esa-sceva/satcom-chunk-collection huggingface dataset:
# Test Bed of Semantic Interaction of Smart Objects in the Web of Things
## 3 Architecture Deployed  
In the following, an architecture is deployed to develop the test bed. The architecture is based on the Nino-Zambrano Nino-Zambrano (2016) approach. It presents well defined layers, is service oriented and is addressed by a WoT semantic interaction model. The architecture has these main features:  
* Distribute the information logic in the network edge (\"fog computing\" [PERSON] (2012)), reducing the request time and the communication requirements.
* Reuse context and services, these are not linked with any entity, environment or purpose. The relationships depend on the usage of the objects.
* Allow objects linking, in order to cooperate in the new services creation. The objects can take part in different objectives.  
Specific devices, protocols, and servers are named. However, depending on the design needs of the developers, this architecture can be instanced in any type of hardware that supports the mentioned characteristics of a smart object.  
For a better understanding, the following basic concepts that are proposed in the architecture should be highlighted.  
* Physical representation: consisting of the material and tangible part of a device.
* Digital representation: the information stored digitally on a physical representation of the device. In this case it is stored in a middleware called Xively and in each smart object.
* Resource: elements that have the objects to interact with the physical world. These can be either sensors, which examine the physical environment and capture the information that enters the system, or actuators, which perform an action on the physical environment in order to change its status.
* Entity: representation of a person, place or thing, which is related to one or more smart objects and which are subject to measurement or action.
* Entity of interest: an entity that is the object of an information search or an interaction by a user.
* Basic service: the service of a smart object by default, it is associated with the original operation with which it was created.
* Interaction service: generated by the interaction between two objects. Each interaction service is supported by an ECA and is executed by the object that contains the actuator resource.
* ECA (event-condition-action): XML document with the information of the contract between two smart objects. The ECA contains the logic of the interaction service and informs the objects about the variables and the resources that intervene in the execution of an interaction service. The event and action are related to smart objects and the condition is defined by the user.  
The architecture presented in Figure 1 organizes the different tools and elements in a service-oriented model in which six layers are defined.  
Figure 1: Architecture.
# Polarization Differential Visible Light Communication: Theory and Experimental Evaluation +
## 2 System Model
### Modulation and Demodulation  
In order to transmit data using linearly polarized light, the intensity of the transmitted light is kept constant, but the polarization axis is rotated orthogonally in order to represent '1' and '0' bits. To achieve this in practice, a setup using two identical LED transmitters T\({}_{X1}\) and T\({}_{X2}\) is constructed where both LEDs are equipped with mutually orthogonal linear polarizers as indicated in the upper part of Figure 1. The LEDs are power switched in a complementary way, i.e., LED T\({}_{X1}\) is on while T\({}_{X2}\) is off during a bit period \(T_{b}\), thus emitting polarized light along the axis of the T\({}_{X1}\) LP to represent a '1'-bit Vice versa, turning LED T\({}_{X1}\) off whilst turning T\({}_{X2}\) on emits light polarized along an axis orthogonal to the first case, effectively representing a '0'-bit. Following this mode of operation, the information is thus completely embedded in the polarization direction of the transmitted light. This modulation scheme can also be considered to be a polarization-based variant of On-Off Keying (OOK).  
Demodulation at the receiver can be effectively realized using a similar setup. A pair of photodiodes, R\({}_{\text{X1}}\) and R\({}_{\text{X2}}\), are likewise equipped with mutually orthogonal polarizers as indicated in the lower part of Figure 1. The differential signal between this pair of photodiodes can then be used to reconstruct the transmitted signal at the transmitter. As the LEDs are transmitting an identical intensity in a complementary fashion, the received intensity is constant over time if no polarizing optics were equipped, thus limiting interference with conventional intensity-based VLC and inherently preventing human-perceivable flicker. The receiver polarizers maintain mutual orthogonal alignment by the design of the receiver, but unlike the transmitter polarizers, the orientation of the receiver polarizers is not fixed and can rotate over an angle \(\theta\) with respect to the transmitter polarizers axes. At \(\theta=0^{\circ}\) the polarizer at T\({}_{\text{X1}}\) is parallel with the R\({}_{\text{X1}}\) polarizer while at T\({}_{\text{X2}}\), the polarizer is parallel with the R\({}_{\text{X2}}\) polarizer. In this setup, the distance between transmitter and receiver is denoted as \(h\), as illustrated in Figure 1.
# Bridge Assessment under Earthquake and Flood-Induced Scour
## 2 Methodology
### Fragility Curves and Surfaces Development
#### 2.6.1 Fragility Curves at the Component Level  
A two-parameter log-normal distribution (log-ND) characterizes the seismic fragility curve and is defined as follows [18]:  
\[F\left(x_{\text{j}};\theta_{\text{k}},\beta_{\text{k}}\right)=\Phi\left(\frac{ \ln\left(x_{\text{j}}/\theta_{\text{k}}\right)}{\beta_{\text{k}}}\right) \tag{2}\]  
The function of fragility function \(F(x_{\text{j}};\theta_{\text{k}},\beta_{\text{k}})\) gives the probability of a seismic action with IM (PGA) = \(x_{\text{j}}\) will lead to bridge element failure at DS \(k\) (slight, moderate, major, and complete). The function \(\Phi(\ )\) represents the cumulative distribution (CDF) with fragility parameters, \(\theta_{\text{k}}\) (median) and \(\beta_{\text{k}}\) (standard deviation), of the log-ND at damage state \(k\), respectively. Given the logical presumption that \(x_{\text{j}}\) for each seismic level operates independently, the fragility parameters are evaluated with the maximum likelihood method for truncated incremental dynamic analysis (IDA) [12]:  
\[L=\left(\prod_{j=1}^{m}\Phi\left(\frac{\ln\left(x_{\text{j}}/\theta_{\text{k}} \right)}{\beta_{\text{k}}}\right)\right)\left(1-\Phi\left(\frac{\ln\left(x_{ \text{max}}/\theta_{\text{k}}\right)}{\beta_{\text{k}}}\right)\right)^{n-m} \tag{3}\]  
For \(n\) seismic levels used, there will be \(m\) seismic levels that cause failure at \(x_{\text{j}}\) levels less than \(x_{\text{max}}\), and \(n-m\) seismic levels that result in failure prior to the termination of the analyses.
## 5 Data Security and Privacy Framework (Alpha Version)
### Identification of Possible Threats and Vulnerabilities  
A structured process is required to examine how vulnerable an application is, and which types of attack can be launched to compromise the application. Threat modelling is a widely recognised process for identifying the possible threats to an application and is considered a significant step in assuring security. Threat modelling activities will start with defining the scope and data flow of the application. There are several tools and methods available to conduct threat modelling such as STRIDE, Linddun, The Process for Attack Simulation & Threat Analysis (PASTA), and Trike.
## 2 Research Methodology
### Robust BFGS Parameter Inversion Algorithm  
Probabilistic integral parameter inversion for parameterization with the IGGIII-BFGS algorithm uses a stepwise BFGS weight selection iteration method:  
(1) Set the initial value of the parameters to be obtained according to the experience value of the probability integral parameter differentiated by stratum properties: q\({}_{0}\), tan \(\beta_{0}\), \(\theta_{0v}\), \(s_{1_{0}}\), \(\varepsilon_{2_{0}}\), \(s_{3_{0}}\), \(s_{4_{0}}\), \(\text{p}_{0}\), and the initial Hessian approximation array \(H_{0}\), the iterative convergence accuracy \(\varepsilon\).  
(2) Calculate the predicted subsidence value based on parameter vector X, compute the residual difference between the predicted value and the observed value, update the weight matrix using the IGGIII weight function, and then build the objective function f\({}_{1}\):  
\[\text{f}_{1}=\sum_{i=1}^{\text{N}}[W_{i}(X)-W_{i}]^{T}\times\text{P}_{\text{W} }\times[W_{i}(X)-W_{i}] \tag{19}\]  
In the formula presented, N is the number of ground observation points; \(\text{P}_{\text{W}}\) is the weight matrix corresponding to the residuals; X is a vector of parameters to be solved consisting of the parameters q\({}_{0}\), tan \(\beta_{0}\), \(\theta_{0v}\), \(s_{1_{0}}\), \(\varepsilon_{2_{0}}\), \(s_{3_{0}}\), and \(s_{4_{0}}\); and the function \(W_{i}(X)\) with independent variable X can be obtained by substituting the working face location data and geological mining parameters into the subsidence prediction formula.  
The BFGS algorithm is utilized to calculate the partial derivatives cyclically and further calculate the Hessian approximation array, search direction d, and step size \(\alpha\) to update the parameters until the partial derivatives satisfy the convergence conditions to stop the loop. Then, determine whether the parameters satisfy the convergence condition; if not, re-update the weight array and the objective function to find the parameters until they satisfy the convergence conditions and output parameter vector X.  
(3) Substitute parameter X, the working face position data, and the geological mining parameters into the horizontal movement formula to obtain function U (b) with the independent variable b. Establish objective function f\({}_{2}\), and calculate parameter b using the same method.  
\[\mathrm{f}_{2}=\sum_{i=1}^{N}[U_{i}(\mathrm{b})-U_{i}]^{T}\times\mathrm{P}_{ \mathrm{U}}\times[U_{i}(\mathrm{b})-U_{i}] \tag{20}\]  
In the formula presented, U\({}_{i}\) is the measured value of horizontal movement, and P\({}_{\mathrm{U}}\) is the weight matrix corresponding to the residual of horizontal movement. Function \(U_{i}(\mathrm{b})\) with independent variable b can be obtained by substituting the working face location data and geological mining parameters into the subsidence prediction formula.  
The specific steps are shown in Figure 1:
# Experimental Damage Identification of a Model Reticulated Shell
## 2 Methodologies
### Damage Detection Flowchart  
Figure 2 illustrates the procedure of the proposed damage detection method, which is described as follows:  
1. Measure the acceleration responses of the reticulated shell structure before and after damage under the excitation condition.
2. Obtain the stationary random acceleration signals by using the data pre-processed method introduced in Section 2.1.
3. Establish the auto-regressive time series model (AR model) by using the modeling method introduced in Section 2.1.
4. Detect the damage of the reticulated shell according to the changes in the coefficients of the AR model before and after damage.
5. Obtain the damage sensitive factors by using Equation (2).
6. Train the back-propagation neural networks (BPNNs) by using the method introduced in Section 2.2, with the damage sensitive factors as the inputs and the damage positions as the outputs.  
Figure 1: The architecture of a back-propagation neural networks (BPNNs).  
(7) Find the locations of the damages by testing the trained BPNNs.  
This paper develops the novel application of a damage detection method based on the time series AR model and BPNNs using acceleration responses of the reticulated shell. This method has the advantages of relaxing the requirement of prior knowledge about the excitation input and the modal parameters. It effectively eliminates the method's dependency on modeling accuracy and improves the damage detection accuracy for a reticulated shell.
# A Review on Deep-Learning-Based Cyberbullying Detection
## 5 Deep-Learning-Based Models
### Attention-Based Model
#### 5.10.2 BERT (Bidirectional Encoder Representations from Transformers)  
BERT (Bidirectional Encoder Representations from Transformers) has demonstrated outstanding results in detecting cyberbullying since it is an extensive language model that can extract contextual information from a given text. The transformer model can capture intricate associations between words and contexts. BERT can accurately detect bullying behavior in social media messages when the cue is subtle or indirect [22; 116; 117].
## 3 Results
### RQ1: Impact on Decision-Making  
Figure 5 shows the distribution of WOA of our CDSS with explanation by feature contribution or example. One-sample one-sided Wilcoxon signed-rank tests indicated that WOA for both XAI methods were significantly greater than zero (explanation by feature contribution: V = 780, \(p\)-value < 0.001; explanation by example: V = 861, \(p\)-value < 0.001). This showed that the participating healthcare practitioners took a substantial amount of advice from our CDSS with either type of explanation, and that our explainable CDSS with either type of explanation had a significant impact on healthcare practitioners' decision-making.
# Enhanced Resistance to Fire of the Bark-Based Panels Bonded with Clay
## 3 Results and Discussion
These findings are consistent with the results of [59], who emphasized the role of mineral coatings for wood composites in order to avoid fire hazards. Therefore, the composites made of larch bark and clay could be a good solution for fire-resistant decorative boards, especially for different types of protection walls. Isolith, Knauf and Heraklith cement-bonded boards have a fire resistance of B s1 d0, which is comparable to the bark clay panels in this research. Samples A, B, C, D, E, F, G and H were also tested for thermal conductivity (\(\lambda\)). They were not essentially designed for thermal insulation purposes because their target density is high (from 600 to 950 kg/m\({}^{3}\)). The aim of this test (Figure 7) is to identify the \(\lambda\) values as a material property for the composite boards with larch bark, using three different particle sizes (\(<\)4 mm, 4 \(<\)\(\times\) \(<\) 11 mm and \(>\)11 mm) [38]. The thermal conductivity of boards A and C (Figure 7) are relatively similar at measuring temperatures of 10 \({}^{\circ}\)C, 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, respectively. Figure 6: Small flame test after 30s, applied on the surface and edge of the composite boards with larch and poplar bark and clay. A decrease of \(\lambda\) values was observed for panel B with 600 kg/m\({}^{3}\)[38]. Thermal conductivity for panel B has an excellent value despite a higher density, and can also serve as thermal insulating material [60]. The \(\lambda\) value of thermal insulation materials covers, although a higher spectrum, below 0.1 W/mK [61]. The measurements at 25 \({}^{\circ}\)C for panels B (0.07 W/mK), E (0.1 W/mK) and F (0.1 W/mK) are consistent with the values for the Isolith, Heraklith and Knauf panels, as well as with the findings of [62,63]. They studied the thermal conductivity of tree bark panels bonded with tannin adhesives and reported values between 0.06 and 0.09 W/mK. The advantage of the bark clay boards as an insulation material is mostly in the well-known accumulative capacity of clay, which can significantly improve the climatic stability of indoor spaces, mostly in spaces with low ventilation rates. Another advantage of clay, as a component in bark clay boards, is the excellent absorbance and diffusivity of water vapor [64]. In the analysis of fiberglass-urethane, fiberglass-rigid, urethane-rigid, perlite, extruded polystyrene and urethane-roof deck materials, [PERSON] et al. [65] concluded that there should be a relation between the thermal conductivity and the thickness of the insulation panel for each material. They proposed a non-linear relationship between the \(\lambda\) value and optimal thickness (\(x_{opt}\)) that obeys a polynomial function:  
\[x_{opt}=a+b\cdot k+c\cdot k^{2} \tag{1}\]  
where \(a=0.0818\), \(b=-2.973\) and \(c=64.4\). In the case of thermal conductivity, the thickness of the board plays an important role, correlated with density. The thickest board (33 mm) reached values that are consistent with the results from other recent studies.
# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 4 PID Controller Parameter Tuning Based on the Improved QUATRE Algorithm  
The performance index of PID control is a set of standards for judging the quality of the controlled object. The evaluation index has the function of guiding the field personnel to adjust the PID parameters to establish a reasonable control system [29]. The selection of control indicators has an important influence on the results of the control. Commonly used performance indicators include dynamic performance indicators and error integral performance indicators [30]. This article uses the absolute error multiplied by time (ITAE) performance index:  
\[ITAE=\int_{0}^{\infty}t\ |e(t)|dt \tag{12}\]  
In the improved QUATRE algorithm, all the particles can be regarded as a combination of PID controller parameters. In a given search space, the particles are constantly iteratively optimized, and the globally optimal individuals are constantly updated. The algorithm will converge to the optimal solution, that is, the parameter combination with the best control performance for the controlled object. Additionally, QUATRE algorithm is improved to optimize PID parameters, especially the first set value range of PID parameters, namely the particles under the different dimensions of search, then all particles and random distribution patterns are initialized using the Sim function assigned to the PID controller parameters, through the system performance index function to calculate all the particle's fitness, and the optimal adaptive value and position of particles are kept in the search space with the constant iterative update, which obtains the global optimal particle of the optimal PID controller parameters. The main steps are as follows:  
1. Set the search space of PID parameters, initialize the position of the three parameters of the particle, particle swarm size, number of iterations, and constant term C of the difference matrix.
2. The position of each particle is transferred to the established PID control model PID MODEL, and the SIMULINK program is called to simulate and calculate the ITAE performance index of the controller as a fitness function which is then transmitted back to the QUATRE algorithm.
3. Determine according to the ITAE performance index of the current particle swarm, search all particles to determine the optimal position of all, and perform position updates for individuals that meet the performance requirements.
4. Judge whether it is required according to the termination condition. If the current situation meets the stop condition of the algorithm, exit the algorithm and take the current optimal position as the optimal proportional, integral, and differential coefficients of the PID controller. If not, perform a speed and position update operation, judge and limit particle update speed and optimization range, then execute Step 2.
# Predicting Location of Tweets Using Machine Learning Approaches
###### Abstract  
Twitter, one of the most popular microblogging platforms, has tens of millions of active users worldwide, generating hundreds of millions of posts every day. Twitter posts, referred to as \"tweets\", the short and the noisy text, bring many challenges with them, such as in the case of some emergency or disaster. Predicting the location of these tweets is important for social, security, human rights, and business reasons and has raised noteworthy consideration lately. However, most Twitter users disable the geo-tagging feature, and their home locations are neither standardized nor accurate. In this study, we applied four machine learning techniques named Logistic Regression, Random Forest, Multinomial Naive Bayes, and Support Vector Machine with and without the utilization of the geo-distance matrix for location prediction of a tweet using its textual content. Our extensive experiments on our vast collection of Arabic tweets From Saudi Arabia with different feature sets yielded promising results with 67% accuracy.  
location prediction; location extraction; Twitter; Arabic tweets; social media; computational linguistics; natural language processing; feature selection; machine learning +
Footnote †: journal: Medical Intelligence
# Retrospective Study on Seismic Ionospheric Anomalies Based on Five-Year Observations from CSES
## 3 Results
### Summative Analysis
Additionally, anomalies in the electromagnetic field are often detected as early as 20-25 days before the EQ. 4. The spatial distribution of anomalies exhibits an overall tendency to shift toward the equator, but nearly all of the anomalies fall within [PERSON]'s radius. Specifically, for EQs occurring in the Northern Hemisphere, anomalies tend to appear in the south of the epicenter, and vice versa in the Southern Hemisphere, though they are not as obvious as those for EQs in the Northern Hemisphere.
## 1 Introduction
Based on the existing research outcomes, it can be found that comprehensive parametric analyses of the SSI effect on structural seismic demands considering the nonlinearities of structure and soil are rarely reported. To provide a reference for fast regional seismic damage evaluation considering SSI, based on a nonlinear numerical model to account for the SSI effect, detailed parametric analysis considering the influence of the pile length, pile diameter, number of spans, frequency of the structure, seismic records, and soil shear wave velocity on SSI effects were conducted.