esa-sceva/satcom-chunk-collection huggingface dataset:
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 4 Our Proposal: Difference DV_Distance Using Correction Coefficients of Unknown Nodes
### Distance Correction  
After the final correction coefficient from unknown node \(p\) to the target anchor node \(i\) is obtained, it can be used to correct the corresponding accumulated hop distance, so as to make the corrected distance closer to the actual Euclidean distance, and then the localization accuracy can be further improved. \(Cdist_{p,i}\) which represents the corrected distance from the unknown node \(p\) to the target anchor node \(i\) can be computed as follows:  
\[Cdist_{p,i}=\frac{d_{p,i}}{1+C_{p,i}.h_{p,i}} \tag{21}\]  
where \(d_{p,i}\) and \(h_{p,i}\) are respectively the accumulated hop distance and the hop number from unknown node \(p\) to target anchor node \(i\).  
So far, after the unknown node gets correction distances from at least three anchor nodes, the position of unknown node can be computed using three-edge or multilateral distance method.
## 5 Conclusions  
This article proposed two methods that combine Halbach array and a PMA-SynRM model with magnetic focusing effect, to improve electromagnetic torque and efficiency by increasing excitation and increasing internal magnetic saturation of a rotor. A multi  
Figure 17: Demagnetization analysis of the optimized model.  
Figure 18: Demagnetization rate distribution of permanent magnets.  
objective optimization was carried out on the rotor topology structure of proposed model two. The specific conclusion is as follows.  
1. The magnetic field line distribution, air gap magnetic density and back EMF under no-load conditions were analyzed. It was proven that the magnetic focusing effect exists in the proposed model, and the excitation effect is obviously improved.
2. Under load conditions, proposed model two had a more saturated internal magnetic circuit than the basic model, with an average torque increase of 24.08% and an efficiency improvement of 1.45%.
3. After multi-objective optimization of the rotor magnetic barrier and permanent magnet size of the proposed model two, under no-load conditions, the RMS value of the air gap magnetic density increased by 41.99%, and the RMS value of back EMF increased by 43.85%. Under load conditions, the average torque was increased by 18.14%, the torque ripple was decreased by 5.22%, the total loss was decreased by 1.35%, and the efficiency was increased by 1.05% compared to the initial proposed model two.
4. After conducting an anti-demagnetization analysis on the optimized model, the simulation results show that the structure of the model has good demagnetization resistance and positive reliability.  
Conceptualization, F.X.; methodology, F.X.; software, J.Z.; validation, J.Z.; formal analysis, J.Z.; investigation, L.K.; resources, C.Q.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, L.K.; visualization, L.K.; supervision, C.Q.; project administration, F.X.; funding acquisition, C.Q. All authors have read and agreed to the published version of the manuscript.  
This research was in part supported by the Stable Funding Support for Universities in Shenzhen (GXWD20220817140906007) and the Start-up Funding for Newly Introduced Talents in Shenzhen (CA11409031).  
Data are contained within the article.  
The authors declare no conflicts of interest.
# Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects
## 3 Results and Discussion
### State-of-the-Art Classifiers for Comparison  
This subsection concisely describes the conceptual basis of five of the most important state-of-the-art classifiers implemented on the WEKA platform [47]. These are the classification algorithms against which the random forest algorithm proposed in this paper, will be compared. The proposed random forest algorithm was implemented in Python, using the scikit-learn library [48].  
Figure 3: Schematic illustration of a confusion matrix for two classes.  
Naive Bayes [8]  
The Naive Bayes classifier is a probabilistic algorithm with a superstructure based on [PERSON] theorem. The classifier assumes that the features are independent of each other, which is why it has the naive name.  
IBk (Instance-Based) [9]  
IBk (Instance-based classifiers) is a family of classifiers based on metrics, which arises as an improvement to the k-NN family of classifiers (the k nearest neighbors). The difference between these two families of classifiers is that IBk can classify patterns with mixed features and missing values, thanks to the use of the HEOM metric. (Heterogeneous Euclidean-overlap Metric) [49].  
SVM [10; 11]  
Support vector machines (SVMs) are a popular and powerful machine learning algorithm used for classification and regression analysis. SVMs aim to find the hyperplane that best separates the data points of different classes in a high-dimensional space. The hyperplane is chosen to maximize the margin, which is the distance between the hyperplane and the closest data points of each class. SVMs have shown high performance in various applications, including text classification, image classification, and bioinformatics. In this paper, a version of SVM called SMO is used, which is implemented in the WEKA platform. [47]  
MLP [12]  
The multi-layer perceptron classifier is an artificial neural network consisting of multiple layers, which allows for solving non-linear problems. Although neural networks have many advantages, they also have their limitations. If the model is trained correctly, it can give accurate results, in addition to the fact that the functions only look for local minima, which causes the training to stop even without having reached the percentage of allowed error.  
A summary of the five algorithms against which the random forest algorithm will be compared is included in Table 1.
## 4 Security Approach
### Determining the Storage Location  
Having multiple pieces of the public key distributed over several regions in the mapping system, one question remains: In which way does the client learn about the storage location of the key pieces?  
Storing the list of the regions at the responsible region imposes another risk. In case the responsible region is the attacker, it can simply manipulate this list and distribute the malicious key pieces to collaborating regions. Therefore, the client must learn the information about the storage locations in another way.  
For the following proposal, we assume that less than 256 regions exist and for the sake of simplicity we assume \(n=4\) in this example. Note that any other number up to 256 will work for \(n\). After having received the locator and key piece from the responsible region, the client hashes the identifier to an  
Figure 4: Reconstructing the shared public key.  
32 bit value. The hereby used hash function must satisfy the requirement that even small changes to the input must result in a complete different hash value. This is to ensure that the key pieces of subsequent identifiers are stored at different regions. We suggest to use a cryptographic hash function like SHA-1 for that matter.  
In a next step, the 32 bit outcome of the hash function is split into quarters (8 bit each). Each 8 bit value represents the storage location of one of the key pieces. We will call it key storage address space (KSA) from now on. Since there are viewer then 256 countries, the 8 bit address space for the regions is not completely full and a mapping directive is required. This mapping directive can be downloaded from the global authority. It is sufficient to do this very seldom, as the directive is expected to change very rarely. For each value in the KSA, the mapping directive specifies a region, where the key piece is stored. This means that a single region can be responsible for several KSA values. In that way, the load can be fairly distributed over all regions depending on their size. Figure 5 illustrates the process.  
Should the hashing and mapping to regions result in two key pieces being stored at the same region, the first piece is stored at this region and the second piece at the region with the next higher region number.
# A Distributed Approach to the Evasion Problem
## 2 Materials and Methods
### Enumerating Evasion Paths
#### 2.4.2. Forwards Propagation  
We can also track possible evasion paths in an online fashion--moving forwards in time as we track paths. Doing this, however, we are forced to represent more paths than those that may survive to the end, and need to prune the graph of possible path equivalence classes as regions transition from _True_ to _False_. Any path that leads to such a transitioning region will have to be removed from further consideration. We describe this in Algorithm 4.  
```
1:Build a set \(\{G_{i}\}_{i=1..N}\) of trees of height zero, s.t. \(i^{\text{th}}\)_True_ region is a root of \(G_{i}\). \(\triangleright\) Initialization
2:for timestep \(t\in T\)do\(\triangleright\) Iteration
3:if A region in the leaf \(v_{j}\) of \(G_{i}\) becomes _False_then
4: Iteratively prune parent nodes of \(v_{j}\) until we hit \(v_{k}\) with a least 2 children.
5:if A region in the leaf \(v_{j}\) of \(G_{i}\) splits into two regions then
6: Create two children nodes \(v_{j+1}\) and \(v_{j+2}\) each containing 1 new region.
7:if all other cases then
8: Create child nodes of each leaf node tracking the regions of the child nodes.
```  
**Algorithm 4** Evasion paths enumeration through forward propagation.
## 2 Materials and Methods
### Chemical Characteristic Analysis of SGP  
The molecular weight distribution of SGP was evaluated through high-performance gel permeation chromatography (HP-GPC) using a Flexar HPLC system equipped with a reflective index detector (PerkinElmer, Waltham, MA, USA). The PL Aquagel-OH 30 column (100-60,000 Da, 8 \(\upmu\)m, 300 \(\times\) 7.5 mm, PL1120-6830, Agilent, Santa Clara, CA, USA) and PL Aquagel-OH 40 column (10,000-200,000 Da, 8 \(\upmu\)m, 300 \(\times\) 7.5 mm, PL1149-6840, Agilent) were connected to the HP-GPC system. Each solution (1 mg/mL) of standards (Pullulan 47.3-788 kDa; [PERSON], Tokyo, Japan) and SGP were prepared by dissolving them in distilled water. The injection volume was 30 \(\upmu\)L. The eluent was 0.2 M NaCl at a flow rate of 1 mL/min.  
The total carbohydrate, uronic acid, and protein contents in SGP were assessed using a phenol-sulfuric acid method ([PERSON], 2015), an m-hydroxdiphenyl assay ([PERSON], 2015), and a Bradford assay (Quick Start(tm) Bradford Protein Assay; Bio-Rad Hercules, CA, USA), respectively. The reference standards for each assay were glucose, galacturonic acid, and bovine serum albumin, respectively.  
The monosaccharide composition of SGP was assessed using 1-phenyl-3-methyl-5-pyrazolone (PMP) derivatization and liquid chromatographic analysis following the modified method of [PERSON] et al. [27]. Briefly, 200 ug SGP was hydrolyzed with 100 \(\upmu\)L 2.5 M trifluoroacetic acid (TFA) at 120 \({}^{\circ}\)C for 90 min in an autoclave. The hydrolysate was dried under a stream of nitrogen to remove excess TFA with the addition of methanol. The dried hydrolysate was dissolved in 100 \(\upmu\)L water, followed by addition of 400 \(\upmu\)L 35% ammonia solution and 25 \(\upmu\)L 0.5 M PMP solution prepared in methanol. The mixture was incubated at 70 \({}^{\circ}\)C for 30 min and was then dried under nitrogen. The residue was dissolved in 1 mL water and was washed three times using 1 mL chloroform. The aqueous layer containing the PMP-derivatized monosaccharides was filtered through a 0.45-\(\upmu\)m nylon filter, and 1 \(\upmu\)L was injected into the UPLC system equipped with a Shim-pack GIST column (2.1 \(\times\) 100 mm, 2 \(\upmu\)m; Shimadzu, Kyoto, Japan). A mixture of eight sugar standards was derivatized in a range of 0.01 to 0.25 nmol/tube and was analyzed in the same way. Mobile phase A was a mixture (10:90, \(v/v\)) of acetonitrile and aqueous solution (0.045% KH2 PO4, \(w/v\); 0.05% triethylamine, \(v/v\); final pH 7.5 with H3 PO4). Mobile phase B was acetonitrile. Isocratic elution of mobile phases A and B (90:10) was used at a flow rate of 0.3 mL/min. Column oven temperature was maintained at 35 \({}^{\circ}\)C. PMP-labeled monosaccharides were detected at 245 nm wavelength.
## 6 Conclusions  
In this paper, we propose a novel purpose-driven model called SLRBC for the Chinese electronic medical record NER model, namely, SLRBC, which uses the fusion of SoftLexicon and RoBERTa within the representation layer. Compared to existing character-based, word-based, and hybrid methods, SLRBC fuses SoftLexicon and RoBERTa, constructing four sets with different purposes to achieve a more comprehensive purpose representation. Such a purpose-driven approach helps to incorporate the word lexicon information from electronic medical records into the character representations, thereby endowing the model with more extensive semantic embedding representations. Simultaneously, SLRBC introduces the classical BiLSTM-CRF framework to enhance the model's ability in medical entity recognition. We conducted several comparative experiments on two public NER datasets. The experimental results show that each module plays an indispensable role in our proposed SLRBC. Specifically, the F1 of SLRBC increases by 0.78% to 9.71% on CCKS2018 and 1.32% to 16.93% on CCKS2019, which validates the effectiveness of the model and lays the foundation for clinical knowledge discovery in electronic medical records.  
Although SLRBC has achieved advanced performance in experiments, there are still challenges and space for improvement. In the future, faced with increasingly complex medical knowledge, we will delve deeper into integrating more domain knowledge and merging the latest prompt learning paradigm to further elevate the model's performance and its practical value in clinical applications.  
Conceptualization, X.C. and Y.Y.; data curation, Y.Y.; formal analysis, L.Y.; funding acquisition, X.C.; investigation, Y.Y. and C.S.; methodology, Y.Y. and S.L.; project administration, Y.Y.; resources, S.L.; software, Y.Y. and X.Q.; supervision, D.L.; validation, X.C., Y.Y. and D.L.; visualization, S.L.; writing\(-\)original draft, Y.Y.; writing\(-\)review and editing, D.L., X.Q. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China (2022 YFF1302700), the Outstanding Youth Team Project of Central Universities (QNTD202308) and the Ant Group through the CCF-Ant Research Fund (CCF-AFSG 769498 RF20220214).  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: Data are contained within the article.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 3 System Overview
### Position Determination
#### 3.2.3 Coordinate Estimation  
Upon receiving a test fingerprint \(S_{0}\), the first step is to determine its corresponding region using the algorithm described in Section 3.1. Let the algorithm identify the region as \(\mathcal{T}_{j}\), which contains \(M_{j}\) RPs. Subsequently, the similarity vector \(p\) can be computed by applying either the proposed DNN or the WKNN method, as follows:  
\[p=\begin{cases}f_{L},&\text{if}\mathcal{L}<\beta\\ W,&\text{otherwise.}\end{cases} \tag{10}\]  
where \(p=\left[p_{1},p_{2},\cdots,p_{M_{j}}\right]^{\top}\), \(f_{L}\) represents the output of the DNN, and \(W\) corresponds to weights obtained from WKNN. Once the similarity vector \(p\) is obtained, the estimated location coordinates \((\ell_{o},\ell_{a})\) can be computed as follows:  
\[\ell_{o} =\ell_{o}p \tag{11}\] \[\ell_{a} =\ell_{a}p \tag{12}\]  
where \(\ell_{o}=\left[\ell_{o1},\ell_{o2},\cdots,\ell_{oM_{j}}\right]^{\top}\) and \(\ell_{a}=\left[\ell_{a1},\ell_{a2},\cdots,\ell_{aM_{j}}\right]^{\top}\) represent the longitude and latitude coordinates of training data \(\{S_{i}\}_{i\in\mathcal{T}_{j}}\), associated with region \(\mathcal{T}_{j}\).
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 3 The Proposed EPT-VMD-TPA-TCN Model and the Restructured EPT-VMD-FE-TPA-TCN Model
### Experimental Datasets  
In this paper, the datasets of Area 1 and Area 2 provided by the 9 th National Electrician Mathematical Modeling Competition in China are used as the experimental data sources. The data for each Area include load data and meteorological data between 1 January 2012 and 10 January 2015. The electrical load sampling interval was 15 min, and 96 points were taken per day in MW. The meteorological data include daily maximum temperature, minimum temperature, average temperature, rainfall, and relative humidity. The data from Area 1 are used as the main dataset for the experiment, and the dataset from Area 2 is used to verify the scalability of the prediction model proposed in this paper. In each Area, the first 80% of the data were used as the training set and the last 20% were used as the test set. The original load data series of Area 1 are shown in Figure 8. The trend of Area 1's continuous four-week load data is shown in Figure 9. The load data statistics are shown in Table 1. The meteorological data for one week in Area 1 are described in Table 2 (only 1 August 2012-7 August 2012 are shown).  
1. Dataset (Area 1): 1 January 2012-10 January 2015 (total 1106 days, 106,176 load data collection points).
2. Training set: data from 1 January 2012-2 June 2014 (884 days, 84,864 load data collection points) were used to train the model.
3. Test set: 3 June 2014-10 January 2015 (222 days, 21,312 load data sampling points) was used for the evaluation of the model.  
Throughout the training and prediction process of the model, a sliding window training method is used, i.e., load data from the past consecutive week (168 h) and weather data are always selected to predict the future day's load, and for each completed training, the data used for training are pushed back 24 h for the next model training.  
According to Figures 8 and 9, it can be seen that the power load data have obvious periodic characteristics and also show strong fluctuation. Let the original load data in the dataset be \(f(t)\) and the meteorological data be \(m(t)\). The load forecast is determined by both the historical load data \(f(t)\) and meteorological data \(m(t)\). Since the load data have strong volatility and periodicity, this paper will first process the load data, and then the processed data will be fused with the meteorological data. Finally, the fused data will be trained and predicted using the TCN-TPA network model. The processing of the load data will be described in detail below.  
Figure 8: Annual load of Area 1.
## 4 Experiments and Analysis
### AIS Data Sources and Preprocessing  
The AIS data were collected in November 2020, and the experimental area was from the Zhuankou waterway of the Wuhan section of the Yangtze River to the Baihushan crossing area. Because of the interrupted or missing data in the process of AIS signal sending, transmission, and reception and that the time series data with large deviations may appear in the AIS data, it is necessary to preprocess the data appropriately. The preprocessing process [33] of the collected AIS data in this paper is as follows:  
Firstly, removing invalid data, mainly including:  
Figure 2: Flowchart of the vessel trajectory prediction framework.  
1. MMSI is not a 9-bit data value.
2. AIS attribute information contains a large amount of data with null values.
3. In this paper, the LAT range of the track point is set to [110.00, 115.00], the LON range is set to [30.00, 32.00], the SOG range is set to [2.0-14.0], the SOG range is set to [0-360], and the distribution of research data after AIS data cleaning is shown in Figures 3 and 4.
4. Treatment of missing values.  
In the experimental data in this paper, there are some missing AIS data for individual ships. As for the problem of missing values for ship LON and LAT, the method of cubic spline interpolation [34] is used to complete the model, as shown in Figure 5 for the interpolation results of completing missing values of LON and LAT data in two AIS data values. For the missing of SOG and COG during this period, considering the relatively constant characteristics of ship SOG and COG in a short period of time, the average value is used for interpolation.  
Figure 4: SOG and COG distributions.  
Figure 3: LON and LAT distributions.  
(5) The attribute data contained in AIS information have different dimensions, so the trajectory data are normalized between 0 and 1. In this paper, the deviation method [3] is used for processing, and the normalization formula is shown in Equation (14):  
\[X^{\prime}=\frac{X-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{14}\]  
where \(X_{\text{min}}\) is the minimum value in the experimental data, \(X_{\text{max}}\) is the maximum value, \(X\) is the original data value, and \(X^{\prime}\) is the normalized data value.
## 3 Proposed Approach
### Segmentation
#### 3.1.7 Cluster Validity  
The dataset used in this research includes 588 grey-scale IVUS images and their corresponding VH-IVUS images acquired from 10 patients (age, sex and risk factors should be determined) that were used by the authors in [20]. IVUS images of 10 patients (size 400 \(\times\) 400 pixels) were acquired for VH-IVUS analysis by an electronic probe (In Vision Gold, Volcano Therapeutics Inc.) with a synthetic aperture of \(2.9\,f\) and a frequency of 20 MHz. A motorised pullback was performed along the entire vessel with a speed of 1.0 mm/s using a dedicated pullback device. A total number of 500 frames from 12 vessels--6 left anterior descending (LAD), 3 right coronary artery (RCA), and 3 left circumflex (LCX)--of 10 patients were obtained for VH-IVUS analysis and comparison using the proposed algorithm [20].  
K-means, FCM, and Self-Organising Maps (SOM) algorithms have been adapted for VH-IVUS image segmentation. The adapted algorithms are then implemented in Matlab software. A comparative study between the applied clustering methods was conducted using 86 images obtained from three patients. Figure 8 depicts the average of SW for Patient 1 (11 images), Patient 2 (42 images), and Patient 5 (33 images) by applying the FCM, K-means, and SOM algorithms. Horizontal and vertical axes indicate the frame number of the VH-IVUS image and the average of Silhouette Weight (SW), respectively.  
As shown in Figure 8, the best result was obtained by the K-means algorithm, while the worst result was obtained by applying the SOM algorithm. In addition, the results of FCM algorithm for Patients 1, 2, and 3 were more than 0.92, indicating that this algorithm can be applied for VH-IVUS segmentation. Additionally, the VH-IVUS data obtained from 10 patients were divided into training and testing datasets. Frames provided by patient 1 were considered the training set, and the rest were defined as the testing set. FCMPSO-mED, KMPSO-mED, HFCM-mED, and HFCM-kNN [45] models were implemented in MATLAB R2014b software (The Mathworks, Inc., Natick, MA, USA). Figure 9 illustrates the comparison of KMPSO-mED and HFCM-mED.  
Figure 8: The average SW obtained by FCM, K-means, and SOM algorithms for Patients 1, 2, and 5.  
Regarding Figure 9, FCMPSO-mED achieved a greater SW than HFCM-mED for Patients 3 and 4, while HFCM-mED achieved better results for Patients 2 and 6. The two methods obtained the same results for the other patients. Figure 10 illustrates the comparison of SW achieved by the FCMPSO-mED, KMPSO-mED, and HFCM-kNN models.  
Based on this figure, the FCMPSO-mED and KMPSO-mED models achieve higher SW values rather than the HFCM-kNN model. The experimental results suggest that the mED method has the potential for significant benefits in practice. In terms of memory consumption, this method requires the storage of only the colour-based feature matrix.