esa-sceva/satcom-chunk-collection huggingface dataset:
# Dealing with Low Quality Images in Railway Obstacle Detection System
###### Abstract
In order to avoid further complications of the overall system's functionality and save time, the blur detection (BD) problem needs to be addressed by an algorithm which will satisfy requirements with regard to simplicity, computation time, and accuracy. Many studies, which will be discussed in more detail in Section 3, are particularly interested in the BD problem because blur is one of the most significant distortion types in images. The solution to this challenge could be the beginning of a de-blurring procedure or some other form of picture processing, but the focus in this paper is exclusively on blur and non-blur image classification. The existing literature on this topic shows that among the many approaches, three groups of blur approaches can be distinguished: frequency-based, edge-based, and depth-based methods. We achieved the best overall results using BD based on the Laplace operator, which seems to be an ideal candidate that satisfies all the previously specified requirements. Certain practical challenges were handled during the project by upgrading the existing method and introducing new functionality. The main disadvantages regarding inappropriate results in the presence of noise and the manual selection of the threshold were overcome by implementing our own solutions which are elaborated in Section 4. At the end of the paper in Section 5, we evaluate our proposed algorithm using real-world images of railway scenes with obstacles and compare the obtained results with several other state-of-the-art methods. We select different methods which belong both to the group of edge-based and frequency-based algorithms and conduct an analysis by comparing the relevant parameters such as precision, accuracy, recall, and F score. It is shown that the proposed method gives the best overall results in the railway application for each of the proposed evaluation measures. Finally, the results of the evaluation of the performance of different approaches using the real-world railway dataset are presented in the form of a precision recall curve, which also favors the proposed method. In summary, the major contributions of the proposed paper are as follows:  
1. We proposed an improved Laplacian-based edge detection algorithm for blur detection;
2. The main disadvantage of the edge-detection algorithm regarding the manual setting of the threshold was overcome by introducing the automatic selection of threshold values;
3. We implemented the proposed algorithm in real-case scenarios in the railway application for obstacle detection;
4. We improved the performance of our algorithm even further by selecting separate thresholds for day and night images;* Rigorous experiments were performed against several state-of-the-art methods over our own real-world railway dataset to prove the effectiveness of the proposed algorithm.
## 3 Properties of Different STBCs Signals
### First STBC Option, \(\alpha=c_{0}\)  
Based on the broadcast matrix shown in (1a), the sent vectors from antennas \(q=0\) and \(q=1\) can be respectively written as \(\mathbf{W}_{q=0}^{(\alpha=c_{0})}=\!\!\left[d_{0}^{(0)},d_{0}^{(1)},\cdots,d_{ 0}^{(b)},\cdots\right]\), and  
\(\mathbf{W}_{q=1}^{(\alpha=c_{0})}=\!\!\left[d_{1}^{(0)},d_{1}^{(1)},\cdots,d_{ 1}^{(b)},\cdots\right]\). Taking into account that \(d_{i}^{(b)}\) and \(d_{i^{\prime}}^{(b^{\prime})}\) are independent symbols, one writes  
\[\mathrm{E}\Big{[}\overline{w}_{q}^{(\alpha=c_{0})}(n)\overline{w}_{q^{\prime }}^{(\alpha,c_{0})}(n+\tau)\Big{]}=0, \tag{7}\]  
for all possible values of \(\tau\), \(q\), and \(q^{\prime}\). Using (3), (5), and (7) in (6), we show that  
\[U^{(\alpha=c_{0})}(\tau)=0\quad\text{for all possible values of $\tau$}. \tag{8}\]
## 5 Case Study: Experimental Validation in a Representative Smart Building Scenario
### Implementation of the JFML-IoT-Based Energy Management System  
This section presents the implementation details of the proposed energy management system using the JFML-IoT framework. The implementation illustrates how to instantiate and configure the components required to deploy the control system in a real-world scenario. It includes the creation of sensor and actuator objects, the association of devices with embedded systems (e.g., RasperrryPi, Arduino or ESP32), and the configuration of a central inference node executing the fuzzy logic controller encoded in FML.
## 4 Homogeneous Soil Slope (Simple Slope) Stability Analysis Using the FLAC3D Model
### Analysis of Displacement-Reduction Factor Relationships at Monitoring Points  
The relationship between the horizontal, vertical, and total displacements and the reduction coefficient of different monitoring points is recorded, as shown in Figure 7. Also, as can be seen from contour plots, only the displacement curves of points 1, 2, 3, 4, and 5 exist near the boundary slip line, so qualitatively, these points can be considered effective monitoring points with horizontal, vertical, and total displacement positions. The closer the point is to the slope curve, the greater the slope. In some literature, the foot of the slope is chosen as the monitoring point. This is not universal; in this example, the critical slip line does not pass through the foot of the slope, and it cannot indicate whether the slope is damaged or not. Therefore, the position of the slip line is not determined. In this case, it is inappropriate to select the toe of the slope as the monitoring point.  
Figure 7: _Cont._The numerical model displays simulated displacement outputs across monitoring locations and parametric scenarios for a homogenous soil slope stability model. Figure 6(a) presents the horizontal component of displacements. Positive values represent downslope translations, with magnitudes increasing over higher displacement reduction factors up until instability thresholds are exceeded, indicating translational failures. This is consistent with the findings of [PERSON] et al. regarding the acceleration of horizontal deformations prior to collapse [41]. Figure 6(b) contains the vertical settlement displacements, with subsidence trends observable leading up to instability thresholds, as highlighted by [PERSON] et al. regarding precursory deformation [42]. Finally, Figure 6(c) summarizes the total 3D displacement vectors. At some monitoring points, horizontal translations dominate the failures, while vertical subsidence dominates at other locations--demonstrating spatially heterogeneous modes as analyzed by [PERSON] et al.'s geotechnical fragility framework [43; 44; 45].
# State-of-the-Art Techniques in RF Energy Harvesting Circuits
## 3 Results
### Design Specification
#### 3.2.5 Load  
The most basic parameter that characterizes the quality of RF to DC conversion systems, as shown in the above analysis, is the power conversion efficiency (PCE). PCE refers to the amount of energy transferred from the device and is defined as:  
\[n=\frac{P_{in}}{P_{out}},\;P_{out}=\frac{V_{out}^{2}}{R_{L}} \tag{3}\]  
\(P_{in}\) is the RF input power, \(P_{out}\) refers to the output power with \(V_{out}\) as the output voltage and RL as the output load. To achieve maximum efficiency of the device, the load resistance must be adjusted. However, high voltage requires high resistance, according to [PERSON]'s law. Therefore, trade-off becomes inevitable. From the above equation, we understand the importance of choosing the proper load value. The load can be capacitive, inductive, or purely ohmic. For this reason, all works involved in the design of such circuits are given a special study of what load will be used. As can be deduced from [43, 54], the efficiency of the system decreases when the load increases. As a result of the previous analysis, we notice that it is very important that the load impedance be carefully selected for a specific energy harvesting circuit [81]. Figure 13 depicts the change of the PCE depending on the output loads for various power inputs [82].  
\begin{table}
\begin{tabular}{c c} \hline
**Rectifier’s Topology** & **References** \\ \hline Dual Band Rectifier & [35, 43, 69] \\ Single Diode Rectifier & [49, 70] \\ Voltage Multiplier & [50, 71, 73] \\ Greinacher Rectifier with Rat-Race coupler & [51, 52] \\ Voltage Douppler & [54, 56, 57, 58, 59, 60, 62, 64, 67, 68] \\ Voltage Quadrupler & [55] \\ Voltage Multiplier & [61, 63, 65] \\ Balanced RF Rectifier & [66] \\ Rectifier with RF Combiner & [72] \\ Reconfigurable System & [75, 76, 77, 29] \\ CMOS Rectifier & [37, 74, 80] \\ \hline \end{tabular}
\end{table}
Table 6: List of rectifiers based on their topology.  
Figure 13: The change of PCE depends on different output loads.
## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### GIS for Shoreline Change Analysis and Coastal Monitoring
Commonly, studies involving the DSA utilize EPR, LRR, and WLR metrics on Landsat or Sentinel-2 imagery, demonstrating different spatial patterns of change and supporting the versatility of GIS monitoring tools in different coastal environments.
## 2 Materials and Methods
### Transfer Matrix for Units in Series and Parallel  
We combine now in three different ways the single units described in the previous Section: in particular, we consider configurations in series (Figure 2a), in parallel (Figure 2b) and the special one described in Figure 2c. For simplicity, we indicate with:  
\[\mathbf{M}_{i}=\left[\begin{array}{cc}m_{11}^{(i)}&m_{12}^{(i)}\\ \\ m_{21}^{(i)}&m_{22}^{(i)}\end{array}\right],\qquad\qquad i=\alpha,\beta,\chi \tag{7}\]  
the single unit transfer matrix.  
For the systems shown in Figure 2a, with two units arranged in series, the global transfer matrix is simply obtained by multiplying single unit matrices [60], namely:  
\[\mathbf{M}_{S}=\mathbf{M}_{\beta}\,\mathbf{M}_{a}=\left[\begin{array}{cc}m_{ 11}^{(\alpha)}m_{11}^{(\beta)}+m_{21}^{(\alpha)}m_{12}^{(\beta)}&m_{12}^{( \alpha)}m_{11}^{(\beta)}+m_{22}^{(\alpha)}m_{12}^{(\beta)}\\ \\ m_{11}^{(\alpha)}m_{21}^{(\beta)}+m_{21}^{(\alpha)}m_{22}^{(\beta)}&m_{12}^{( \alpha)}m_{21}^{(\beta)}+m_{22}^{(\alpha)}m_{22}^{(\beta)}\end{array}\right]. \tag{8}\]  
Figure 1: Single units. (**a**) Continuous layer with shear modulus \(\mu\) and density \(\rho\). ( **b**) Discrete unit with mass \(m\) and stiffness \(\gamma\).  
If the single units \(\alpha\) and \(\beta\) are disposed in parallel as in Figure 2b, the corresponding matrix \(\mathbf{M}_{P}\) has components:  
\[m_{11}^{(P)} = \frac{m_{11}^{(a)}m_{12}^{(\beta)}+m_{12}^{(a)}m_{11}^{(\beta)}}{m_ {12}^{(a)}+m_{12}^{(\beta)}},\] \[m_{12}^{(P)} = \frac{m_{12}^{(a)}m_{12}^{(\beta)}}{m_{12}^{(a)}+m_{12}^{(\beta)}},\] \[m_{21}^{(P)} = \frac{(m_{12}^{(a)}+m_{12}^{(\beta)})(m_{21}^{(\beta)}+m_{21}^{( \beta)})-(m_{11}^{(a)}-m_{11}^{(\beta)})(m_{22}^{(\alpha)}-m_{22}^{(\beta)})}{ m_{12}^{(\alpha)}+m_{12}^{(\beta)}}\] \[= \frac{m_{12}^{(a)}m_{21}^{(\beta)}+m_{21}^{(\alpha)}\,m_{12}^{( \beta)}+m_{11}^{(\alpha)}\,m_{22}^{(\beta)}+m_{22}^{(\alpha)}\,m_{11}^{(\beta) }-2}{m_{12}^{(\alpha)}\,m_{12}^{(\beta)}},\] \[m_{22}^{(P)} = \frac{m_{22}^{(a)}m_{12}^{(\beta)}+m_{12}^{(\alpha)}m_{22}^{( \beta)}}{m_{12}^{(\alpha)}+m_{12}^{(\beta)}}. \tag{9}\]  
The transfer matrix for the structured system of Figure 2c can be computed by considering the three single units, \(\alpha\), \(\hat{\chi}\), \(\beta\), in series, i.e., \(\mathbf{M}_{T}=\mathbf{M}_{\beta}\,\hat{\mathbf{M}}_{\chi}\,\mathbf{M}_{\alpha}\), where \(\hat{\mathbf{M}}_{\chi}\) can be computed from the components of the transfer matrix \(\mathbf{M}_{\chi}\) of the unit \(\chi\) in the following manner:  
\[\hat{\mathbf{M}}_{\chi}=\left[\begin{array}{cc}1&0\\ \\ \frac{m_{21}^{(\chi)}}{m_{22}^{(\chi)}}&1\end{array}\right]. \tag{10}\]  
Needless to say, it is trivial to check that \(\mathbf{M}_{S}\), \(\mathbf{M}_{P}\) and \(\mathbf{M}_{T}\) are unimodular.
# Markov-Modulated Poisson Process Modeling for Machine-to-Machine Heterogeneous Traffic
## 1 Introduction
M2M traffic is highly homogeneous in comparison to H2H traffic since it uses small data chunks and transfer rates, typically with predictable communication periods and durations [8]. However, accumulative traffic from various sources is expected to be received due to M2M synchronization behavior and a variety of applications with varying payloads, times, and data rates. This turns homogeneous M2M traffic into heterogeneous M2M traffic, which quickly saturates the network bandwidth. Saturation is an issue that inevitably affects traffic, services, and applications in both M2M and H2H contexts. Recently, the complexity of processing algorithms in cellular systems has pushed the boundaries of existing technologies to the limit. According to Heavy Reading [9], mobile operators pay USD 20 billion annually to overcome service degradation and network breakdown. Therefore, the efficient radio communication strategy is one of the biggest issues faced by researchers, mobile operators, and the 3 rd Generation Partnership Project (3 GPP) community [10]. The main research gap is how to model heterogeneous M2M traffic. In this study, we bridge this gap by proposing a Continuous-Time Markov Chain (CTMC) model as a stochastic process tool to characterize the H2H/M2M coexistence while focusing on heterogeneous M2M traffic, not homogeneous M2M traffic. The main contributions of this paper are as follows:  
* We propose a Markov-Modulated Poisson Process (MMPP) framework called Coexistence of Heterogeneous traffic Analyzer and Network Architecture for Long-termevolution (CHANAL) to characterize Human-to-Human traffic and M2M heterogeneous traffic mathematically
* Using CHANAL, we mimic the real-time and synchronization behavior of M2M traffic. * We study the impact of heterogeneous M2M traffic over H2H traffic, especially during disaster scenarios. * To confirm our mathematical results, the CHANAL solution is illustrated using an extensive simulation for different scenarios. The rest of the paper is organized as follows: In Section 2, we give an overview about source traffic modeling and aggregated traffic modeling. The third section introduces the MMPP model. In Section 4, our CHANAL model is presented. In Section 5, we discuss our mathematical results, while Section 6 analyzes our empirical results. Finally, the conclusion and future works are provided in the last section. It is important to note that the performance of homogeneous M2M traffic and H2H traffic is characterized mathematically in our previous work [11]. In this work, we used a mathematical model called \"Coexistence Analyzer and Network Architecture for Long term evolution\" (CANAL) to characterize the key performance of homogeneous M2M traffic as well as H2H traffic. In this work, we are motivated to test the ability of CANAL to analyze heterogeneous M2M traffic. To answer this question, we consider traffic modeling in the following section.
## 4 Dynamic Placement of SFC Based on Meta Reinforcement Learning and Fuzzy Logic
### Fuzzy Logic  
In classical Boolean logic, \"false\" is usually denoted by 0, and \"true\" is denoted by 1; a proposition is either true or false. In fuzzy logic, a proposition is no longer either true or false; it can be thought of as \"partially true\". Fuzzy logic uses non-numeric linguistic variables to express facts and further deals with approximate data in a manner similar to human reasoning. It comprises three main procedures (see Figure 3): first, convert the input values to fuzzy values, i.e., fuzzification. The degree to which the bandwidth of a link is part of the large, medium, and small categories is calculated using the bandwidth membership function. Similarly, the degree to which the delay belongs to the large, medium, and small categories and the degree to which the packet loss rate belongs to the large, medium, and small categories need to be calculated using the corresponding membership function. Then, IF/THEN rules are used to obtain the fuzzy output values. Those fuzzy output values are defined as fire strength (FS) and cannot solve real problems. Therefore, defuzzification can be used to obtain certain numerical values and set a threshold (\(\chi\)) for evaluation of links.
## The Investigation Framework
### Framework Overview  
The investigation framework is composed of many modules. First of all, we give a high-level overview of them, describing the task performed by each single module.  
* **Module M1-Pre-processor.** The module named _Pre-processor_ performs many pre-processing activities on the data set, i.e., the set of labeled messages that constitutes the input data set for the investigation framework. Specifically, it performs tokenization, stop-word removal, special symbol elimination, and stemming (that are typical pre-processing techniques adopted in information retrieval). Specifically, stemming is important to reduce dimensionality of features: in fact, natural languages provide many different forms for the same word (for instance, singular and plural); stemming reduces words to the root form. The result of the _Pre-processor_ module is the corpus of messages, where each document is represented as an array of stems.
* **Module M2-Feature Extractor.** After the _Pre-processor_ is executed, the module named _Feature Extractor_ extracts features that represent messages. In this module, messages are translated into vectors of basic and combined feature patterns, as reported in Table 2. The \(Weight(t,c,C)\) score defined in Formula 1 is computed for each term (i.e., feature) belonging to the training set.
* **Module M3-Multi-Classifier.** The final module is called _Multi-Classifier_, because it has to apply all the four different classification techniques we discussed in Section 4.1.2. In fact, the goal of the investigation framework is to understand which is the best combination of feature pattern and classification technique (recall Figure 2). Thus, for each feature pattern, the module builds four classification models and tests them to evaluate the accuracy of classification.  
Figure 3 reports the organization of the framework, by illustrating data flows between and inside modules. They are discussed in details hereafter.  
The framework is implemented in the Python programming languages, by exploiting the libraries nltk for pre-processing, sklearn for feature extraction, generation of n-gram combinations, training and testing of classifiers.
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
## 3 Results and Discussion
### Chemical Characterization of Promising Microalgal Extracts' Fractions
These compounds ranged from 18 to 20 carbons in _Porphyridium_ sp., 17 to 20 carbons in _Tetraselmis striata_ CTP4 and 18 to 54 carbons _P. tricornutum_. The only cycloalkene compound was found in _Tetraselmis striata_ CTP4, which have reported anti-inflammatory, antimicrobial, antibiotic, antibacterial, antitumor and antiviral activity and is also being used in the development of new pharmaceuticals [81]. Minor amounts of phenolic compound such as alpha-tocopherol (vitamin E) and butylated hydroxytlouene (BHT) were also identified in the microalgal fractions and both are known to have antioxidant properties [82]. All the microalgal fractions had small amounts of ethers (mainly tetrahydropyrans), which have reported diverse biological and pharmaceutical properties including antimicrobial, antitumor and anti-proliferative properties and have the potential to be used in the treatment of neurodegenerative diseases such as Alzheimer's disease [83]. Sterols occur naturally in plants and algae, but sterol composition may vary widely between species. In most algae, campesterol, stigmasterrol, ergosterol and fucosterol are generally the most abundant sterols. In our case, brassicosterol was the main phytosterol in _Phaodactylum tricornutum_, chloresterol in _Skeletonema_ sp. and dehydrocholesterol in _Porphyridium_ sp.. Phytosterols have been shown to provide a variety of health benefits in humans, including immunomodulatory, anticancer, anti-inflammatory, and antioxidant properties [84]. The hexane fraction of _Tetraselmis striata_ CTP4 had octadecadienoic acid, which was previously reported to reduce the expression of cytokines and proinflammatory mediators and therefore having anti-inflammatory potential [85]. Despite the identified compounds having some reported antioxidant, metal-chelating, anti-tumoral, and anti-inflammatory activity, it is likely that other compounds, which could not be identified in this preliminary phytochemical analysis, are also responsible for the bioactivities of these extracts, as many other bioactive compounds (e.g., pigments, sphingolipids, glycerophospholipids, and glycerolipids) have been reported by other studies [66; 86].
## 4 Discussion  
Although the relationship of bite force to age and gender and facial type has been previously studied, the exact impact of facial height on bite force distribution has remained relatively unexplored [22]. This study sought to compare the distribution of occlusal forces among children with short, average, and long facial heights. The development of digital bite analysis software has allowed researchers to measure not only bite force, but also map the distribution of the bite and the impact this has on the time of the occlusal cycle [23]. The fact that bite pattern is a better indicator of function than only bite force is a concept that has been gaining traction in literature [17; 18]. The fact that there was no significant difference between males and females in the distribution of occlusal forces is in keeping with studies that show that despite gender differences in force, there is no difference in the pattern of distribution of forces. Similarly, the fact that there was no influence of age, body weight, or body height to the distribution of the bite is in keeping with studies that show that bite pattern may be independent of bite force [23; 24]. Studies have previously sought to measure the impact of facial height on bite force and maximum mouth opening [25; 26]. However, the impact of facial type on the distribution of the bite has remained relatively unexplored. The findings of the current study found that while there was no difference between children with a short facial height and average facial height, children with a long facial height had a significantly greater distribution of force in the posterior segments and significantly lower distribution of bite in the anterior segment. Despite this finding, there were no significant differences among the three facial types in the time of the occlusal cycle. This is important, as increases in the time of the occlusal cycle are suggestive of loss of balanced occlusion [18]. This would seem to suggest that although children in the late mixed dentition with an increased facial height have a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized**} & \multicolumn{2}{c}{**Standardized**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig.**} \\  & & **Coefficients** & & & \\ \cline{3-5}  & & **B** & **Std. Error** & & & \\ \hline \multirow{3}{*}{(Constant)} & \multirow{3}{*}{0.174} & \multirow{3}{*}{0.539} & \multirow{3}{*}{0.323} & \multirow{3}{*}{0.750} \\  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \cline{1-1} \cline{5-5}  & & & & & \\ \hline Age & \(-\)0.028 & 0.116 & \(-\)0.055 & \(-\)0.239 & 0.813 \\ \hline \hline \end{tabular} No significant associations with occlusal time.  
\end{table} 
Table 4: Regression model of factors influencing the occlusal time.