esa-sceva/satcom-chunk-collection huggingface dataset:
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 5 Pointing Correction Verification
### Solar Hit Reanalysis  
The simplest method for verifying the correction is to consider its impact on the solar hits themselves. Re-analysis of the solar hits using the 2D Gaussian fit model indicates an  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & \(\mathbf{x_{0}}\) & \(\mathbf{y_{0}}\) & **I** & **D** \\ \hline \(1\) & \(-0.13^{\circ}\) & \(-0.14^{\circ}\) & \(0.72^{\circ}\) & \(119^{\circ}\) \\ \(2\) & \(-0.04^{\circ}\) & \(0.16^{\circ}\) & \(0.00^{\circ}\) & \(0^{\circ}\) \\ \(3\) & \(-0.03^{\circ}\) & \(0.16^{\circ}\) & \(0.00^{\circ}\) & \(0^{\circ}\) \\ \(4\) & \(-0.03^{\circ}\) & \(0.20^{\circ}\) & \(0.41^{\circ}\) & \(181^{\circ}\) \\ \(5\) & \(-0.10^{\circ}\) & \(0.24^{\circ}\) & \(0.00^{\circ}\) & \(0^{\circ}\) \\ \(6\) & \(-0.17^{\circ}\) & \(0.52^{\circ}\) & \(0.23^{\circ}\) & \(186^{\circ}\) \\ \(7\) & \(-0.01^{\circ}\) & \(0.49^{\circ}\) & \(0.14^{\circ}\) & \(292^{\circ}\) \\ \(8\)a & \(-0.08^{\circ}\) & \(0.42^{\circ}\) & \(0.18^{\circ}\) & \(238^{\circ}\) \\ \(8\)b & \(0.26^{\circ}\) & \(0.58^{\circ}\) & \(0.48^{\circ}\) & \(261^{\circ}\) \\ \(9\) & \(-0.12^{\circ}\) & \(0.19^{\circ}\) & \(0.27^{\circ}\) & \(307^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The final 4-parameter corrections applied to each of the time periods from Table 2. These are the fixed azimuth offset, \(x_{0}\); the fixed elevation offset, \(y_{0}\); the inclination of tilt, I; and the declination of tilt, D. Period 8a is from 5 November 2019 to 15 June 2020, and Period 8b is from 15 June 2020 to 14 October 2020. Note that Period 2 and Period 3 have the same correction applied.  
increase in consistency across the field campaign (even though the power differential has not been addressed). Figure 5 shows these fits for the entire campaign both before and after correction. In addition to the derived offsets now being near zero, there is an improvement to both the first and second fit RMSEs along with the second fit now retaining all but seven of the data points. While these results are a strong indication that the correction has improved the pointing accuracy of the data, they do not provide independent verification; this is considered in the following section on radar QPE.
## 3 Systematic Odometry Error Evaluation and Correction
### Discussion of the Results Obtained with the Odometry Calibration Strategies
The application of the calibrated kinematic parameters also showed a reduction in the maximum absolute differences between the odometry and ground truth trajectories, which have been reduced from 76 mm to 23 mm and from 5.86\({}^{\circ}\) to 1.77\({}^{\circ}\). Similar improvements have been obtained in the error in the determination of the final location and angular orientation of the mobile robot that has been reduced from 74 mm to 16 mm and from and from 5.32\({}^{\circ}\) to 0.73\({}^{\circ}\). Figure 10 shows the application of the calibrated odometry to the trajectories shown previously in Figure 7, which correspond to five repetitions of the R1 calibration trajectory. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**RMS Error**} & \multicolumn{2}{c}{**Max Error**} & \multicolumn{2}{c}{**Final position error**} & \multirow{2}{*}{_CF\({}_{\text{Validation}}\)} \\  & **Distance** & **Abs(angle)** & & **Distance** & **Abs(angle)** & & **Distance** & **Abs(angle)** \\  & **(m)** & **(\({}^{\circ}\))** & **(m)** & **(\({}^{\circ}\))** & **(m)** & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** \\ \hline Uncalibrated & 0.040 & 3.00 & 0.076 & 5.86 & 0.074 & 5.32 & 0.1242 \\ Calibrated (STE-GA5) & 0.013 & 0.86 & 0.023 & 1.77 & 0.016 & 0.73 & 0.0222 \\ \hline Improvement & 67.00\% & 71.22\% & 68.99\% & 69.82\% & 78.38\% & 86.24\% & 82.14\% \\ \hline \end{tabular}
\end{table} 
Table 6: Summary of the average trajectory errors obtained with the uncalibrated and calibrated kinematic parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \(R_{a}\) **(m)** & \(R_{b}\) **(m)** & \(R_{c}\) **(m)** & \(r_{a}\) **(m)** & \(r_{b}\) **(m)** & \(r_{c}\) **(m)** & \(\delta_{a}\) **(\({}^{\circ}\))** & \(\delta_{b}\) **(\({}^{\circ}\))** & \(\delta_{c}\) **(\({}^{\circ}\))** \\ \hline Nominal & 0.195 & 0.195 & 0.195 & 0.148 & 0.148 & 0.148 & 60.0 & 180.0 & 300.0 \\ Calibrated & 0.218643 & 0.215735 & 0.216287 & 0.155068 & 0.151534 & 0.152728 & 59.7397 & 179.6820 & 301.5735 \\ Difference & 12.13\% & 10.63\% & 10.92\% & 4.78\% & 2.39\% & 3.19\% & \(-0.43\%\) & \(-0.18\%\) & 0.52\% \\ \hline \end{tabular}
\end{table} 
Table 5: Nominal and calibrated kinematic parameters (strategy STE-GA5) of the mobile robot.
## Results and Discussion
### Effect of the Addition of Freeze-Dried GPP on Pasta Gluten Network
#### 3.2.2 Assessment of Polymeric Protein Distribution by SE-HPLC  
Polymeric protein distribution of SDS-extractable proteins in semolina flour, freeze-dried grape pomace powder, and raw and cooked pasta samples are shown in Figure 2. Semolina flour (Figure 2A) showed a profile described by [PERSON] et al. [31; 34], with a polymeric protein peak of glutenin with a high molecular weight (large polymeric proteins, LPP > 100.000 Da) after 7 min of elution, followed by a small peak of glutenins at a lower molecular weight (small polymeric proteins, included between 100.000 and 80.000 Da) between 8 and 10.5 min of elution, a large peak of monomeric gliadins (large monomeric proteins, LMP < 80.000 Da) (after 12 min of elution) and finally a small peak, including albumins and globulins, known as small monomeric proteins (SMP), comprised in the main peak of LMP.  
Figure 2: SE-HPLC profiles of SDS-extractable proteins of samples. (**A**): semolina flour; (**B**): freeze-dried GPP; (**C**): raw fresh pasta samples; (**D**): cooked fresh pasta samples.  
Freeze-dried GPP (Figure 2B) did not show SDS-extractable polymeric proteins in the considered range of molecular weights, because the elution of peaks after 15 min is attributable to the extraction solvent, according to [PERSON] et al. [35].  
The elution profile of raw pasta made with 100% semolina (Figure 2C), as well as those of raw GP-fresh pasta showed a profile very similar to semolina. On the contrary, significant differences in the number of peaks and peaks area were detectable in all the cooked pasta profiles (Figure 2D) (100% semolina, 5% and 10% GP-fresh pasta samples) with respect to the raw profiles.  
The SDS-extractable protein peak areas are shown in Table 3. As it can be inferred from the table, a significant decrease in all SDS-extractable proteins peaks area of cooked samples was detected, probably due to both the protein loss phenomenon, as shown in Table 2, and the polymerization reaction between different classes of gluten proteins during the cooking process, which led to the formation of SDS-unextractable polymeric proteins [30; 31]. Among all the cooked pasta samples, the 10% GP one showed the highest total peaks area for SDS-extractable proteins, including all classes of gluten and non-gluten proteins, in line with Table 2, and thus confirming a different protein arrangement of this sample along the cooking process.
## 4 Combining Ascending and Descending Sentinel-1 Images to Analyze
### Projection to a Common Reference System
However, when \(\sigma\) approaches 90\({}^{\circ}\) the cos\(\sigma\) approaches 0 and it is evident that LOS projection can produce extreme values that must be considered artifacts (Figure 7c). In general, this configuration is unlikely among velocity obtained from DInSAR processing since unfavorable topographies tend to produce geometric distortions, and therefore low coherence pixels that are normally removed during processing. However, in special locations with a peculiar topography, extreme Vslope values can be obtained, and the results must be interpreted accordingly. That is why it is important to use Digital Elevation Models (DEM) with a similar resolution to the radar image to reduce noise in the post-processing calculations. In our processing, we used a multi-looking parametrization of 3 \(\times\) 15 getting a radar-range resolution of 60 \(\times\) 60 m that can be translated to a flat Earth resolution of 40 \(\times\) 40 m (using \(\theta\) = 42\({}^{\circ}\)). Hence, we resample the DEM using a cubic convolution algorithm to decrease the resolution to 60 \(\times\) 60 m, filtering in this way the high topographic wavelengths that can produce that kind of artifact. Finally, we projected the Vlos values, ascending and descending, to a common reference system: the local slope when this is greater than 13\({}^{\circ}\), and the vertical if it is smaller (stable points are not projected). However, in the displacement affecting Zone 4 we had additional information from the local GNSS network that provided the motion direction that we used as projection (more detail in Section 5).
## 2 Materials and Methodology
### Methodology
#### 2.2.3 Random Forest Method  
Random Forest is a bagging model that trains multiple decision trees in parallel. The final output is determined by aggregating the predictions of individual trees, typically using majority voting [37]. In contrast, the XGBoost algorithm creates an ensemble of decision trees sequentially. Each subsequent tree is trained to correct the errors made by the previous ones, all of which work to improve each other and determine the final output [38]. The following is a further introduction to them.  
Random forest (RF) was developed by [PERSON] and [PERSON] in 2001 [37]. RF has been actively applied in many areas due to its excellent performance [39]. \"Random\" means the randomness of sampling from the training dataset and the randomness of selecting features for the basic regression tree. RF model training can be highly parallelized, which is advantageous for the speed of large-sample training in the area of big data. The main process in the RF model is as follows:  
Figure 3: The flow chart of the experiment.  
For the input dataset \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{m},y_{m})\}\), the RF model uses the Bagging sampling method to obtain \(n\) subsample sets, which each have \(m\) samples. With the subsample set \(D_{i}\), \((i=1,2,\ldots,n)\), \(k\) features were selected randomly from all features for training the basic learner. Finally, average the results of each basic learner as the prediction result \(\hat{Y}\).  
Three key parameters affect RF model performance: the number of maximum features selected \(k\), the number of regression trees, \(n\) and the maximum tree depth of each regression tree \(d\). The larger \(k\) is, the better the performance of base learners is, but the independence between base learners will be reduced. Generally, increasing the number of regression trees \(n\) can improve the accuracy of the model, but at the cost of increased computational complexity and training time. The depth of each tree \(d\) in a random forest affects its ability to generalize and avoid overfitting. The probability of overfitting increases with larger values of \(d\). These parameters will be optimized in this paper by the grid search method [40].
# Common Data Model and Database System Development for the Korea Biobank Network
## 4 Proposed System  
### KBN-CDM Development  
The specifications of the proposed CDM are presented as a unified modeling language in Figure 1. The data model was designed to include all 27 disease types and all other diseases that do not belong to one of the 27 types that were included in the information from the 17 biochanks, which was collected in 2019. The model consists of a total of 18 tables and 177 variables. The patient information of each hospital is loaded into the BASICINFO table, and because one patient can be admitted multiple times, the BASICINFO table and the REGISTRATION table form a 1:N relationship. The REGISTRATION table and the remaining 15 tables containing clinical information were also designed to form a 1:N relationship. The codes for the data are managed in the VOCA table. Among these codes, clinical information such as diagnosis, drinking habits, and  
Figure 1: KBN clinical CDM unified modeling language. smoking habits are mapped to international standards such as Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) or Logical Observation Identifiers Names and Codes (LOINC). The details of each KBN clinical CDM table are given in Table 1. The BASICINFO table consists of basic patient information such as patient number, sex, date of birth, occupation, and registered biobank. Occupation is coded into 13 types and mapped using the SNOMED-CT language. The REGISTRATION table contains records about patient registration with a certain institution. The information included in this table consists of registration date, age (at the time of registration), and diagnosed disease (at the time of registration). As mentioned, diseases are classified into 27 types. The SPECIMEN table contains the collected specimen information. All data were obtained by linking them to the HuBIS_Sam system used in KBN. The information included in this table consists of the specimen collection date, specimen type, and number of specimens. The information included in the STAGE table consists of the American Joint Committee on Cancer (AJCC) pre-op and post-op results. The BDMEASURE table contains information related to body measurement data. These data include height, weight, body mass index (BMI), systolic blood pressure, diastolic blood pressure, pregnancy status, loss of weight (yes or no), amount of weight lost, European Cooperative Oncology Group (ECOG) waist circumference, pulse rate, temperature, respiration rate, head circumference, and umbilical cord blood count. The FAMHISTORY table includes information on the patient's prior disease status before being registered on the system, that is, information such as any other disease history before registration, similar family disease history, and any family history of allergies. The CLINEXAM table includes laboratory test results at the time of biobank registration. The NOTE table includes pathology and surgery records. The EXAM table includes functional tests such
# MAC-Based Compression Ratio Improvement for CAN Security
###### Abstract  
Information security in a controller area network (CAN) is becoming more important as the connections between a vehicle's internal and external networks increase. Encryption and authentication techniques can be applied to CAN data frames to enhance security. To authenticate a data frame, a message authentication code (MAC) needs to be transmitted with the CAN data frame. Therefore, space for transmitting the MAC is required within the CAN frame. Recently, the Triple ID algorithm has been proposed to create additional space in the data field of the CAN frame. The Triple ID algorithm ensures every CAN frame is authenticated by at least 4 bytes of MAC without changing the original CAN protocol. However, since the Triple ID algorithm uses six header bits, there is a problem associated with low data compression efficiency. In this paper, we propose an algorithm that can remove up to 15 bits from frames compressed with the Triple ID algorithm. Through simulation using CAN signals of a Kia Sorento vehicle and an LS Mtron tractor, we show that the generation of frames containing compressed messages of 4 bytes or more is reduced by up to 99.57% compared to the Triple ID method.  
CAN; security; data compression; authentication; header bits; Triple ID; MAC
# Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation
## 2 Methodology of the Random-Speckle Optical Detection and Processing Algorithm
### Initial Edge Point  
First, viewpoint-independent 3D object segmentation was performed to separate point clouds into individual objects and obtain the initial edges of each object [19]. The inspection point was defined as the origin of a Cartesian coordinate system with two vectors of unit length pointing along the x and y axis to its eight neighboring pixels, as shown in Figure 8.  
The vector comprises the standard basis vectors \(\mathrm{e}_{1}\) and \(\mathrm{e}_{2}\), as shown in Equation (2).  
\[\mathrm{v}_{\mathrm{n}}=a_{\mathrm{1n}}\mathrm{e}_{1}+a_{\mathrm{2n}}\mathrm{ e}_{2} \tag{2}\]  
Where \(a_{\mathrm{1n}}\) and \(a_{\mathrm{2n}}\) are vector components of \(\mathrm{v}_{\mathrm{n}}\) on the basis vectors.  
Then, the composite vector of an inspection point is defined by Equation (3).  
\[\mathrm{Composite\ vector}\ =\ \sum_{n=1}^{8}a_{\mathrm{1n}}\mathrm{e}_{1}+\sum_{n =1}^{8}a_{\mathrm{2n}}\mathrm{e}_{2} \tag{3}\]  
There exists a vector from the inspection point to each of its neighboring points, which are marked as red spots in Figure 9. An inspection point in this step was defined as an initial edge point if its composite vector is not a zero vector (shown as Figure 9a). In contrast, the composite vector of a non-edge point is a zero vector (shown as Figure 9b). The initial edge link was generated by enclosing all the neighboring initial edge points.  
Figure 8: The Cartesian coordinate system of inspection points.
## 3 Results and Discussion
### Phylogenetic Analysis  
The length of the determined sequence was 1398 bp. A BLAST ([[https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/)]([https://blast.ncbi.nlm.nih.gov/](https://blast.ncbi.nlm.nih.gov/)) Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome, accessed on 18 January 2025) search of the 165 rRNA gene sequence against the GenBank database showed that WLY-B-L8T was related most closely to _Bacillus rhizoplane_ JJ-63 DSM 12442T (98.71%), _Bacillus pseudomycooides_ DSM 12442T (98.21%), and _Bacillus cytotoxicus_ NVH 391-98T (98.14%). Phylogenetic analysis based on the 165 rRNA gene showed that strain WLY-B-L8T formed a distinct branch with _Bacillus cytotoxicus_ NVH 391-98T in the neighbor-joining tree (Figure 2).  
Figure 1: The cell morphology of WLY-B-L8T observed by scanning electron microscopy (SEM) after incubation on PCA.  
Figure 2: Phylogenetic tree based on 165 rRNA gene sequences, showing the relationships between strain WLY-B-L8T and members of the genus _Bacillus_. The tree was reconstructed using the neighborjoining method. Bootstrap values (5-50%) based on 1000 replications are shown at branch nodes. The tree was rooted by _Penaibacillus kribbens_ AM49T (AF391123) as the outgroup. Bar, 0.01 substitutions per nucleotide position.
## 1 Multi-Class Transfer Learning and Domain Selection for Cross-Subject EEG Classification  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1\({}^{\copyright}\) and [PERSON] 2,***  
* **Correspondence: [EMAIL_ADDRESS] (C.T.); [EMAIL_ADDRESS] (S.D.); Tel: +27-012-382-9943 (C.T.)**  
**Abstract:** Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and cross-subject variability with more accurate intention prediction. In this case, TL utilizes knowledge (signal features) in the source domain(s) to improve the classification in the target domain. However, current existing transfer learning approaches on EEG-based BCI are mostly limited to two-class cross-subject classification problems, while multi-class problems are only implemented with a focus on within-subject classification due to the complexity of multi-class cross-subject classification problems. In this paper, we first extended the transfer learning approaches to a multi-class cross-subject scenario, then investigated the reason for transfer learning performance being poor in multi-class cross-subject classification. Secondly, we address the challenge of significant sessional and subject-to-subject variations originating from both known and unknown factors. It is discovered that such variations have a massive influence on the classification because of the negative transfer (NT) across domains. Based on this discovery, we propose a multi-class transfer learning approach based on multi-source manifold feature transfer learning (MMFT) framework and an enhanced version to minimize the effects of NT. The proposed multi-class transfer learning approach extends the existing MMFT to multi-class cases. Then enhanced multi-class MMFT firstly searches for domains with high transferability and selects only the best combination among source domains (SD), then utilize the best-selected combination of domains for transfer learning. Experimental results illustrate that the proposed multi-class MMFT can be employed in the cross-subject classification of both three-class and four-class problems. Experimental results also demonstrated that the enhanced multi-class MMFT could effectively minimize the effect of negative transfer and significantly increase the prediction rates across individual target domains (TD). The highest classification accuracy (CA) of 98% is obtained by the enhanced multi-class MMFT.  
**Keywords:** brain-computer interface (BCI); electroencephalogram (EEG); transfer learning (TL); multi-source manifold feature transfer learning (MMFT); multi-class transfer learning; cross-session classification; cross-subject classification
## 4 Methodological Conception and Execution
### Training Configuration  
In order to be able to reproduce experimental results and compare them to similar ones across all repetitions of the experiment, we took a common set of hyperparameter settings with which we trained our proposed HFL model and baseline comparisons. The learning rate was fixed to 0.01, and it was trained with a batch size of 64. The clients did 5 local epochs of training with each round of communication. The overall number of communication rounds worldwide was set to 100 in order to provide adequate convergence and at the same time ensure computational efficiency. To avoid overfitting, model optimization was conducted with the Adam optimizer and a weight decay coefficient of 0.0001.  
In the case of privacy-preserving experiments, the model was tested with various values of privacy budget (\(\epsilon\)), which were 1.0 and 3.0, respectively. The Gaussian noise scale (\(\sigma\)) used were 0.5 and 1.0, and the clipping norm was 1.0 to restrict the effect of any one update. The final dense layers were also used with a dropout rate of 0.3 to promote further generalization. All experiments were carried out with the help of [insert framework, e.g., TensorFlow 2.11 or PyTorch 1.13] and performed on the system, which had an Intel Core i7 processor, 32 GB RAM, and an NVIDIA RTX 3080 GPU. The settings of the hyperparameters are summarized in Table 2.  
To reduce overfitting, especially considering the non-IID and possibly unbalanced characteristics of healthcare datasets, several preventive measures were applied. Early stopping was used with a patience of 10 communication rounds, where the training was stopped when no reduction in validation loss was seen. On local training, order-dependent learning was avoided by randomizing the data during each epoch. Regularization was also achieved through the work of Differential Privacy itself, because the addition of calibrated noise to the gradient updates decreased the ability of the model to memorize individual client data. These coupled methods improved the stability and the overall generalization capacity of the offered HFL framework on the various data distributions and client states.