esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Impact of the Number of Cells  
To compare the impact of the number of cells on CBDAS, we partitioned the sensor field into a grid of 6 \(\times\) 6, 8 \(\times\) 8, and 10 \(\times\) 10 cells, respectively. The numbers of cells demonstrated in Figures 6a-c are 6 \(\times\) 6, 8 \(\times\) 8, and 10 \(\times\) 10, respectively. As shown in these figures, CBDAS is better than the other  
Figure 5: Number of rounds when 1%, 20%, 50%, and 100% nodes die. (**a**) Each node with initial energy 0.25 J; (**b**) Each node with initial energy 0.5 J; (**c**) Each node with initial energy 1.0 J.  
approaches. For the number of rounds compared to PEGASIS, Figure 5(a) shows that CBDAS has approximately 1.2 times more for a grid of 6 \(\times\)6 cells, Figure 5(b) shows that CBDAS has approximately 1.3 times more for a grid of 8 \(\times\)8 cells, and Figure 5(c) shows that CBDAS has approximately 1.6 times more for a grid of 10 \(\times\)10 cells.  
In this comparison, we realize that a grid of 10 \(\times\)10 cells is the most appropriate for CBDAS by applying the same parameters of 300 nodes used with initial energy of 0.5 J per node. According to Equation (5), the shorter the distance, the less energy the node consumes. In CBDAS, the ordinary nodes only need to transmit the data locally to their cell head located at a comparatively short distance. The energy consumed by them is thus reduced. Besides, the cell head also only transmits aggregated data to its uplink cell head on the chain within a shorter distance.
## 4 Proposed Framework
### Ontology Design and Deployment
#### 4.2.2 Ontology Customization  
We suggest the **dynamic extension layer** for the ontology, which will ensure adaptability and flexibility in dynamic IoT environments. While the system evolves, this layer enables the integration of new features, device types, or domains without problems.  
Example Schema for Environmental Sensing  
The typical format for environmental sensing data representation is as outlined below:  
\"DeviceID\": \"String\",  
\"Timestamp\": \"Datetime\",  
\"Readings\": {  
\"Temperature\": \"Float\",  
\"CO2 Level\": \"Float\",  
\"Humidity\": \"Float\"\"Location\": {  \"Latitude\": \"Float\",  \"Longitude\": \"Float\"  }  }
# Discrete Lattice Element Model for Fracture Propagation with Improved Elastic Response
## 2 Novel Discrete Lattice Element Model
### Stress Tensor Computation and Fictitious Forces
#### 2.2.2. Fictitious Forces  
Fictitious forces are utilized to adjust the elastic model response for various Poisson ratio values. These forces are introduced into the model as an additional load that must be incorporated into the residual force vector. Firstly, the principal stresses \(\sigma_{1}\) and \(\sigma_{2}\) are computed for an initial state of \(\
u=0\), along with the corresponding principal deformations \(\varepsilon_{1}\) and \(\varepsilon_{2}\). Fictitious deformations are further computed by using these principal strains and the required Poisson ratio, as presented in [8]:  
\[\begin{array}{l}\varepsilon_{1}^{fic}=\
u\varepsilon_{2}\\ \varepsilon_{2}^{fic}=\
u\varepsilon_{1}.\end{array} \tag{8}\]  
By multiplying by the modulus of elasticity \(E\), we can compute the fictitious stresses:  
\[\begin{array}{l}\sigma_{1}^{fic}=E\varepsilon_{2}^{fic}\\ \sigma_{2}^{fic}=E\varepsilon_{2}^{fic}.\end{array} \tag{9}\]  
The corresponding nodal fictitious forces (Figure 6a) can be computed as:  
\[\begin{array}{l}F_{1}^{fic}=\sigma_{1}^{fic}A_{\alpha,1},\ F_{2}^{fic}= \sigma_{2}^{fic}A_{\alpha,2},\end{array} \tag{10}\]  
where areas \(A_{\alpha,1}\) and \(A_{\alpha,2}\) correspond to the principal stress planes.  
Figure 5: Two perpendicular cut planes for retrieving the stresses. (**a**) Cut plane \(A_{\alpha=0}\), (**b**) cut plane \(A_{\alpha=90}\).  
Figure 6: (**a**) The cut plane through the Voronoi cell for computation of nodal fictitious force. (**b**) The distribution of nodal fictitious forces on a cell.  
The nodal fictitious force is in equilibrium with the forces acting in the neighboring elements of the Voronoi cell (Figure 6b). Each elemental force is proportional to the projected area of the Voronoi cell on the cut plane \(A_{a,1}\). After the elemental forces are computed, they are projected into the local coordinate system of the elements and added to the corresponding lattice elements.  
The algorithm for the computation of fictitious forces relies on an iterative procedure for general stress states. However, in the simple cases considered in this paper, iterations are not required [8].
###### Abstract  
Lung carcinoma, which is commonly known as lung cancer, is one of the most common cancers throughout the world. Mostly, it is not diagnosed until it has spread, and it is very difficult to treat. Hence, early diagnosis of benign and malignant pulmonary nodules can help in the risk assessment of lung cancer for patients, and with proper treatment can save their lives. In this study, a framework for the classification of pulmonary nodules from Computerized Tomography (CT) images using the machine learning-based modified gradient boosting method is proposed. Initially, the obtained CT scan images are preprocessed for better image quality. Next, a random walker method is used to segment the lung nodule boundaries based on seeds provided by the user. After that, the intensity and texture features are extracted using the Local Binary Pattern (LBP) filter and the coefficients of the Riesz wavelet transform. Finally, the proposed modified gradient boost classifier model is trained and tested using the extracted features to classify nodules as either benign or malignant. The proposed framework is verified and validated using the Lung Image Database Consortium (LIDC-IDRI) dataset. From the performance analysis, it was observed that the proposed method achieves a precision, recall, F1 score, and validation accuracy of 0.957, 0.91, 0.941, and 95.67%, respectively. The performance of the proposed method is compared with existing models and is found to be superior. It was found that the proposed classifier is able to efficiently classify pulmonary nodules as either benign or malignant.  
as Support Vector Machine (SVM) [8], random forest [9; 10], and the gradient boosting algorithm [11]. Deep neural networks and deep residual networks have also been developed to classify lung cancer from Computerized Tomography (CT) scan images [12; 13]. Because of its non-invasiveness and high spatial resolution, CT is affordable compared to other imaging techniques such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), etc. CT is one of the most often utilized imaging modalities for screening and diagnosing cancer [14]. For clinical purposes, the identification and classification of pulmonary nodules as either benign or malignant is performed by radiologists which is a time-consuming process and prone to errors. It is also a very challenging task due to variable sizes, low contrast, random positions, and the irregular shapes of lung nodules. With the advent of Computer-Aided Design (CAD)-based systems, medical diagnosis has become faster and more accurate, which helps radiologists to improve the sensitivity of their diagnosis by providing a second opinion.
## 5 Experiments
### Emotion Recognition
#### 5.2.1 Datasets  
To train the models for both audio and visual emotion recognition, we used carefully selected datasets known for their diversity and high quality in the field of artificial intelligence.  
2.2 The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) contains controlled audiovisual recordings of actors performing a variety of emotional expressions [13]. For visual emotion recognition, we utilize the facial expression data, which include emotions like joy, sadness, anger, and surprise. On the audio side, the dataset provides high-quality recordings of emotionally expressive speech, which is ideal for training emotion recognition models based on voice analysis.  
Crema-D  
The CREMA-D (Crowd-sourced Emotional Multimodal Actors Dataset) offers both audio and video recordings of actors delivering emotionally charged dialogues [14]. The dataset stands out for its range of emotional expressions, as well as the diversity in the actors' backgrounds, making it particularly useful for training models that need to handle dynamic emotional expressions with varying intensity levels.
# A Partitioned IRS-Aided Transmit SM Scheme for Wireless Communication
## 1 Introduction
The contributions of this paper can be summarized as follows:  
* In PIRS-TSM, SM is performed at the Tx, so both antenna index and modulated symbol can be transmitted in each time slot, effectively resolving the symbolic information absence issue inherent in [20; 22]. In addition, the IRS is divided into groups corresponding to the transmit antennas and phase shifts are made on each IRS group corresponding to the transmit channel states to enhance the differences between channels so as to improve the BER performance. The proposed PIRS-TSM scheme achieves significantly better ABER performance compared to the unpartitioned IRS-TSM scheme. Moreover, even under the same partitioning strategy of IRS, the performance of PIRS-TSM is superior to the existing PIRS-TSSK [20], PIRS-TSSSK [22], and PIRS-FGSSK [22] schemes. * The theoretical ABER bound of the proposed PIRS-TSM scheme is provided based on maximum likelihood (ML) detection and is confirmed by simulations. The computational complexity of the scheme is also investigated and compared with PIRS-TSSK and PIRS-TGSK schemes. * Compared with existing PIRS-TSSK, PIRS-TGSK, and PIRS-FGSK schemes, the joint transmission of antenna index and modulated symbol information and the configuration influence of antenna numbers and symbol orders of PIRS-TSM under the same SE have also been investigated in this paper. Simulation results show that the configuration with fewer transmit antennas and higher modulation orders has better BER performance. The remainder of this paper is organized as follows. Section 2 details the proposed PIRS-TSM system model. Subsequently, Section 3 presents the theoretical derivation and analysis of PIRS-TSM ABER performance. Thereafter, Section 4 conducts an analysis and comparison of the computational complexity of ML detection. Section 5 provides the simulation results along with corresponding discussions and analyses. Finally, Section 6 concludes the paper by summarizing key contributions. _Notation:_\(X_{\Re}\) and \(X_{\Im}\) represent the real and imaginary part of a complex variable \(X\), respectively. \(j=\sqrt{-1}\) represents the imaginary unit. \(\text{E}[X]\) and \(\text{Var}[X]\) denote the mean and variance of \(X\), respectively.
## 2 Theoretical Basis
### Estimating User Costs
#### 2.3.1 Cost Savings in User Travel Time  
The user travel time cost is the value that arises due to the existence of an opportunity cost of the time consumed by the vehicle during the trip [18; 19]. The savings in user travel time cost after implementing pavement maintenance \(\Delta T\) can be expressed by Equation [10]:  
\[\Delta T=\theta\cdot Q\cdot(T_{0}-T_{1}) \tag{6}\]  
where  
\(\theta\)--the time value coefficient, usually related to the personal income of regional travelers [20].  
Q--the traffic volume of the road section((pcu/d)).  
\(T_{0}\)--the travel time of a single vehicle before maintenance.  
\(T_{1}\)--the travel time of a single vehicle after maintenance.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Combination** & **PCI** & **RQI** & **SRI** & **PSSI** \\ \hline
1 & Good & Good & Sufficient & Sufficient \\
2 & Fair & Good & Sufficient & Sufficient \\
3 & Poor & Good & Sufficient & Sufficient \\
4 & Good & Fair & Sufficient & Sufficient \\
5 & Fair & Fair & Sufficient & Sufficient \\
6 & Poor & Fair & Sufficient & Sufficient \\
7 & Good & Poor & Sufficient & Sufficient \\
8 & Fair & Poor & Sufficient & Sufficient \\
9 & Poor & Poor & Sufficient & Sufficient \\
10 & & & & & \\
11 & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Condition combination of the expressway asphalt pavement performance.  
The calculation of Equation (6) requires a functional relationship between the user travel time of the road section and the pavement condition. The PCI is then adopted as an indicator of the pavement condition. The functional relationship between v and the PCI can be obtained through tests on a large number of road sections, which is expressed as follows:  
\[T=\frac{l}{v}=\frac{l}{0.82 PCI+16.5} \tag{7}\]  
where \(l\) is the section distance (km), \(v\) is the vehicle speed (km/h).  
Therefore, the savings in user travel time costs after the pavement maintenance can be represented as follows:  
\[\Delta T=Q\cdot\theta\cdot(\frac{l}{v_{0}}-\frac{l}{v_{1}})=Q\cdot\theta\cdot (\frac{l}{0.76 PCI_{0}+0.35}-\frac{l}{0.76 PCI_{1}+0.35}) \tag{8}\]  
where \(PCI_{0}\) represents the PCI value of the road before maintenance and \(PCI_{1}\) represents the PCI value of the road after maintenance.
# PDTR: Probabilistic and Deterministic Tree-based Routing for Wireless Sensor Networks
## 6 Simulation Results
### Control Parameters Upper Bound  
The user has to decide the values of the control parameters, depending on the requirement of the applications. The role of each parameter is to determine the impact of the corresponding distribution on the other distributions. The values of the control parameters should be selected in order to keep the influence of the three distributions. After intensive experimental work, it was concluded that to maintain this objective, the upper bound of control-parameters denoted by \(U_{b}\), has a strong relation with the network density deployment; this relation is defined by Equation (26). In the homogeneous network, sensors are deployed at the interest network area \(\Omega\), and they have the same communication radius, denoted by \(\delta\). To calculate the density of the deployment of these \(N\) sensors on the given network, we consider two parts. First, the area of each sensor in the network is denoted by \(S_{n_{i}}\) (it is the same for all sensors, since sensors are homogenous), and is defined by Equation (22). Second, the average of sensors' neighboring nodes in the network, denoted by \(\overline{M}\) and defined by Equation (23). The density of sensor node deployment in the network is denoted by \(\mathfrak{D}\), and is defined by Equation (25), and the sum of the area of all sensors is denoted by \(S_{N}\), and is defined by Equation (24), par the interest network area \(\Omega\).  
\[\forall n_{i}\in\mathbb{N},\;\;S_{n_{i}}=\pi\delta^{2} \tag{22}\]  
\[\overline{M}=\frac{\sum_{0}^{n}m_{i}}{n},\;\text{{such that}}:\;|N_{i}|=m_{i} \tag{23}\]  
\[S_{N}\cong S_{n_{i}}\left(\frac{n+\overline{M}}{2}\right)\;\cong\pi\delta^{2} \left(\frac{n+\frac{\sum_{0}^{n}m_{i}}{2}}{2}\right), \tag{24}\]  
\[\mathfrak{D}\cong\frac{S_{n_{i}}\left(\frac{n+\overline{M}_{i}}{2}\right)}{ \Omega}=\frac{S_{n_{i}}\left(n+\overline{M}_{i}\right)}{2\Omega}, \tag{25}\]  
\[U_{b}=2\mathfrak{D}.\;(\text{Experimental view}) \tag{26}\]
# Nutritional Support for Liver Diseases
## 2 Nutritional Support for Liver Diseases
### Cholangiocarcinoma
#### 2.1.1 Oral Nutritional Supplements (ONS)  
The administration of ONS provides beneficial effects in multiple conditions/ diseases [14; 15]. In the study by [PERSON] et al., the effect of ONS consumption in patients with pancreatic and bile duct cancer who received chemotherapy was assessed [16]. The participants were divided into two groups: the first (\(n=15\)) consumed ONS (at a dose of two packages per 8 weeks), and the second (\(n=19\)) did not receive ONS. It was noted that the consumption of ONS significantly increased the daily intake of both energy and macronutrients, as well as positively affecting body mass. Moreover, it was observed that ONS alleviated fatigue symptoms [16]. The data on ONS usage in the case of bile duct cancer are still strongly limited. Nevertheless, there are some studies registered in the ClinicalTrials.gov system (such as \"Influence of an Oral Nutritional Supplement Rich in Omega-3 Fatty Acids on Functional State and Quality of Life in Malnourished Patients with Gastroenterological Tumors\" identifier: NCT00168987; diseases: colorectal neoplasm, hepatocellular carcinoma, cholangiocarcinoma).
# Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction
## 4 ROIE: A Recurrent Neural Network Model for Open Information Extraction
### Word Embeddings  
Recently, several types of word embeddings have been introduced; nevertheless, they all serve the same purpose of mapping words to low-dimensional vector representations. The aforementioned OIE and RE deep learning-based approaches in Sections 3.1.3 and 3.2, respectively, utilized one of the traditional word embeddings, either GloVe [34] or Word2 Vec [35].  
In our work, we incorporated the novel contextualized word embeddings. Due to their ability to capture complex syntactic and semantic features of a word, deep contextualized word embeddings have proven to be successful in various NLP tasks when compared to the traditional word embeddings. The main concept behind contextualized word embeddings is that a word's representation varies according to its neighboring words, and thus the same word can have different representations depending on its adjacent words.  
Table 2 shows the word embeddings we employed in our experiment, along with the dimensionality of each embedding and the data they are trained on. We picked one traditional non-contextualized embedding, GloVe, and three contextualized embeddings with different dimensionalities: BERT [9], XLNet [36], and XLM-RoBERTa [37]. XLNet is trained on data much larger than Google's BERT training data, and thus it outperforms BERT on 20 different NLP tasks [36]. Facebook's XLM-RoBERTa depends on the masked language model objective and is effective in text processing from 100 different languages.  
Flair [38] is a simple framework that offers a unified interface for conceptually varying types of word and document embeddings, which we utilized in our experiments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Embedding** & **Dimensionality** & **Trained On** \\ \hline GloVe [34] & 100 & Aggregated global word–word co-occurrence statistics from a corpus. \\ BERT [9] & 3072 & Wikipedia and +10,000 books of different genres. \\ XLNet [36] & 2048 & Over 130 GB of textual data. \\ XLM-RoBERTa [37] & 1024 & 2.5 TB of filtered CommonCrawl data. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Word embeddings employed in our work.
###### Abstract  
The directional antenna combined with beamforming is one of the attractive solutions to accommodate high data rate applications in 5G vehicle communications. However, the directional nature of beamforming requires beam alignment between the transmitter and the receiver, which incurs significant signaling overhead. Hence, we need to find the optimal parameters for directional beamforming, i.e., the antenna beamwidth and beam alignment interval, that maximize the throughput, taking the beam alignment overhead into consideration. In this paper, we propose a reinforcement learning (RL)-based beamforming scheme in a vehicle-to-infrastructure system, where we jointly determine the antenna beamwidth and the beam alignment interval, taking into account the past and future rewards. The simulation results show that the proposed RL-based joint beamforming scheme outperforms conventional beamforming schemes in terms of the average throughput and the average link stability ratio.  
vehicle communications; antenna beamwidth; beam alignment overhead; beam alignment interval; reinforcement learning +
Footnote †: journal: Sensors  
csc: 2024
# Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning
## 3 Statistical Validation Method
### Concept and Previous Work  
This framework takes its fundamental ideas from the statistical VV+UQ frameworks of [PERSON] and [PERSON] [28; 40] and [PERSON] and [PERSON] [37; 41]. While [PERSON] [21] specialises in computational fluid dynamics and [PERSON] [41] in general engineering systems, this framework focuses on multi-component, modular modelswith a large number of parameters in automotive total vehicle simulations. Previous work has been carried out in the automotive domain, upon which this framework is directly based: (1) the review of [PERSON] et al. [1], which analyses and structures statistical validation frameworks. (2) The review of [PERSON] et al. [30], which compares a large number of uncertainty validation frameworks and brings them into one main structure. (3) The method and application of [PERSON] et al. [4], which is concentrated on statistical validation metrics for automotive total vehicle simulation. The statistical validation application with theoretical data of [PERSON] et al. [42] focuses on predicting the reliability quantification of simulation results under new application scenarios of autonomous vehicles. In contrast, this paper focuses on predicting the reliability quantification of simulation results under new application parameter configurations using real experimental data. In this paper, parameter configurations are internal vehicle parameters and not external scenarios or scenario parameters. The basic idea of the validation framework is explained in Figure 3.  
In M+S, it is essential to validate and estimate the reliability of a model and each single simulation result depending on arbitrary parameter configurations of a large application domain \(a\). Therefore, the model and result reliability of several validation parameter configurations must be estimated with multiple validation experiments forming the validation domain \(v\). The model and result reliability in the application domain can be predicted through inter- and extrapolation based on the knowledge about the reliability from the validation domain.
## 3 Results and Discussion
### Optimization of DES-TF-SPME Conditions
#### 3.3.4 The Time of Extraction  
The extraction time in SPME is crucial as it influences the analytical response. Although the greatest sensitivity of SPME methods is observed when the extractions are performed at equilibrium, reaching this state may be very long. In the present experiment, the influence of time on the extraction was examined at 5, 10, 15, 20, 25, and 30 min. The results presented in Figure 7c show the optimal value at 20 min and no further increase of analytical signal was gained at 25 and 30 min.