esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Third-Party Perspective
#### 3.4.2. Spatial Coordinate Alignment Algorithm Based on ARCore Continuous Space Localization  
Both HoloLens and ARCore-enabled devices have the function of continuous spatial localization, which can model real space and display fixed virtual content within it. Due to the difference in devices, the coordinate systems of the two will be different when recognizing the same real space. The problem needing to be solved by the spatial coordinate alignment algorithm that is based on ARCore's continuous spatial localization is how to align the spatial coordinates of the two devices so that they can accurately synchronize their respective operations, and finally complete the third-party perspective display function. This algorithm uses HoloLens to identify the ARCore-enabled devices and obtain the six-dimensional coordinate vectors \(r\) (relative spatial position (\(x\), \(y\), \(z\)) and relative rotation angle  
Figure 6: Third-Party Perspective Process.  
(\(\alpha\), \(\beta\), \(\gamma\))) of the ARCore-enabled devices relative to the virtual content in the HoloLens coordinate system. The virtual content is transformed from under the HoloLens coordinate system to under the ARCore device coordinate system through the spatial mapping rule \(T\). Finally, the spatial coordinate system's alignment is completed, as in Equation (5). Through this algorithm, the virtual content in the third-party view device can be fixed in the real world without the problem of jittering with the movement of the camera.  
\[Coordinate_{ARcore}=T(r), \tag{5}\]  
There are two problems in spatial mapping: the first is that HoloLens and ARCore use different coordinate system axis orientations, so it is necessary to use rotation transformation to solve the axis orientation problem; the second is that when considering the portability problem, the distance between the camera and the center of the device is different for different ARCore devices, so it is necessary to transform the spatial position of the virtual content. In summary, the mapping rule \(T\) can be divided into two parts: rotation angle mapping \(T_{R}\) and spatial position mapping \(T_{S}\), as in Equation (6).  
\[T(r)=T_{R}(\alpha,\beta,\gamma)+T_{S}(x,y,z), \tag{6}\]  
Considering the portability issue, the distance between the camera and the center of the device is different for different ARCore devices. The distance between the camera and the center of the device can be expressed as a 3-dimensional vector \(C_{offset}\) (spatial offset \((x,y,z)\)), while the spatial location mapping \(T_{S}\) can be expressed as Equation (7).  
\[T_{S}(x,y,z)=(x,y,z)-C_{offset}, \tag{7}\]
## 3 Performance Analysis
### Channel Fading
#### 3.4.2 Nakagami-m Fading Channel  
The Nakagami-m fading model has a larger spread than the Rician and Rayleigh fading systems and is a channel model that depicts rapid fading over vast distances. The PDF of the SNR \(\gamma\) may be obtained using the \(\gamma\) Nakagami-m distribution as [41]  
\[f(\gamma)=\frac{1}{\Gamma(m)\gamma}\bigg{(}\frac{m\gamma}{\overline{\gamma}} \bigg{)}^{m}e^{(-\frac{m\gamma}{\gamma})}. \tag{16}\]  
where \(\Gamma(.)\) denotes the gamma function, and \(m\) represents the parameter determining the distribution's shape. When the Nakagami-m fading parameter is defined as an extremely high value, \(\left(m\rightarrow\infty\right),\) it converges with the AWGN channel. Rayleigh distribution is produced when \(m\) is one.  
The average SNR of the received signal is provided by  
\[\overline{\gamma}=\frac{\sigma_{T}G_{R}G_{T}\lambda^{2}}{N_{0}\left(4\pi \right)^{2}d^{\alpha}}. \tag{17}\]  
\(G_{R}\) and \(G_{T}\) represent the receiving and transmitting antenna gains, respectively.  
Outage probability can be written as  
\[\begin{array}{ll}P_{out}&=P(\gamma<\gamma_{th})=\int_{0}^{\gamma_{th}}f_{ \gamma}(\gamma)d\gamma=\int_{0}^{\gamma_{th}}\frac{1}{\Gamma(m)\gamma}\left( \frac{m\gamma}{\overline{\gamma}}\right)^{m}e^{(-\frac{m\gamma}{\overline{ \gamma}})}d\gamma\\ &\cong 1-e^{(-\frac{m\gamma_{th}}{\gamma})}\sum\limits_{s=0}^{m-1}\frac{ \left(\frac{m\gamma_{th}}{\gamma}\right)^{s}}{s!}=1-e^{(-\frac{m\gamma_{th}N_ {0}(4\pi)^{2}d}{C_{R}G_{T}\lambda^{2}P_{T}})}\sum\limits_{s=0}^{m-1}\frac{ \left(\frac{m\gamma_{th}N_{0}(4\pi)^{2}d}{C_{R}G_{T}\lambda^{2}P_{T}}\right)^{ s}}{s!}.\end{array} \tag{18}\]
# Suppression of Harmonic Current in Magnetic Bearing-Rotor System with Redundant Structure
## 3 Characteristic and Control Strategy of Magnetic Bearings with Redundant Structures
### Control Strategy and Analysis of Magnetic Bearings with Redundant Structures
Therefore, the equivalent control schematic diagram in the \(x\)-direction can be described as shown in Figure 6.  
According to Figure 6, the logic current \(i_{x}\)(s) influenced by the unbalance force and sensor runout can be described as  
\[ \begin{split} i_{x}(\mathrm{s})=\frac{G_{cx}(\mathrm{s})G_{w}( \mathrm{s})}{1+K_{xx}(\mathrm{s})G_{cx}(\mathrm{s})G_{cx}(\mathrm{s})}x+\frac{- K_{xx}(\mathrm{s})G_{cx}(\mathrm{s})G_{cx}(\mathrm{s})}{1+K_{xx}(\mathrm{s})G_{cx}( \mathrm{s})G_{cx}(\mathrm{s})G_{cx}(\mathrm{s})}x_{d}(\mathrm{s})\\ +\frac{-K_{xx}(\mathrm{s})G_{cx}(\mathrm{s})G_{cx}(\mathrm{s})}{ 1+K_{xx}(\mathrm{s})G_{cx}(\mathrm{s})G_{cx}(\mathrm{s})}F_{cx}(\mathrm{s}) \end{split}  \tag{32}\]  
From Equation (32), it can be concluded that the unbalance force \(F_{cx}\) and sensor runout \(x_{d}\) will induce the logic harmonic \(i_{x}\). Logic current reflects the harmonic characteristics of current in the coils, which will cause undesirable multifrequency vibration of the rotor. If not suppressed, the vibration caused by unbalance mass and sensor runout will degrade the system performance and even lead to system instability. In a magnetic bearing-rotor system with redundant structures, to achieve the purpose of suppressing current harmonics, it is necessary to add the corresponding method in the control loop of the \(x\)- and \(y\)-directions at the same time.
## 5 Error Analysis from a User-Centric Approach
### Search Query Generation Problems  
We categorize search query generation problem into two main types: (1) the query generator fails to produce a search query, relying instead on dialogue history, even when a query is necessary to retrieve accurate information from the Internet. This can lead to inaccurate responses. (2) The model generates an incorrect response using an incorrect search query.  
Figure 4 exemplifies such problems. When asked in the first turn of Case 2-1 about which year the movie _Doctor Strange_ belongs to, the model responds with incorrect information (2019) without generating a search query. Additionally, Case 2-2 demonstrates the generation of hallucinated knowledge responses without an Internet search.  
Figure 3: An example of Case 1: Internet document retrieval problems.  
We identified the key characteristic of the error in BlenderBot 2.0's search query generation: the model seldom attempts to search the Internet on the first turn, despite the apparent need. We attribute this behavior to the MSC and WizInt datasets, which primarily consist of plain conversations that do not require retrieval.  
In the case of LLMs, similar challenges are encountered, especially in terms of search query generation. These AI systems, skilled in processing and generating language, often struggle to translate complex or ambiguous user inputs into effective search queries. This can lead to less accurate or relevant search results, impacting the efficiency and reliability of LLMs. The issue is more pronounced in specialized or niche topics, where precise query formulation is key to retrieving relevant information. Addressing these query-generation problems is crucial for enhancing LLM performance in various applications.
# Study on the Combustion Behavior and Kinetic Characteristics of Semi-Coke from Oil Shale
## 3 Results and Discussion
### Characterization of Combustion Residues Analysis  
#### 3.3.1 SEM Characterization  
High-temperature combustion not only leads to the decomposition of organic matter but also causes the decomposition, concentration, and recrystallization of mineral components, revealing the profound impact of combustion on the solid-phase composition of oil shale.  
Figure 6 presents the SEM images of oil shale semi-coke samples before and after combustion, illustrating the morphological changes induced by the combustion process.  
As shown in Figure 6a, the surface of the sample before combustion is relatively smooth, with a few small and evenly distributed pores. The pores in the semi-coke are mainly formed by the release of volatiles during the combustion process. At this stage, the material has a high carbon content and low exposure of mineral components.  
In Figure 6b, after combustion, the surface of the residual sample exhibits numerous irregular pores with larger and uneven pore sizes. Some areas display a honeycomb-like structure. This is due to the oxidative decomposition of carbonaceous components during combustion, leading to significant material loss and the formation of a porous structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & F2-D4-F2 & & & F2-D4 \\ \hline \(E_{a}/\text{KJ}\cdot\text{mol}^{-1}\) & stage I & Stage II & Stage II & stage I & Stage II \\ \(A/\text{S}^{-1}\) & \(3.08\times 10^{3}\) & \(50.227\) & \(265.536\) & \(61.982\) & \(21.187\) \\ Contribution & \(0.431\) & \(0.157\) & \(0.443\) & \(0.542\) & \(0.410\) \\ \(E_{a,\text{age}}/\text{KJ}\cdot\text{mol}^{-1}\) & & \(136.70\) & & \(41.58\) & \\ \(R^{2}\) & & \(0.94\) & & \(0.82\) & \\ \(f(\alpha)\) & \((1-\alpha)^{2}\) & \(3/2[(1-\alpha)^{-1/3}-1]-1\) & \((1-\alpha)^{2}\) & \((1-\alpha)^{2}\) & \(3/2[(1-\alpha)^{-1/3}-1]-1\) \\ \hline \multicolumn{5}{c}{F2-R3-F2} & \multicolumn{5}{c}{A2-F2} \\ \hline  & stage I & Stage II & Stage III & stage I & Stage II \\ \(E_{a}/\text{KJ}\cdot\text{mol}^{-1}\) & \(131.344\) & \(102.546\) & \(203.089\) & \(114.196\) & \(221.454\) \\ \(A/\text{S}^{-1}\) & \(2.21\times 10^{6}\) & \(9.08\times 10^{3}\) & \(2.95\times 10^{7}\) & \(1.17\times 10^{5}\) & \(7.11\times 10^{8}\) \\ Contribution & \(0.248\) & \(0.187\) & \(0.583\) & \(0.386\) & \(0.649\) \\ \(E_{a,\text{age}}/\text{KJ}\cdot\text{mol}^{-1}\) & & \(145.66\) & & & \(167.83\) & \\ \(R^{2}\) & & \(0.97\) & & \(0.95\) & \\ \(f(\alpha)\) & \((1-\alpha)^{2}\) & \((1-\alpha)^{2/3}\) & \((1-\alpha)^{2}\) & \(1/2(1-\alpha)[-\ln(1-\alpha)]^{-1}\) & \((1-\alpha)^{2}\) \\ \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 6: SEM images of oil shale semi-coke samples before and after combustion. (**a**) Oil shale before combustion; (**b**) oil shale semi-coke after combustion.  
Additionally, the exposure and partial melting of mineral particles at high temperatures further alter the surface morphology.
# A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs
## 4 Methodology Proposal
### Stage 2: Graph Building  
The aim of this stage is to create a graph model that considers the questions formulated in the previous stage and validate that the data is sufficient to address the questions. Additionally, it is essential to implement this graph model within a suitable data storage medium.  
This stage consists of four tasks: (2.1) Exploratory Data Analysis, consisting of analyzing the available information by applying one or more of the following options: statistics, visualization, data aggregations, and identification of missing or corrupted data. This exploration and understanding of the data occur early in the process, preceding more in-depth data analysis. In the context of graph building, the objective of this activity is to identify the entities, properties, and their relationship useful to answer each question defined in the previous stage. For guidance in the exploratory data analysis (EDA), thereader can refer to [42; 43], and for a review, a comparative study of four methods employed in EDA, they can refer to [44]. (2.2) Graph Model Design defines which data will be represented as nodes, establishes the relationships between them, and defines the attributes associated with each element. It is possible to design homogeneous or heterogeneous graphs, as well as labeled or not labeled and directed or indirected graphs. For guidance in the model process, the reader can refer to [34; 45; 46]. (2.3) Data Storage Formats Selection, which involves choosing the technology to store it in graph databases, such as relational, columns, key-value, documents, graphs, or any other type of file format. To fully harness the analytical potential of graph analysis, opting for a graph database is recommended but not obligatory. The reader can refer to [47] for guidance in this selection process. Some examples are NEO4J, TigerGraph, JSON files, MongoDB, and Apache Cassandra. Finally, (2.4) Extract, Transform, and Load Data involves acquiring data from diverse sources, including databases, spreadsheets, flat files, or web services. Data are collected from various origins and brought into a staging area for subsequent processing. Throughout the transformation phase, the data undergoes cleansing, formatting, and restructuring to align with the graph's requirements. Ultimately, the refined and transformed data are loaded into the storage media. The reader can refer to [48; 49; 50] for guidance in this task. Examples of technologies that support extract, transform, and load tasks include Kettle Pentaho, AWS Glue, Knime, and Apache Spark.  
The outcome of this stage is the successful implementation of the graph in the selected data storage format.
## 4 Results and Discussion
### Data Generation Configuration
#### 4.2.3 Evaluation of the Minimal Number of Real Images  
To evaluate the impact of different subset sizes (see Figure 5) of real images on model performance, we fine-tuned a model trained on synthetic images with varying amounts of real data. These subsets were carefully generated, considering background brightness, class distribution, and colony counts, and were drawn from the train set of the AGAR dataset [4].  
When fine-tuning on just 10 real images, the model achieved a significant boost with an AP\({}^{50}\) of 77.6 and mAP of 30.5, representing improvements of \(+\)28.9 and \(+\)18.9, respectively, compared to the model trained solely on synthetic data. Fine-tuning with 25 real images further improved performance, yielding an AP\({}^{50}\) of 80.8 and mAP of 45.4, with a notable \(+\)14.9 increase in mAP compared to the 10-image model. This suggests that additional real data enhance detection accuracy, particularly at higher IoU thresholds. However, fine-tuning with 50 real images showed a slight decline in AP\({}^{50}\) (79.2), though the mAP continued to improve, indicating better generalization across IoU thresholds. Adding more images did not always lead to proportional gains. The model fine-tuned with 75 images showed no significant overall improvement, and its AP\({}^{50}\) fell behind the 25-image model. However, using 100 real images resulted in a marked improvement, reaching an AP\({}^{50}\) of 84.9 and mAP of 52.6, approaching the performance of the model trained on exclusively 1000 real images, with a small accuracy gap of only \(-\)4.1 in AP\({}^{50}\) and \(-\)3.8 in mAP. Fine-tuning with 200 images brought further gains, achieving 87.3 AP\({}^{50}\) and 54.5 mAP, closing the gap to the 1000-image model by \(-\)1.9 in both metrics. Finally, the model fine-tuned with 500 images reached an AP\({}^{50}\) of 89.9 and an mAP of 57.5, surpassing the model trained on 1000 real images, albeit by a small margin that can be attributed to variations in the dataset. These results demonstrate that 500 real images are sufficient to fine-tune a model pre-trained on synthetic data, achieving near-parity with models trained on larger real datasets.
# MSA K-BERT: A Method for Medical Text Intent Classification
###### Abstract  
Improving medical text intent classification accuracy can assist the medical field in achieving more precise diagnoses. However, existing methods suffer from problems such as low accuracy and a lack of knowledge supplementation. To address these challenges, this paper proposes MSA K-BERT, a knowledge-enhanced bidirectional encoder representation model that integrates a multi-scale attention (MSA) mechanism to enhance prediction performance while solving critical issues like heterogeneity of embedding spaces and knowledge noise. We systematically validate the reliability of this model on medical text intent classification datasets and compare it with various deep learning models. The research results indicate that MSA K-BERT makes the following key contributions: First, it introduces a knowledge-supported language representation model compatible with BERT, enhancing language representations through the refined injection of knowledge graphs. Second, it adopts a multi-scale attention mechanism to reinforce different feature layers, significantly improving the model's accuracy and interpretability. Especially in the IMCS-21 dataset, MSA K-BERT achieves precision, recall, and F\({}_{1}\) scores of 0.826, 0.794, and 0.810, respectively, all exceeding the current mainstream methods.  
medical text intent classification; natural language processing; knowledge-enhanced BERT; IMCS-21; multi-scale attention Article
## 2 Methods
### Laboratory Experiments and Mie Scattering Calculations to Evaluate LISST-VSF
#### 2.1.3 Data Processing
Because each LISST-VSF measurement consists of two linear polarization states of the incident beam, the average of these two is used as the final estimate of \(c_{p}^{LISST*}\). As a final step, the determined \(c_{p}^{SPEC}\) values were multiplied by relevant dilution factors to obtain final results representing particle concentration in LISST-VSF samples and enable direct comparisons with LISST-VSF measurements.
# A Nonlinear Optimization Design Algorithm for Nearly Linear-Phase 2D IIR Digital Filters
## 2 Formulation of the Design Problem  
Much attention has been paid to the optimal design of 2D filters with separable denominators [18, 29, 30, 33, 41, 42, 43]. The stability of such 2D filters can be easily guaranteed by ensuring the roots of both denominators are within the unit bi-disc independently. As such, the internal stability triangles for 1D second-order factors are used as stability constraints [44]. In addition, separable denominators reduce the complexity of the 2D filter and the induced optimization problem and also result in fewer coefficients [29, 45]. Although the phase of the optimized 2D IIR filter is only nearly linear, one can see that the separable denominators 2D IIR filter offers good selectivity and computation efficiency and reduced system delay without compromising the required amplitude specifications when compared with the corresponding other 2D filters [45]. As such, we will assume in this paper that the filters of interest have separable denominators. The desired 2D IIR filter can then be represented by the following transfer function [46, 47, 48]:  
\[H(a,b,z_{1},z_{2}) =\frac{N(z_{1},z_{2},b)}{D(z_{1},z_{2},a)} \tag{1}\] \[=H_{0}\frac{N(z_{1},z_{2},b)}{D_{1}(z_{1},a_{1})D_{2}(z_{2},a_{2})},\]  
where \(b\) is a vector of the form \([b_{00}\,b_{01}\ldots\,b_{N_{1}N_{2}}]\), and \(N(z_{1},z_{2},b)\) is a multivariate polynomial of the form:  
\[N(z_{1},z_{2},b)=\sum_{j=0}^{N_{2}}\sum_{i=0}^{N_{1}}b_{i\bar{j}}z_{1}^{-i}z_{2 }^{-j}, \tag{2}\]  
\(D_{1}(z_{1},a_{1})\) and \(D_{2}(z_{2},a_{2})\) are polynomials in \(z_{1}\) and \(z_{2}\), respectively, of the form  
\[D_{1}(z_{1},a_{1}) =\prod_{n=1}^{J_{1}}(1+a_{1n,1}z_{1}^{-1}+a_{1n,2}z_{1}^{-2}), \tag{3}\] \[D_{2}(z_{2},a_{2}) =\prod_{n=1}^{J_{2}}(1+a_{2n,1}z_{2}^{-1}+a_{2n,2}z_{2}^{-2}), \tag{4}\]  
where \(J_{1}=N_{1}/2\) and \(J_{2}=N_{2}/2\) and \(N_{1}\) and \(N_{2}\) are even.  
The vectors \(a_{1}\) and \(a_{2}\) are given by:  
\[a_{1} =[a_{11,1}\,a_{11,2}\,a_{12,1}\,a_{12,2}\,\ldots\,a_{1I_{1},1}\,a_ {1I_{1},2}],\text{ and } \tag{5}\] \[a_{2} =[a_{21,1}\,a_{21,2}\,a_{22,1}\,a_{22,2}\,\ldots\,a_{2I_{2},1}\,a_ {2I_{2},2}]. \tag{6}\]  
We then construct a vector \(c\) by concatenating the vectors \(b\), \(a_{1}\), and \(a_{2}\) as follows [44]:  
\[c=[b\,a_{1}\,a_{2}\,H_{0}]^{T}.\]  
Using the same notation symbol, the transfer function can be written as follows:  
\[H(c,z_{1},z_{2})=\frac{N(z_{1},z_{2},b)}{D(z_{1},z_{2},a)}. \tag{7}\]Now, the main design problem is to determine the coefficients in the numerator and denominator of Equation (7) in such a way that the obtained transfer function \(H(z_{1},z_{2})\) satisfies all the desired specifications as explained in the following sections.
## 2 Related Work  
There are ways to assist non-deep learning and ways to support deep learning in the area of image feature extraction, and many analysts have spent a lot of time researching these two approaches. A major drawback of the approach of training a convolutional neural network is the difficulty of extracting a large amount of visual feature data by increasing the network's structure, and a secondary drawback is the difficulty of making the network detection faster to meet time constraints. Speed and precision appear to have always been two competing concepts. We compare the current popular object detection algorithms and summarize their main ideas, as shown in Table 1.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & \begin{tabular}{c} **Yes or No** \\ **CNN-Based** \\ \end{tabular} & \begin{tabular}{c} **One or** \\ **Two Stage** \\ \end{tabular} & **Loss Description** & **Backbone** & **Main Strategy** \\ \hline SIFT & NO & - & - & - & Keeping invariance to rotation, scaling, brightness change, etc. \\ \hline HOG & NO & - & - & - & Using the distribution of light intensity or edge direction on the surface of the object to describe the whole object. \\ \hline BoVW & NO & - & - & - & Applying Bag-of-Words algorithm to image representation. \\ \hline DPM & NO & - & - & - & Adopting the improved HOG feature, SVM classifier and sliding windows detection idea. \\ \hline R-CNN & Yes & Two & - & Pre-trained AlexNet & Using the selective search algorithm [20] to improve the filtering speed of candidate boxes. \\ \hline Fast R-CNN & Yes & Two & Classification, regression & VGG-16 & Absorbing the characteristics of SPP-net [21] to improve the speed based on R-CNN. \\ \hline Mask R-CNN & Yes & Two & Classification, regression, mask & Pre-trained ResNet or FPN & Adding a branch of prediction segmentation mask based on Fast R-CNN. \\ \hline YOLOv1 & Yes & One & \
\begin{tabular}{c} Bounding box, confidence, \\ probability, etc. \\ \end{tabular} & VGG-16 & Regression of object bounding box. \\ \hline YOLOv2 & Yes & One & Same as YOLOv1 & Darknet-19 & Multiscale training, full convolution network, anchor mechanism of fast R-CNN, more training skills, etc. \\ \hline YOLOv3 & Yes & One &
\begin{tabular}{c} Binary cross entropy \\ in classification \\ \end{tabular} & Darknet-53 & Integrating FPN, using Softmax in classifier. \\ \hline YOLOv4 & Yes & One & CIOU in anchor & CSPdarkNet53 & Changes in input images, neck and head based on V3. \\ \hline YOLOv5 & Yes & One & CIOU in anchor & BottleneckCSP & Not much better than V4. \\ \hline YOLOv3-Tiny & Yes & One & Same as YOLOv3 & Simplified Darknet-53 & Removing some feature layers, retaining two independent prediction branches based on YOLOv3. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of the discussed works along their main distinctive characteristics.