esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Video mining is a hot topic that has attracted significant interests in recent years. Video mining is able to find the implicit, valuable, and understandable video patterns by analyzing visual features, time structure, event relationships, and semantic information of video data [1], which can be classified into video structure mining and video motion mining [2]. In particular, for poor structural videos such as traffic surveillance video, video motion mining can realize the applications of abnormal events detection or congestion analysis, and so on.  
With the evolution of video mining technology, there has been an increasing number of research works focused on the use of topic models for video motion mining. Although probabilistic topic models were originally studied in the field of natural language processing [3; 4], they also provide a way for discovering hidden pattern from images or document corpus. In the text mining, a topic model represents unlabeled documents as mixtures of topics where latent topics are distributions over observed words. In the video motion mining, full video is treated as document collection; a short video clip is treated as a document that divided from full video; the video features are considered as words. In this way, with the introduction of probabilities topic model in video motion analysis, variety of latent motion patterns, and latent motions correlations were discovered, which are represented by topics. Figure 1 shows the diagram of video topic modeling.  
Although several topic models have successfully applied in surveillance systems [5; 6; 7; 8], there exist several premature phenomena in the procedure of video topic modeling--such as abnormal events locating and computational performance of real-time mining. In this paper, we focus on topic modeling with region information and uses it to automatically detect abnormal events from a complex video scene in real-time.  
The rest of the paper is organized as follows. In the next section, we present a brief survey of the related works. In Section 3.1--Video Representation--the video representation is explained. In Section 3.2--Regional Topic Model and its Online Inference Algorithm, our regional topic model (RTM) and its hybrid stochastic variational Gibbs Sampling algorithm (HSVG) are presented. The datasets, evaluations and comparisons are discussed in detail, in Section 4. Our conclusions are presented in the last section.
# Application of Corrected Methods for High-Resolution XRF Core Scanning Elements in Lake Sediments
## 2 Description of the Calibration Methods
### Normalized Polynomial-Scaled (NPS) Calibration  
The NPS calibration includes the same procedures as the NMS calibration, except for the scaling and normalization process [7]. The NPS employs quadratic polynomial fits instead of median values (as in NMS) to scale the XRF-scan intensities, which eliminates the influences of temporal variations in water content (see detail in [7]). Thereafter, the polynomial-scaled value is normalized to the sum of conventional-fractions for all element components rather than being constrained to unity [7].
# Effect of Thermal via Design on Heat Dissipation of High-Lead QFN Packages Mounted on PCB
## 2 Finite Element Simulation
### Material Characterization and Meshing  
Table 2 presents an enumeration of the material performance parameters employed during the assessment of temperature distribution and thermal stress within the analytical  
\begin{table}
\begin{tabular}{c c c c} \hline
**Structure Feature** & **Conventional QFN** & **Dual-Row QFN** & **High-Lead QFN** \\ \hline Die size (mm) & 2.8 \(\times\) 2.8 & 2.8 \(\times\) 2.8 & 2.8 \(\times\) 2.8 \\ Die thickness (mm) & 0.3 & 0.3 & 0.3 \\ Package size (mm) & 8 \(\times\) 8 & 8 \(\times\) 8 & 8 \(\times\) 8 \\ Package thickness (mm) & 0.75 & 0.75 & 0.75 \\ Die attach thickness (mm) & 0.02 & 0.02 & 0.02 \\ Thermal pad size (mm) & 4.6 \(\times\) 4.6 & 4.6 \(\times\) 4.6 & 4.6 \(\times\) 4.6 \\ Thermal pad QFN thickness (mm) & 0.1 & 0.1 & 0.1 \\ Thermal pad PCB thickness (mm) & 0.035 & 0.035 & 0.035 \\ PCB thickness (mm) & 1.6 & 1.6 & 1.6 \\ Lead size (mm) & 0.4 \(\times\) 0.2 & 0.4 \(\times\) 0.2 & - \\ Within row pitch (mm) & 0.4 & 0.8 & 0.4 \\ Between row pitch (mm) & - & 0.5 & - \\ \hline \end{tabular}
\end{table}
Table 1: Main structural features of the three QFN designs.  
Figure 2: Models for (**a**) conventional QFN, (**b**) dual-row QFN, and (**c**) high-lead QFN.  
model. These materials encompass silicon, silver paste, FR-4, epoxy molding compound (EMC), SAC305 solder alloy, and copper thermal pads. The thermal conductivity of EMC and FR-4 is low compared to other materials, which is one of the main reasons for the accumulation of heat generated by the chip. In order to simplify the calculation process, we used FR-4 as the only material for the PCB. SAC305 solder has better thermal conductivity, which can improve the efficiency of heat transfer and thus reduce the influence of the solder on the experimental results in comparison with other solders.  
Mesh generation stands as a pivotal stride within the realm of finite element numerical simulation analysis, where the quality of the mesh holds direct sway over the subsequent accuracy of numerical computation outcomes. In pursuit of heightened computational precision, we have escalated the resolution of the mesh. To reduce the difficulty of the calculation, we use tetrahedral and hexahedral cells. As illustrated in Figure 3, we unveil the regular mesh structure resultant from meticulous mesh generation endeavors. At this juncture, the aggregate count of mesh elements for the high-lead QFN package model tallies 397,176, concomitant with a node ensemble numbering 82,629.
# Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance.
## 4 Doppler Frequency Shift Analysis
Furthermore, for millimeter-wave deployment (Fc = 26 GHz), a single DM-RS is enough for speeds below 30 km/h if the selected SCS is above 60 kHz. The main outcome of Doppler frequency shift analysis is that for 5G NR deployment in the 3.5 GHz band with SCS of 30 kHz, in an area where speeds above 120 km/h are not expected (urban areas, no highways or high-speed trains), reserving a single symbol for DM-RS (dmrs-AdditionalPosition = 0) is a legitimate strategy for optimizing the capacity of the system worth testing. For 5G NR deployments in the millimeter-wave band, DM-RS reduction is still possible; however, there are stringent requirements for supported mobility and SCS configuration of 60 kHz or 120 kHz.
# The Immunogenetics of Psoriasis and Implications for Drug Repositioning
## 2 The Immunogenetics of Psoriasis  
Like many immune-mediated diseases, psoriasis is associated with the major histocompatibility complex (MHC) alleles [11]. The ability of a series of immunosuppressive agents, such as cyclosporine, denileukin diftitox, and alefacept, to improve psoriasis has revealed the importance of the immune system in its pathogenesis. Psoriasis was once thought to be a disease associated with the _TNF_-mediated Th1-type immune response. Recently, the cellular and molecular contributions to the overactive immune response were further elucidated. The expression profiles of genes encoding Th1, Th2, and Th17 cytokines were extensively evaluated in differentiated skin cells of psoriatic patients. Th1 and Th22 cells were found to produce abundant psoriatic cytokines, such as _IL-17_, _IFN-7_, _TNF_, and _IL-22_, which mediated the potentiation of keratinocytes on psoriatic inflammation. Although the role of Th1 cells in psoriasis is questioned, the use of cytokine antibodies in treatments has shown that _IL-23_, _TNF_, and _IL-17_ play key roles in the pathogenesis of psoriasis [12,13].  
More than 40 regions in the human genome have been associated with psoriasis [14], such as _HLA-Cw6_, _IL12B_, _IL23R_, _ICE3A_, _ICE3D_, and _STAT3C_. The chromosomal region that is thought to encode a psoriasis gene is referred to as the psoriasis susceptibility (PSORS) locus and is currently known to encode at least 15 different PSORS loci, which are mainly linked by a chain analysis of multiple members of the psoriasis family. The first gene found to be significantly susceptible to psoriasis was HLA-Cw6, which is located at PSORS1 with the chromosome position 6p21.3. Pathological studies have shown that HLA-C plays an important role in the antigen presentation of CD8+ T cells, and the CD8+ T cells in turn play a dominant role in the development and progression of psoriatic lesions. Therefore, HLA-C may be responsible for the expression profiles of genes important to psoriasis [15]. Studies have also shown that both the _ZNF816A_ and _GJB2_ loci are significantly associated with psoriasis in the German population, while _ERAP1_ and _ZNF816A_ are associated with type 1 (early-onset) psoriasis in the Chinese Han population. The disease susceptibility between the Chinese and European populations is heterogeneous, and may vary due to geographical differences, such as environmental factors affecting the disease susceptibility loci [16]. In general, after the classification of the associated genes, different types of biological mechanisms have been implicated in contributing to the etiology of psoriasis, including innate and adaptive immunity.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Delivery Mode
#### 3.2.2 Non-Real-Time Delivery  
Contrary to real-time applications, there are several sensor applications, such as some environment monitoring systems like water and soil surveillance and habitat monitoring [50], which do not define time conditions on the data delivery and are referenced as non-real-time applications. The routing protocol task is to ensure data delivery with a minimum of energy. All routing protocols that are not RT-protocols can be classified as NRT-protocols. There are some routing protocols that can work either in RT or NRT mode, such as:  
\(\bullet\) **On-demand Multi-hop Look ahead Real-time routing protocol (OMLRP)**  
As a reliable routing protocol for WSNs, OMLRP uses a look-ahead message and a mechanism involving three phases, namely: multi-hop look-ahead trigger, look-ahead message exchange and multi-hop management. OMLRP can work either in real-time or in non-real-time mode. Simulations show that the OMLRP gives good management in terms of energy but overloads the network with control messages [51].  
Table 3 establishes a comparison between delivery mode-based routing protocols [52].
## 3 Materials and Methods
### Fault Detection and Classification
```
Training data: Time series sequence with \(\mathcal{T}\) data points, \(\mathcal{T}=\{x_{1},x_{2},x_{3},\cdots,x_{\mathcal{T}}\}\),  Encoder: \(\mathcal{E}(\cdot)\), Decoder: \(\mathcal{D}(\cdot)\), Window size: \(w\), Prediction horizon: \(h\)  Outputs: Decision on fault detection and fault type  Training Autoencoder:  Create input and output sequences \((X_{i},Y_{i})\) for\(i=1,2,\cdots,\mathcal{T}-w+1\)do \(X_{i}=\{x_{i},x_{i+1},\cdots,x_{i+w-1}\}\) \(Y_{i}=\{x_{i+w},x_{i+w+1},\cdots,x_{i+w+h-1}\}\) endfor for Each training epochs do  Encoder output: \(d_{i}=\mathcal{E}(X_{i})\)  Decoder output: \(\hat{Y}_{h}=\mathcal{D}(d_{i})\)  Calculate loss: \(\mathcal{L}\left(\hat{Y}_{i},Y_{i}\right)=\frac{1}{h}\sum_{t=1}^{h}(y_{i,t}^{ \prime}-y_{i,t})^{2}\)  Optimize the model parameters, \(\varphi\): \(\underset{\varphi}{minimize\mathcal{L}\left(\hat{Y}_{i},Y_{i}\right)}\) endfor Prediction, Fault detection and classification:  Predict the output sequence: \(\hat{Y}_{h}=\mathcal{D}(\mathcal{E}(X_{i}))\)  Compute the four selected features: \(f_{1}\left(\hat{Y}_{h}\right),f_{2}\left(\hat{Y}_{h}\right),f_{3}\left(\hat{Y }_{h}\right),f_{4}\left(\hat{Y}_{h}\right)\)  Fault detection: \(C_{1}(f_{1},f_{2},f_{3},f_{4})=fault_{detect}\) if\(fault_{detect}=True\)then  Classify the fault type: \(C_{2}(f_{1},f_{2},f_{3},f_{4})=fault_{type}\) endif Return: Fault detection decision: \(fault_{detect}\) Fault classification result: \(fault_{type}\)
```  
**Algorithm 1** Multi-step prediction-based fault detection and classification.
## 3 Results and Discussions  
Table 5 shows a summary of mechanical properties for different parameters for the AA5083/Silicon carbide and AA5083/Coal composites, respectively. From the results, the AA5083/Silicon Carbide and AA5083/Coal composites generally exhibit comparable mechanical properties to the base AA5083 material in terms of ultimate tensile strength and hardness. In terms of the UTS, while slightly lower than the base material, both composites maintain respectable UTS values. In terms of ductility, reinforcement particles hinder plastic deformation, leading to a significant decrease in ductility compared to the base material. And finally, for the hardness the addition of reinforcements increases the hardness of both composite types.  
The highest hardness of both composite types is generally higher than the base material. This is due to the presence of hard reinforcement particles, which strengthen the matrix and increase its resistance to indentation. It is important to note that specific mechanical properties will depend on various factors, such as the volume fraction of reinforcements, the size and distribution of reinforcement particles, and the processing parameters used.  
It should be noted that, since two different reinforcements (silicon carbide and coal) were used to optimize the AMMC fabrication parameters, the data analysis for the fabricated AA5083/Silicon carbide composite will be provided first, and then the analysis for the created AA5083/Coal composite will be shown in the next section.  
Figure 4: Hardness specimen.  
Figure 3: Tensile specimen.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **TS** \\ **(mm/min)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **RS** \\ **(rpm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **TA** \\ **(\({}^{\circ}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **AA5083/Silicon Carbide Composite** \\ **(MH)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **PES** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **MH** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **UTS** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **PE** \\ **(\%)** \\ \end{tabular} } \\ \hline
30 & 600 & 1 & 94.8 & 71.5 & 9.88 & 93.6 & 167 & 11.9 \\
45 & 600 & 1.75 & 89.6 & 114 & 11.8 & 88.3 & 109 & 7.00 \\
60 & 600 & 2 & 89.6 & 88 & 11.2 & 92.7 & 224 & 22.4 \\
30 & 900 & 1.75 & 89.4 & 210 & 29.0 & 96.3 & 134 & 12.2 \\
45 & 900 & 2 & 94.8 & 145 & 18.5 & 90.7 & 242 & 20.7 \\
60 & 900 & 1 & 92.9 & 141 & 19.9 & 92.4 & 161 & 21.2 \\
30 & 1200 & 2 & 90.6 & 243 & 29.5 & 95.3 & 101 & 7.60 \\
45 & 1200 & 1 & 93.7 & 132 & 18.7 & 93.1 & 181 & 12.8 \\
60 & 1200 & 1.75 & 89.6 & 121 & 12.1 & 95.9 & 141 & 9.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of mechanical properties for different composites and parameters.
## 1 Introduction
pointed out the problems of heterogeneous structure, massive documents, semantic gaps, and lack of domain knowledge regarding disasters, and proposed a method of extracting and integrating information from semi-structured texts related to disasters by using natural language processing [17]. Most of the research on natural hazards knowledge graphs is either disaster problem-oriented [18; 19; 20; 21] or user-oriented [22; 23; 24; 25; 26]. Moreover, the main data used in these studies are disaster scene data, basic geographic data, or disaster news information. Few studies have examined semantic knowledge graphs of natural hazards based on massive literature data. Through missing these important resources, a clear gap is seen regarding how to use text mining to retrieve specific disaster knowledge and obtain more understanding about the relationship between disaster research and physical natural hazard events. The aim of this paper is to analyze flood disaster research trends and hotspots and identify their quantitative and spatial distribution features supported by literature mining. This study retrieved the hidden knowledge about floods theme classification and subcategory, hotspot research area, research institutions, and their spatial distribution features using abstracts of the massive flood disaster-related literature, and consequently, a semantic knowledge graph about floods was constructed.
# Removal of Crotamiton from Reverse Osmosis Concentrate by a TiO\({}_{2}\)/Zeolite Composite Sheet
## Materials and Methods  
### Materials  
HSZ-385 (surface area 600 m\({}^{2}\)/g, mean particle size 4 \(\upmu\)m, SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratio 100:1) was purchased from Tosoh Ltd. (Tokyo, Japan). TiO\({}_{2}\) powder (P-25, 50 m\({}^{2}\)/g, anatase) was purchased from Degussa (Dusseldorf, Germany) and F-type zeolite powder (F9, SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) ratio 2.1:1) was purchased from Wako Pure Chemical Industries, Ltd. (Tokyo, Japan). Crotamiton (purity \(>\) 97%) and isotope-labelled surrogate crotamiton-d7 (purity 94.5%) were purchased from Sigma-Aldrich (St Louis, MO, USA) and Hayashi Pure Chemical (Osaka, Japan), respectively. Crotamiton-d7 was dissolved in methanol(purity \(>\) 99.8%; Kanto Chemical Co., Inc., Tokyo, Japan) to prepare an internal standard solution, which was stored at \(-\)20 \({}^{\circ}\)C. All other chemicals used were of reagent grade.  
Composite sheets consisting of TiO\({}_{2}\) and zeolite (HSZ-385 or F-9) were prepared using a papermaking technique. TiO\({}_{2}\), zeolite (3.125 g each), and polyethylene terephthalate fiber (6.25 g) were suspended in water (1 L); a cationic flocculant [poly-(amideamine) epichlorohydrin, 0.05% of total solid] and an anionic flocculant (anionic polyacrylamide, 0.084% of total solid) were sequentially added and the final suspension was stirred. Hand sheets with a grammage of 200 g/m\({}^{2}\) were prepared according to JIS P8222 [30]. The sheets were dried at 120 \({}^{\circ}\)C. The mass ratio of TiO\({}_{2}\) to zeolite in the composite sheet was 1:1. The TiO\({}_{2}\)/zeolite composite sheet used in this study contained 4 mg/cm\({}^{2}\) of TiO\({}_{2}\) and zeolite. Characterization of the TiO\({}_{2}\)/zeolite composite sheet was performed by scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS: ProX; Phenom World) as shown in Figure 1. The SEM and EDS images revealed the uniform distribution of TiO\({}_{2}\) and zeolite powder in the composite sheet.  
Figure 1: Scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS) mapping images of TiO\({}_{2}\)/zeolite composite sheet: SEM images (**a**); EDS mapping of Si (**b**) and Ti (**c**).  
RO concentrate was collected from a nanofiltration/RO pilot-scale plant for municipal secondary effluent reclamation on 7 March 2017 in Japan, and stored at 4 \({}^{\circ}\)C. The RO concentrate was analyzed and the results are as shown in Table 1. Details of the quantitative analyses are given in the Supplementary materials. To clarify the mechanism for removing of croatoint with the composite sheet, croatoint solutions were prepared using either RO concentrate or ultrapure water (Millipore, Tokyo, Japan).
## 3 Research Methodology
### Evaluation Metrics for Monolingual and Bilingual Speech Recognition
#### 3.1.2 Mixed Error Rate  
Given the dual-language nature of the SEB-ASR system handling both Mandarin and English, relying solely on WER or CER for performance evaluation is inadequate. Therefore, we use the concept of MER [54] to assess the performance of the SEB-ASR system. MER has become a common evaluation metric in numerous studies focusing on mixed-language ASR. It has been widely adopted to assess system performance in scenarios involving multiple languages or language switching, providing a more tailored measure of accuracy compared to traditional metrics like WER or CER. This metric serves as a standardized measure to gauge the accuracy and efficacy of ASR systems in managing bilingual linguistic inputs. In calculating MER, the identification outcomes of the system are denoted as the hypothetical sequence, while the accurate results used for validation are referred to as the reference sequence. Utilizing the LD algorithm, as in Equation (2), we calculate the substitutions, deletions, and insertions for English words and Mandarin characters within the hypothetical sequence, corresponding to their counterparts in the reference sequence. These outcomes are then consolidated individually to derive the cumulative counts of substitutions, deletions, and insertions, which are necessary to compute the MER as follows:  
\[MER=\frac{(S_{W}+S_{C})+(D_{W}+D_{C})+(I_{W}+I_{C})}{N_{W}+N_{C}}=\frac{S_{M}+ D_{M}+I_{M}}{N_{M}}, \tag{3}\]  
where \(S_{W}\), \(D_{W}\), and \(I_{W}\) are the numbers of English word substitutions, deletions, and insertions, and \(S_{C}\), \(D_{C}\), and \(I_{C}\) are the numbers of Mandarin character substitutions, deletions, and insertions. The \(N_{W}\) and \(N_{C}\) are the total number of English words and Mandarin characters in the reference sequence, respectively. Here, \(S_{M}\) represents the total number of replacements, \(D_{M}\) signifies the total number of deletions, \(I_{M}\) denotes the total number of insertions, and \(N_{M}\) equals the sum of the number of English words and the number of Mandarin characters in the reference.
## 2 Methodology
### Relation-Based Attention-Guided Graph Neural Networks Layer
#### 2.4.2 Adjacency Matrix Updating Mechanism in GNNs  
In the GNNs, the adjacency matrix can be updated by different types of node update mechanisms. Then the adjacency matrix \(A\) of the \(l\)-th layer is updated as follows.  
\[A_{ij}^{l}=f\left(v_{i}^{l}W^{l},v_{j}^{l}W^{l}\right) \tag{1}\]  
where \(f\left(\cdot\right)\) denotes an activation function (in fact, it is the ReLU activation function), \(W^{l}\) is the weight vector of the \(l\)-th layer, and \(v_{i}\) represents the relational embedding of the \(i\)-th predefined relation.  
Each node in the graph structure represents a predefined relation. The GNNs combine all relation features with the same weights, and then the result is passed through one activation function to produce the updated node feature output. However, the influence of neighbors can vary greatly, and the attention mechanism can obtain relation importance in the correlation graph by considering the importance of neighbor relations.  
When using GNNs in the model, we use multi-head attention mechanism, which uses \(K\) different heads to describe the information between relations. The operation of this layer is independently copied \(K\) times (each copy is completed with different parameters), and finally the average value of \(K\) attention is taken. Among them, the initial embedding of the node (that is, the representation of the 0 th layer) is the vector that encodes the feature of the node.  
\[v_{i}^{l+1}=\sigma\Bigg{(}\frac{1}{k}\sum\limits_{k=1}^{K}\sum\limits_{j=1}^{m}A _{ij,k}^{l}v_{i}^{l}w^{l}\Bigg{)} \tag{2}\]  
The output from the last layer is the attended relation features \(V_{gat}=R^{m^{\prime}\times d_{k}}\), where \(m^{\prime}\) is the predicted number of potential relations for each sentence. The sentence embedding encoded by the transformer passes through the normalization layer and is denoted as \(F\). Then, we multiply the sentence feature embedding with the relation feature to obtain the final prediction score, and perform the relation classification as follows:  
\[y^{\prime}=F\odot V_{gat} \tag{3}\]  
After extracting the relations, we denote entities relations existing in each sentence as \(R^{\prime}=\{r_{1},r_{2},\cdots,r_{m^{\prime}}\}\), where \(r_{t}\in R^{d_{r}}\) is the trainable embedding of the \(t\)-th relation.