttymia cordata Thunb_ (_H. cordata_). This work demonstrates the potential application of microorganisms in inhibiting the accumulation of Cd in crops.  
Pseudomonas nitroreducens; heavy metal cadmium; bioaccumulation; biosorption; biosorption mechanism Article
## 3 Estimating the Rule Probabilities
### Observations and Dicussion  
We did the training and prediction using a length-dependent version of the Earley-style-parser from [16]. The results of the benchmarks are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Grammar**} & \multicolumn{2}{c}{**Mixed Set**} & \multicolumn{2}{c}{**tRNA Set**} \\  & without & with & without & with \\  & & lengths & & lengths \\ \hline G1 & 2 (2) & 2 (3) & 6 (6) & 6 (20) \\ G2 & 48 (45) & 35 (27) & 80 (80) & 96 (79) \\ G3 & 40 (48) & 40 (41) & 78 (81) & 95 (96) \\ G4 & 39 (47) & 22 (54) & 78 (83) & 84 (95) \\ \hline \end{tabular}
\end{table}
Table 1: Grammar performance, given as sensitivity % (specificity %) rounded to full percent.  
Looking at these results it is immediately apparent that the predictions from G1 are too bad to be of any use either without or with lengths. Additionally the higher complexity of G4 compared to G2 and G3 did not lead to an improvement in prediction quality.  
Concerning the other grammars we note that adding length-dependency significantly improved the results on tRNA while they became worse on the mixed set.  
A possible explanation for these results could be that the correct parameters for the folding are different for different types of RNA. In order to test this hypothesis we took the three types of RNA in the mixed benchmark set, splitting the molecules of each type into a training set containing 2/3 of them and a benchmark set containing the remaining molecules. On these three sets we again did the training and prediction for the grammars G2 and G3, them being the most likely candidates for future use. The results are listed in Table 2.  
For each of the sets G3 with lengths performed best, backing our assumption. Additionally while the non-length-dependent versions of the grammar performed almost equal, the length-dependent version of G2 fell behind G3 significantly, indicating that length-dependent prediction is more sensitive to choice of grammar.
## 2 Related Work
### Design of Embedded Vision Systems  
Image stitching, a key technology in the construction of panoramic vision systems, aims to seamlessly fuse multiple images captured from different viewpoints into a geometrically consistent and visually coherent panorama. The typical pipeline consists of three primary modules: image registration, seam-line optimization, and blending of overlapping regions. Traditional approaches primarily rely on feature-based matching algorithms (e.g., SIFT and SURF) combined with RANSAC for geometric alignment [37; 38], followed by blending strategies such as weighted averaging or Poisson image editing to reduce visual discontinuities in overlapping regions [5; 39].  
However, when dealing with wide-angle images that exhibit strong lens distortion and large parallax, traditional methods often suffer from feature mismatches and seam-line distortions, resulting in geometric discontinuities and ghosting artifacts [5]. Moreover, these approaches typically depend on offline processing, making them unsuitable for real-time applications on edge devices.  
To address these limitations, recent studies have introduced deep learning-based solutions to replace conventional feature extraction and blending modules. For example, MOWA leverages attention mechanisms to enhance stitching consistency [40], while RecRecNet employs a recursive reconstruction module to achieve high-quality stitching even in the absence of precise correspondence points [41]. Although such methods perform well on public datasets, they often involve deep network architectures with high inference latency and deployment costs, limiting their practical utility in real-world engineering contexts.  
With the growing demand for edge computing, platforms such as Jetson TX2 and Orin NX impose stricter requirements on algorithmic real-time performance, lightweight design, and resource efficiency [42]. However, most existing work focuses on optimizing individual components in isolation and lacks a coordinated design between distortion correction and stitching modules, leaving a gap in fully integrated, deployable engineering pipelines.  
In response, this paper proposes an efficient image correction network that integrates Transformer-based architectures and optical flow-guided mechanisms and further develops an embedded panoramic stitching system based on the Jetson TX2 NX platform. The system realizes a complete engineering workflow encompassing image acquisition, distortion correction, and seamless stitching, thereby facilitating the transition of wide-angle vision processing technologies from theoretical research to practical product deployment.
# An Examination of Thermal Features' Relevance in the Task of Battery-Fault Detection
## 3 Adjustments, Testing and Results  
The research of feature selection aims to estimate the dependency among various kinds of variables to select the most relevant subset from the entire dataset for the given classification or regression task [27; 28]. The estimation of relevancy can vary with respect to the kind of variables on both sides, inputs or targets. In our case, the target variable is represented by the fault indicating annotation that varies between two states: 0-1 to distinguish fault and failure free battery sets, and 1-6 to distinguish between all examined battery sets (Table 1). The voltage and temperature vectors are applied as input variables and through all the executed tests, we are comparing whether the thermal features taken from the camera possess a relevancy comparable to the voltage data obtained directly from (dis)charged batteries. The voltage vectors are of length 3 or 5 which depends on the observed battery set. Similarly, temperature vectors lengths also vary among 96 or 160 (32 samples per battery). The ideal dataset, in case of classification, will possess a maximum correlation of all input variables towards the target one and minimal correlation among them (to avoid redundancies).
## 2 System Establishment and Modeling
### Modeling
#### 2.2.6 System Design Point Parameter Selection  
Table 1 shows the variable parameter selection to be used in the system simulation. These parameters are carefully selected to model each of the main components used to build the system. However, under different operational conditions, these parameters are subject to change. The design point conditions are (altitude: 11 km, Mach: 0.8, standard day). The parameters for variable performance are altered relative to the design point parameters.
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 1 Introduction
### Existing Protoring Solutions  
The authors of [22] conducted an extensive review of 29 premium, commercial exam proctoring solutions, evaluating the features and platforms of each solution. These solutions often utilize multi-modal systems, which may involve multiple cameras or specialized hardware, and are usually centralized web platforms. Most of these solutions offer advanced performance and are not open source. They typically take control of the examinee's computer system while executing analytics. However, these solutions may raise concerns about privacy and ethics due to their complexity and lack of transparency [20]. A systematic review on similar software is provided in [6; 23].  
Likewise, many transparent solutions (with research published) utilize a cloud-based multi-modal approach, incorporating data from multiple sources. One example of such a system [24] uses a wearable camera and microphone, as well as a webcam, to evaluate various factors such as gaze, speech, text, and phone screen presence. Other research [25] has focused on analyzing behavior such as facial expressions, eye and mouth movements to detect cheating. In conclusion, proctoring automation requires a variety of factors and imposes certain constraints on examinees in order to operate accurately.  
Additionally, the authors researched open-source tools for facial analytics in proctoring. The open-source projects, in general, can be categorized into general and proctoring-specific. General tools such as OpenCV and Tensorflow can be used as a foundation to build proctoring solutions but may lack specificity, requiring extra effort for proctoring purposes. Proctoring-specific tools, on the other hand, come as a complete package or solution with dashboard, etc. They usually are highly specialized and do not offer much customization. They may not be portable as they are implemented in Python [26], and for web-based deployment [27; 28]. Some proctoring features normally require dedicated hardware such as Graphical Processing Unit (GPU) to perform optimally in real-time, adding further constraints on scalability and portability. Being open-source project, they may not provide all of the features and infrastructure that premium proctoring services offer.
## 1 Introduction
In this way, SNOM can be used to acquire super resolution optical images by scanning a probe tip over a surface in two dimensions, or mapping the entire near-field of a surface by scanning in three dimensions. Although SNOM is a nanoscale and contactless technique that does not require any fluorophore labeling and is not subject to the issue of photo bleaching, it possesses other considerable challenges. Probing the near-field of a surface makes it necessary to bring a nanoscale object within the near-field of the surface. This requires finely machined tips that are most often coupled to an atomic force microscope (AFM) system that allows three dimensional movement of the tip with nanometer resolution. This requirement also makes SNOM primarily a surface technique compared to the depth profiling possible with fluorescence labeling. The near-field light emitted from a sample is only capable of propagating a short distance away from the emitting source, which minimizes the depth profiling ability of SNOM. This review article provides an overview of recent studies using the two different modes of SNOM, aperture and apertureless, that have been applied for the characterization of thin films, nano particles, and devices. The basic theory of SNOM, as well as the differences between the two basic SNOM modes, apertureless and aperture-type, are presented in Section 2. Advances in aperture-type SNOM, defined as mapping of electric and magnetic fields associated to scattering of electromagnetic radiation in the near-field by a particle in the proximity of a nano-aperture are reviewed in Section 3. Imaging using apertureless SNOM, defined as detection of electromagnetic radiation scattered by a particle by using another particle (typically: a scanning probe tip) is presented in Section 4, along with how SNOM has enabled mapping of otherwise inaccessible electromagnetic near-field properties. The final part of this article, Section 5, reviews the use of SNOM in a three-dimensional scanning arrangement. Typically, SNOM measurements are performed by raster scanning the SNOM tip in a two dimensional (\(x\), \(y\)) scan while carefully controlling the height of the tip above the surface. As an extension of this type of scanning, 3D SNOM includes additional (\(x\), \(y\)) scans moving the probe away from the surface in the \(z\)-direction, and has been used to study light scattering near surfaces that is otherwise inaccessible by other techniques.
## 3 Survey of Techniques for Updating
### Traffic Reduction
#### 3.2.6 R-Code  
A Minimum Spanning Tree is used to disseminate the update across the network, using network coding and overhearing to reduce network traffic. The link weights are based on the number of expected packets. The key idea in R-Code [48] is that each node selects a _guardian_ parent node in the MST, which is responsible for ensuring its children receive the full update. Building the MST is not part of the protocol, but is done during periodic _initialization_ stages. The data to be disseminated is broken into generations, each of which contains multiple pages. In the _broadcast_ stage, nodes broadcast the earliest unfinished generation to their children. After a short, random delay, by the children ACKs (at the message, rather than the packet level) are used to ensure reliability. Guardians move to the next generation once all children have ACKed the current one. Periodic _beacons_ are used to determine link quality, and maintain connectivity. Guardians can be switched dynamically, depending on which potential parent receives the full update from overhearing first. Random linear coding and Gaussian elimination are used, as in AdapCode. Their results show a reduction in traffic of 15% and in latency of 50% when compared to AdapCode.  
As for AdapCode, apart from the general advantages of traffic reduction, it is an open question whether autonomous updates can benefit from the use of network coding to disseminate the update.
## 6 Conclusions  
This study proposes a predictive method for the productivity ratio of perforated wells based on three-dimensional finite element numerical simulation and machine learning technology. By utilizing the numerical simulation results as the sample data for machine learning, a predictive model for the productivity ratio of perforated well is established based on the LSSVM. The optimal parameters of the LSSVM predictive model are obtained using PSO. Compared to the traditional grid search algorithm, the PSO algorithm significantly improves the modeling efficiency, reducing the time required by more than half. Furthermore, compared to the PSO-ANN and PSO-RF models based on ANN and RF, the predictive accuracy and generalization performance of the productivity ratio model established based on PSO-LSSVM are higher, meeting the requirements of engineering practice. Therefore, the combination of PSO and LSSVM for predicting the productivity ratio of perforated well is proven to be effective and feasible. This research holds important reference value and guiding significance for perforation optimization design and also provides new insights for predicting the productivity of complex completions and geothermal wells.  
Conceptualization, H.W.; methodology, B.Z. and S.X.; software, H.W.; validation, H.W., C.Z. and F.W.; writing--original draft preparation, H.W.; writing--review and editing, C.Z.; visualization, H.W. and C.Z.; supervision, B.Z. and S.X.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the support from the National Natural Science Foundation of China, grant number 51804323.  
Not applicable.  
The data that support the findings of this study are available from the corresponding authors, [PERSON] and [PERSON], upon reasonable request. The data are not publicly available due to privacy.  
Figure 11: Comparison of the predicted values and actual values based on PSO-LSSVM.
## 4 Results
### Data Preprocessing
#### 4.2.2 Feature Engineering  
In the original data, the values of the feature of discrete data may have no meaning to each other, but they have serial properties when represented. For example, the display of chest pain types in the data set is shown in the left half of Figure 1. Therefore, in the learning process, distance-related algorithms will be affected and lead to erroneous learning. Thus, through one-hot encoding, the original discrete features are expanded into mutually independent and exclusive qualities, as shown in the right half of Figure 1. This will make the features have the same effects on the algorithm. This study uses one-hot encoding to expand the discrete features (such as Cp) that do not contain sequence properties in the original features into four independent features (Cp1, Cp2, Cp3, and Cp4).  
Moreover, the information that the machine learning model can learn is increased using derivative features. There are many ways to derive features [33]. This research regards the results of the \"unsupervised learning\" \(K\)-means algorithm as new features and incorporates them into the original features for machine learning models to learn. Among them, we used the \"Euclidean distance\" as the calculation method of data grouping and selected the results when the number of groups \(K=2\), \(K=3\), and \(K=4\). Furthermore, the analysis of the principal components of projecting multidimensional features to a lower-dimensional feature coordinate system produces new features orthogonal to each other (which implies that the features are irrelevant). Thus, it is possible to represent the original data with fewer features but still retain the most important information. This study regards the projection of the original data to the new 2D coordinates as a derivative feature for the model to learn.
# An Improved Predicted Model for BDS Ultra-Rapid Satellite Clock Offsets
## 3 Basic Predicted Model
### Time Adaptive Weight Function and Initial Deviation Correction  
When the satellite clock model has been identified, we can use the sequential adjustment least square method to fit the clock model parameter and use the sliding window method to estimate the fitting coefficient. Usually the observation weight matrix (P) of the clock model is assumed to be the identity matrix, although the observations near to the start of the prediction should have higher weights. For this reason, a method of equivalent weight is used, which is constructed by a function of time adaptive factors. The performance of the BDS satellite clock is almost equal to that of the GPS Block IIR clock (refer to GPS Block IIR clock results [14]). The formula of the constructed adaptive function was determined to be as follows:  
\[\overline{\mathrm{P}}\;=\;\mathrm{diag}(p_{i}),\quad p_{i}=i\Delta t\qquad \mathrm{Rb}, \tag{3}\]  
\[\overline{\mathrm{P}}\;=\;\left[\begin{array}{cccc}\Delta t&0&0&0\\ 0&2\Delta t&0&0\\ 0&0&\ddots&\vdots\\ 0&0&\ldots&i\Delta t\end{array}\right],\quad i=(1,2,\ldots,n), \tag{4}\]  
Figure 3: The quadratic polynomial fitting residuals as shown at **(a)** GEO satellite using QP model; **(b)** IGSO satellite using QP model; and **(c)** MEO satellite using QP model.  
where \(i\) is the number of the observations and \(n\) is the total number of clock offset data. Rb is the type of the rubidium atomic clock, \(\Delta t\) indicates the sample interval, \(\overline{\text{P}}\) is the observed equivalent weight, and \(p_{i}\) is the diagonal of the weight matrix as shown in Equation (4). The clock offset which is nearly the start of the prediction point have higher weights.  
However, comparing with actual clock observations, the prediction results of some satellites still have large deviations, which usually occur at the beginning. These deviations are called \"initial deviations\" [14], which also exist in the prediction results all the time. In order to keep the continuity between the observed clocks and the predictions, initial deviations, which belong to systemic errors, should be corrected in the real-time clock prediction. In order to reduce the initial deviations, a modified method, which is similar as GPS clock prediction in [14], for the constant term \(a_{0}\) of the clock model is used. The main steps can be referred to in [14]. In case of irregular blunders and non-linear noise in the BDS clock offset series, the numerical experiments show that the adoptive weight and initial deviation correction do not enhance the accuracies evident in the real BDS clock offset prediction. Therefore, they are just introduced as an optional algorithm which has not been compared with the other improved algorithms in the experiments of Section 4.