esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
We can use the method of information extraction to extract the risk factors related to VTE for intelligent evaluation of VTE. For example, [PERSON] et al. [10] developed an AICDSS system that was connected to the hospital's electronic medical record system to obtain information from the electronic medical record system to perform VTE assessment on patients. At present, information extraction can be divided into two types: pipeline extraction and joint extraction. The pipeline-based entity relation extraction divides entity recognition and relation extraction into two independent sub-tasks. First, the identification of named entities is performed, and then the relation extraction is performed on the basis of entities. Although the structure of the pipeline method is simple, the error of entity recognition will be transmitted to the relation extraction model, resulting in poor final results. In order to overcome this problem, some scholars have proposed a model of entity-relation joint extraction. The joint extraction approach refers to using a model to identify entities and relations from text. The joint extraction model is divided into two methods based on parameter sharing and labeling strategy. The method based on parameter sharing [11; 12; 13] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Method** & **Evaluation Scale** & **Result** \\ \hline [PERSON] et al. [6] & Multilayer perceptron feedforward neural network is used to predict the VET risk level of hospitalized patients. & Caprini & Accuracy: 81\% \\ \hline [PERSON] et al. [7] & On the basis of knowledge representation and logical reasoning, case-based reasoning is used to predict the risk level of VTE. & Thromophilia predisposition risk & Accuracy: 90\% \\ \hline [PERSON] et al. [8] & An ontology-based assessment model for venous & Padua & AUC: 97.3\% \\ \hline [PERSON] et al. [9] & An algorithm for automatically calculating VTE risk score & Padua & AUC: 81\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The recent work. is to pass two tasks through the same encoding layer and let both tasks find the best parameters during training. Based on the method of parameter sharing, entity recognition and relation extraction use independent decoding methods, resulting in weak interaction between the two models. For this reason, some researchers proposed a new labeling strategy [14], and proposed using a unified way to encode and decode entities and relations to solve the problem of weak interaction, thereby improving the effect of joint extraction. The method of labeling strategy is often problematic. There is a problem in that the model parameters are too large, and the inference speed is slow. Based on the current problems in VTE intelligent assessment and the problem of joint model error transmission in information extraction, we propose a joint extraction model to extract VTE risk factors from electronic medical records, and then formulate rules in the Wells and Geneva assessment scales of pulmonary embolism, and then use rule reasoning to judge the risk level of pulmonary embolism.
## 6 The Design and Application of MESs
### Microbial Solar Cells Enhance MES Functions  
Microbial solar cells (MSCs) describe MESs that gain energy from photosynthesis by utilising plants (PMSCs), algae (AMSCs) or photosynthetic bacteria (BMSCs) in combination with electroactive bacteria (EAB) to produce electrical current or commodity chemicals [1; 16]. MSCs differ from other MECs in that they produce organics and carbon products via photosynthesis. These organics are then oxidised at the anode by the ARB before commodity chemicals are synthesised at the cathode via the reduction of oxygen or other final electron acceptors [1; 16]. One advantage of using PMFCs over agricultural bioethanol or biodiesel production is that they do not use arable land that would be better allocated for food production [127]. In fact, competition between food production and energy generation has been circumvented in some rice paddy fields where electricity is generated and harvested via EAMs within the rice paddy without interrupting food production [128]. These systems potentially represent a dual benefit, as other studies have also shown that the addition of compost to PMFCs composed of rice plants has increased electricity production [129].
## 3 Deep Feature-Based Adaptive Level Set Model (DFALS)
### CNN Model Design  
In deep learning, CNNs are the most popular networks used for image classification. In this work, CNN is utilized to obtain the confidence value of each pixel belonging to the auroral oval region. The design of the proposed CNN architecture is motivated by [33], and the structure is shown in Figure 5. This network consists of an input layer, four convolution layers, and two fully connected layers. The input layer specifies a sub-image extracted from the auroral images with the size of \(11\times 11\). Each convolutional layer has a \(3\times 3\) filter and a rectified linear unit (ReLU) [34], and zeros are padded around the border before each convolution so that the output does not lose relative pixel position information. The feature map (yellow) after the fourth convolutional layer is reduced to a one-channel feature as a \(1\times 576\) vector. The output of the network is a \(1\times 2\) vector which indicates the confidence values that the center pixel belongs to the auroral region (red) and background region (green).  
The cross-entropy loss function [35] is adopted as the cost function for the network:  
\[Loss=-\left[\hat{y}\overline{\log y}+(1-y)\log(1-\hat{y})\right] \tag{4}\]  
where \(y\) denotes the sample label. If the center pixel of the sub-image locates in the auroral oval region, the sample label is 1; otherwise, the label is 0. \(\hat{y}\) is the confidence value, which indicates the probability that the center pixel belongs to the auroral oval region. The parameters of CNN are optimized by the back-propagation algorithm [36].
## 3 Proposed Method
This algorithm has been used in different signal processing applications [41; 42; 43]. In [9], a modified version of this algorithm was used as a pre-processing stage in the analysis of heart sounds. Its implementation is described in more detail in [9]. In this work, it was decided to use as a reference the method proposed in [9] to reduce the noise of the synthetic signal. Taking into account that the frequency range of the S1 and S2 sounds is between 20-200 Hz [44], it was decided to modify the edge selection method that determines the number of components for the EWT algorithm. The signal is then broken down into two frequency bands: the first component corresponds to the frequency range between 0-200 Hz, while the second component corresponds to frequencies over 200 Hz. Therefore, in this work, the signal corresponding to the first component is used. Taking into account that the input of the GAN model is a Gaussian noise, and in turn, the output of the model during the first epochs of training is expected to be a signal mixed with Gaussian noise, it was decided to do a test using a real heart signal mixed with Gaussian noise to evaluate the performance of the proposed EWT filter on signals with the same expected characteristics of the generator's output. Figure 7A,B shows, respectively, a real heart sound and the same heart sound mixed with low-amplitude Gaussian noise, while Figure 7C,D shows their respective Fourier transforms (FFTs). In this last figure, the frequency of the components between 0 and 200 Hz can be seen in blue, and the frequency of components above 200 Hz caused by the Gaussian noise are in green. The signal shown in Figure 7B was then used as an input example to the proposed EWT algorithm to illustrate its de-noising action. Figure 7D shows the two frequency bands extracted with EWT using the FFT, while Figure 7E,F shows the two components extracted from the noisy signal in the time domain. As can be seen, the signal obtained in Figure 7E presents a lower noise level, and is comparable with the original cardiac signal shown in Figure 7A. Figure 6: Architecture of the discriminator model. Figure 7: **(A)** Real heart sound; **(B)** real heart sound with Gaussian noise; **(C)** Fourier transform (FFT) of real heart sound; **(D)** FFT of heart sound with Gaussian noise; **(E)** empirical wavelet transform (EWT) component of the noisy signal in the frequency range of 0–200 Hz; **(F)** EWT component of the noisy signal in the frequency range greater than 200 Hz.
# A Blockchain Self-Sovereign Identity for Open Banking Secured by the Customer's Banking Cards
## 4 Proposed Solution
### Requirements and Design Goals  
The SSI concept emphasizes that personal data are subject to governance and are not dependent on any external factor, as listed in the below characteristics [27]:  
* Complete control over private data;
* Ensuring the privacy and security over users' data;
* Trust in any central institution is not required;
* Data portability;
* Data integration;
* Personal data transparency.  
This concept is important as it reduces the chance of data leakage incidents since information is not stored in central locations [28]. A high level of assurance is assessed based on specifications [22] compiled from two standards that control digital identity: (1) The ISO 29115 [29] standard; this standard covers the four main phases of (I) enrolment, (II) electronic identification mean management, (III) authentication, and (IV) management and organization; and (2) NIST (National Institute of Standards and Technology (NIST), 2020) [30]. We include these standards' requirements in our work to ensure a high level of assurance by extending the banking card usage as a hardware-based second factor authenticator.
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 4 Results and Discussion
### Visualization  
In the experiment, six attention modules, namely, encoder, SCCA, CA, CBAM, SENet, and triplet, were chosen for visualizing the detection results. Figure 11 displays the detected salient objects using an RGB color map, while Figure 12 shows the detected salient objects using a black-and-white binarized image. For the bird detection results, SCCA can accurately detect the bird entity. However, encoder, CA, and triplet cannot detect the tail of the bird. Although CBAM can detect the bird entity, it is unable to detect the size of the bird. SCCA can accurately detect the woman holding the cat, eliminate noise interference, and outline the woman and the cat. All attention modules perform well in the phone booth detection, but only the predictions of SCCA and encoder which have no gap in the middle of the phone booth are close to the ground truth. When it comes to detecting small sea lions, all attention modules show similar detection effects.  
The model's detection results on the DUTS and HKU-IS datasets indicate that the SCCA attention module exhibits the best overall performance in the evaluation metrics for salient object detection. Furthermore, the two enhancement strategies proposed in this paper have contributed to the model's improved performance. Visualization results demonstrate that the detection performance of SCCA in real-life scenarios surpasses that of other comparison models. It offers rapid detection speed and minimal model parameters, making it fully suitable for deployment in real-world scenes.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**SCCA** & **C2 Loss** & **Size** & \multicolumn{3}{c}{**DUTS**} & \multicolumn{3}{c}{**HKU-IS**} \\ \hline  & & & **Times** & **MAE** & **F\({}_{\mathbf{1}}\)-Score** & **Times** & **MAE** & **F\({}_{\mathbf{1}}\)-Score** \\ \hline \(\times\) & \(\times\) & 1.29 & **0.387** & 0.071 & 0.801 & **0.020** & 0.032 & 0.932 \\ \(\surd\) & \(\times\) & **0.57** & 0.424 & 0.067 & 0.806 & 0.054 & **0.028** & 0.938 \\ \(\times\) & \(\surd\) & 1.29 & 0.402 & 0.069 & 0.804 & 0.032 & 0.030 & 0.934 \\ \(\surd\) & \(\surd\) & **0.57** & 0.424 & **0.066** & **0.809** & 0.056 & **0.028** & **0.939** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation results of U2 Net. \(\times\) indicates that the strategy is not used, \(\surd\) indicates that the strategy is used.  
Figure 10: Ablation results of U2 Net.
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Adherence to the Host
#### 2.1.3 Lhrc  
Lhrc is a multicopy small RNA in _Listeria monocytogenes_ that is activated in response to cell envelope stress and infection-relevant conditions ([PERSON] _et al._, 2015; [PERSON] _et al._, 2015). There are five variants of Lhrc(Lhrc1-5), each ranging from 111 to 114 nucleotides in length. While _lhrC1-4_ is clustered together in the genome, _lhrC5_ is located at a distant site. They are regulated by the stress-responsive two-component system (TCS) LisRK, linking their expression to environmental stressors such as low pH, ethanol exposure, and bile stress ([PERSON] _et al._, 2015). These sRNAs play a critical role in virulence regulation by targeting genes involved in bacterial adhesion and immune evasion. Bioinformatics and \(\beta\)-galactosidase assays identified _lapB_ mRNA, encoding virulence-associated adhesin, as a direct target of LhrC1-5. Akin to PapR, which negatively regulates the expression of P-fimbriae discussed above, Lhrc negatively regulates _lapB_ expression under bile and antibiotic stress or in blood, facilitating immune evasion by downregulating adhesin expression ([PERSON] _et al._, 2015). Unlike redundant regulatory mechanisms often seen in multicopy sRNAs, LhrC1-5 exhibit additive activity, with their CU-rich motifs binding to the AG sequence within the ribosomal binding site of target mRNAs, allowing precise posttranscriptional regulation. Transcriptomic and proteomic analyses further revealed that LhrC regulates additional virulence-associated genes, including the oligopeptide-binding protein _oppA_, the amino acid ABC transporter _lmo2349_, and the CD4\({}^{+}\) T-cell-stimulating antigen TcsA. Interestingly, LhrC variants demonstrated the ability to bind two OppA target mRNAs via two of their three CU-binding sites, suggesting a sophisticated regulatory mechanism [82]. These findings provide novel insights into LhrC-mediated gene regulation and highlight the potential of targeting LhrC for antimicrobial strategies aimed at disrupting _Listeria_ virulence.
# Extending the Quality of Secure Service Model to Multi-Hop Networks
## 3 QoSS Applied to a Multi-Hop Network  
### Reliability of Systems  
Reliability is defined as the \"ability of an item to perform a required function, under given environmental and operational conditions and for a stated period of time\" [20]. For the purposes of QoSS, the item is a communication network, and the required function is to transmit data in a secure manner. Additionally, the definition of security is the \"availability of performance with respect to prevention of deliberate hostile actions\", where availability of performance includes reliability performance, maintainability performance, and maintenance support performance. With that, the reliability function is the \"probability that the item survives\" over a specific period of time [20]. For the concept of unconditional reliability to be possible, both reliability and security are necessary. The message must be correct and must not be intercepted; in other words, both the disruptor and the listener must fail to achieve their objectives [21].  
Calculation of the reliability of a system varies, depending on whether the system is constructed in series, parallel, or is a hybrid, containing a combination of series and parallel items. Series systems have items, elements, or devices connected in series, and the entire system fails if any of the individual elements fail. The reliability of such a system is calculated as the product of each element's reliability in the series, or specifically:  
\[R_{series}=R_{1}\times R_{2}\times R_{3}\times\cdots\times R_{n}=\prod_{j=1}^{ n}R_{j}, \tag{5}\]  
where \(R\) represents the reliability  
where, again, \(R\) represents the reliability of each element [22]. In contrast to a series system, the reliability of a parallel system is always higher than the reliability of any of its components. Hybrid systems will, of course, vary, depending on their construction.
# Cornering Stiffness Prediction Based on Geometric Method
## 3 Results
### Load Distribution Validation Based on FE Model
#### 3.1.3 Load Distribution Validation  
The basic geometry of the tire was obtained from the tire dimension, with \(R_{0}=315.95\) mm, \(w_{0}=102.5\) mm for FE 1\(\#\), and \(R_{0}=357.85\) mm, \(w_{0}=117.5\) mm for FE 2\(\#\).  
For the tilting method, the radial deformation of the cambered tire \(d^{\prime}\) is obtained through the unchanged deformation volume expression before and after camping. For the rotating method, the loaded radius \(R_{L}\) is obtained from zero camber angle FE simulations. Those results are shown in Table 3.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Force Ranges** & **Slip Angle Range** & **Camber Angle Range** \\ \hline Fx: \(-20\)-\(20\) kN & \\ Fy: \(-20\)-\(20\) kN & \\ Fz: \(0\)-\(30\) kN & \(-25^{\circ}\)\(\sim\)\(25^{\circ}\) & \(-69^{\circ}\)\(\sim\)\(12^{\circ}\) \\ Mx: \(-7.5\)-\(7.5\)-\(5\) kN-m & \\ My: \(-3\)-\(3\) kN-m & \\ Mz: \(-1.3\)-\(1.3\) kN-m & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Jilin University Flat Test Rig specifications.  
Figure 6: Pure camber lateral force results of test data and FE simulation. (**a**) FE 1\(\#\); (**b**) FE 2\(\#\).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Tire**} & \multirow{2}{*}{**Load (N)**} & \multicolumn{5}{c}{\(d^{\prime}\) (mm)} & \multirow{2}{*}{\(R_{L}\) (mm)} \\ \cline{3-3} \cline{5-7}  & & **Camber 0** & & **Camber 2** & **Camber 4** & **Camber 6** & **Camber 8** \\ \hline \multirow{4}{*}{FE 1\(\#\)} & 3200 & 13.400 & 13.216 & 12.684 & 11.879 & 10.84 & 302.527 \\  & 6400 & 26.842 & 26.793 & 26.642 & 26.371 & 25.953 & 289.108 \\  & 7900 & 31.979 & 31.949 & 26.371 & 31.701 & 31.468 & 283.971 \\  & 3200 & 10.916 & 10.689 & 10.053 & 8.996 & 7.592 & 346.934 \\ FE 2\(\#\) & 6400 & 21.173 & 20.847 & 20.407 & 19.714 & 18.800 & 336.677 \\  & 9600 & 30.652 & 30.361 & 30.180 & 29.850 & 29.352 & 327.198 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cambered tire \(d^{\prime}\) and \(R_{L}\) at zero camber angle.  
The load distribution according to tilting and rotating methods are compared with the FE simulations, as is shown in Figure 7. The rotating method results are in good agreement with the FE simulations for both high loads and camber angles, and will thus be used in further modelling.
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 2 Fuzzy Methodologies  
Both FIR and ANFIS machine-learning approaches proposed in this research are hybrid methodologies that combine mainly soft computing techniques. FIR combines fuzzy logic with k-nearest neighbours while ANFIS combines fuzzy logic with neural networks.  
FIR and ANFIS, as fuzzy methodologies, have some main differences that are listed below:  
* ANFIS is a pure fuzzy inference system, i.e., a fuzzy system is trained and a fuzzy inference process is performed for prediction. FIR identifies a fuzzy model that represents the system under study, but the prediction is performed applying a k-nearest neighbour (KNN) hybrid approach.
* ANFIS models are trained by means of neural network training algorithms. FIR models are synthesized rather than trained, meaning that an optimization process is performed to organize the training data appropriately.
* ANFIS models are Sugeno-type fuzzy systems where the antecedents are fuzzy sets and the consequent is not a fuzzy set, but a function. FIR, on the contrary, uses fuzzy sets to represent both the antecedents and the consequent, being more useful for obtaining explainable models that can be used for decision making.
* FIR has an internal feature selection (FS) process that allows identifying the input variables that are most relevant in the inference process and their degree of individual relevance. ANFIS has no FS integrated in its structure, and it is necessary to apply a FS algorithm as a pre-process, if desired.  
The main characteristics of these two methodologies are summarized in this section.
## 4 Procedure and Structural Models
### Gravity Loading  
In addition to the seismic loading, the following gravity loads [55] were used in the analysis: (a) the floor dead load for weight calculations was 4.5 kN/m\({}^{2}\); (b) the floor dead load for mass calculations was 4.04 5 kN/m\({}^{2}\); (c) the roof dead load was 3.9 kN/m\({}^{2}\); (d) the reduced live load per floor and for roof was 0.94 kN/m\({}^{2}\). The seismic mass for the entire structure was: (a) for the roof of the 3-level building it was 1023.09 kN-s\({}^{2}\)/m; (b) for floors 2 of the 3-level building it was 945.6 kN-s\({}^{2}\)/m; (c) for the roof of the 10-level building it was 1054.83 kN-s\({}^{2}\)/m; (d) for floor 2 of the 10-level building it was 996.25 kN-s\({}^{2}\)/m; (e) for floors 3 to 9 of the 10-level building it was 979.22 kN-s\({}^{2}\)/m.  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Place**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Station**} & \(T_{n}\) & **ED** & \multirow{2}{*}{**M**} & **PGA** \\  & & **(Day/Month/Year)** & & & **(k/m)** & & **(cm/s\({}^{2}\))** \\ \hline
1 & Landers, California & 28/06/1992 & Fun Valley, Reservoir 361 & 0.11 & 31 & 7.3 & 213 \\
2 & MammothLakes, California & 27/05/1980 & Convic Creek & 0.16 & 12 & 6.3 & 316 \\
3 & Victoria & 09/06/1980 & Cerro Prieto & 0.16 & 37 & 6.1 & 613 \\
4 & Parkfield, California & 28/09/2004 & Parkfield,loquium Canyon & 0.17 & 15 & 6 & 609 \\
5 & PugeSound, Washington & 29/04/1965 & Olympia Hury Test Lab & 0.17 & 89 & 6.5 & 216 \\
6 & Long Beach, California & 10/03/1933 & UtilitiesBldg, Long Beach & 0.2 & 29 & 6.3 & 219 \\
7 & Sierra El Mayor, Mexico & 04/04/2010 & El centro, California & 0.21 & 77 & 7.2 & 544 \\
8 & Petrolia/Cape Mendocino, & 25/04/1992 & Centerville Beach, & 0.21 & 22 & 7.2 & 471 \\
9 & California & 24/04/1984 & SilroyArraySta \#4 & 0.22 & 38 & 6.2 & 395 \\
10 & Western Washington & 13/04/1949 & Olympia Hury Test Lab & 0.22 & 39 & 7.1 & 295 \\
11 & San Fernando & 09/02/1971 & Castaie-Old Ridge Route & 0.23 & 24 & 6.6 & 328 \\
12 & MammothLakes, California & 25/05/1980 & Long Valley Dam & 0.24 & 13 & 6.5 & 418 \\
13 & El Centro & 18/05/1940 & El Centro-ImpVallrrDist & 0.27 & 12 & 7 & 350 \\
14 & Loma Prieta, California & 18/10/1989 & Palo Alto & 0.29 & 47 & 6.9 & 378 \\
15 & Santa Barbara, California & 13/08/1978 & UCSB Goleta FF & 0.36 & 14 & 5.1 & 361 \\
16 & Coalinga, California & 02/05/1983 & ParkfieldFaultZone 14 & 0.39 & 38 & 6.2 & 269 \\
17 & Imperial Valley, California & 15/10/1979 & Chihuahua & 0.4 & 19 & 6.5 & 262 \\
18 & Northridge, California & 17/01/1994 & Canoga Park, Santa Susana & 0.6 & 16 & 6.7 & 602 \\
19 & Offshore Northern, & 10/01/2010 & Fermdale, California & 0.61 & 43 & 6.5 & 431 \\
20 & Joshua Tree, California & 23/04/1992 & India, Jackson Road & 0.62 & 26 & 6.1 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Earthquake records, \(NS\) component. \(T_{n}\): Elastic period; ED: epicentral distance; M: Magnitude moment; PGA: Peak ground acceleration.