esa-sceva/satcom-chunk-collection huggingface dataset:
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 2 Related Work
### Eye Tracking in Research
By comparing all participants' records of performance measurements (i.e., response score, task completion time) with eye movement measures, the study indicates that eye movement data may be used in conjunction with performance measures to acquire a deeper knowledge of usability issues. Similarly, ref. [14] employs eye tracking to estimate user attentiveness and evaluate the usability of website pages. Eye tracking has also had success in lots of different areas of consumer research for a variety of reasons. In traditional research, a task may be carried out by the participant and then afterwards, they would be asked questions based on that task. For example, in a supermarket, a customer could be asked to carry out their daily shopping and then afterwards asked if they noticed certain labels, offers etc. and why those choices may have seemed appealing [23]. One of the problems within this area of research is that human memory is not faultless, and sometimes the customer will either forget, not notice or not think about why such a decision was made, which can make giving an answer to some of these types of questions difficult, or can provide false information at times [24; 25; 26]. The benefit of eye tracking software is that it has the ability to capture the gaze position of the participants' eyes, and this position will be recorded at different time intervals depending on the device. This removes the potential for any memory issues and therefore removes human error in the process. If the trajectories of the participants' eyes can be monitored at all times, then the reasons behind their choices become more apparent, whether because of colour, a bolder font, or a special offer price [27; 28; 29]. The purpose of the study at BTIIC was to evaluate TV customer experience by measuring participants' eye movements while using BT Player on a television [11]. The BT Player screen is very visual, showcasing available material via a combination of menu items, controls, and images (See Figure 1 below). The eye tracker was utilised to document the user's engagement with the interface. During the step of data extraction following the session with participants, the recorded screens for each task were separated into AOIs. Fixation occurs when an AOI is selected, but saccades occur between each AOI. A trajectory is the path of gaze travel between numerous AOIs. Understanding and constructing such a model of user behaviour helps facilitate prediction.
## 3 State of the Art
### Digital EPDs  
Digitalization of EPDs is a critical element for the environmental assessment of built assets. This research is based on the conviction that digital EPDs should be structured to ensure not only machine readability, but also machine interpretability, thereby reducing the resources required to perform the LCA by automating data interpretation and transfer. These tasks currently require the time-consuming analysis of experts.  
There are several digital formats available for transferring LCA data into BIM or other software tools. However, there are limitations that may hinder the use of EPDs due to the cost and resources required for human interpretation of the content (including hypothesis) or transferring the data into BIM. The lines of future research identified in [9] include the assessment of its application to particular formats, such as ISO 22057, ILCD+EPD or IFC.  
The results of [PERSON], [PERSON] et al. [10] include nineteen gaps applicable to ISO 22057 dEPDs, which have been analyzed in this research to assess their applicability to IFC.  
[PERSON] et al. [22] recommended several actions to improve the data quality of digital EPDs related to the results of this paper, in particular:  
* Harmonization among LCA data or EPD issuers and users (manufacturers, LCA practitioners, EPD programme operators, etc.).
* Standardization of EPD data generation and reporting, including traceability of environmental impact data.
* Interoperability between EPD databases and BIM.
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 3 Proposed Low-Complexity Multibeam Wideband Transmit Beamformer
### Minimax Design of the 2D Sparse FIR Filter
#### 3.3.1 Step 1
\begin{bmatrix}W(\omega_{1})H_{d}(e^{i \omega_{1}^{(1)}},e^{i\omega_{2}^{(1)}})\\ \vdots\\ W(\omega_{M})H_{d}(e^{i\omega_{1}^{(M)}},e^{i\omega_{2}^{(M)}})\\ -W(\omega_{1})H_{d}(e^{i\omega_{1}^{(1)}},e^{i\omega_{2}^{(1)}})\\ \vdots\\ -W(\omega_{M})H_{d}(e^{i\omega_{1}^{(M)}},e^{i\omega_{2}^{(M)}})\end{bmatrix}  \tag{22}\]The unique and globally optimal solution \(\mathbf{h}\) of the linear programming problem is an approximately sparse [52]. To obtain a sparse impulse response \(\mathbf{h}^{s}\), hard thresholding is employed, i.e.,  
\[h_{i}^{s}= \begin{cases}h_{i},&\text{if }|h_{i}|\geq\epsilon_{th}\\ 0,&\text{otherwise},\end{cases}  \tag{23}\]  
where \(\epsilon_{th}\) (\(\in[10^{-4},10^{-2}]\) typically [54]) is the threshold value.
## 3 Materials and Methods
### kNN Classification and Clustering in Dual Space  
Here, we consider points in dual space \(\mathcal{P}\). Given two dual points \(dp_{1}\) and \(dp_{2}\), we define as \(dist(dp_{1},dp_{2})\) the distance between \(dp_{1}\) and \(dp_{2}\) in \(\mathcal{P}\). In the context of this work, we utilize the Euclidean distance metric, which is defined as  
\[dist(dp_{1},dp_{2})=\sqrt{\sum_{i=1}^{p}\left(dp_{1}[i]-dp_{2}[i]\right)^{2}},\]  
where \(dp_{1}[i]\), \(dp_{2}[i]\) denote the values of \(dp_{1}\), \(dp_{2}\) along the \(i\) dimension in \(\mathcal{P}\). For example, in Hough-X space, the distance between the dual points \(dp_{1}=(u_{1},a_{1})\), \(dp_{2}=(u_{2},a_{2})\) is computed as \(dist(dp_{1},dp_{2})=\sqrt{(u_{1}-u_{2})^{2}+(a_{1}-a_{2})^{2}}\).  
**Definition 1**.: _DukNN: Given a dual point \(dp\), a data-set of dual points \(Y\) and an integer \(k\), the \(k\) nearest neighbors of \(dp\) from \(Y\), denoted as \(DukNN(dp,Y)\), is a set of \(k\) points from \(Y\) such that \(\forall l\in DukNN(dp,Y)\) and \(\forall q\in\{Y-DukNN(dp,Y)\}\), \(dist(l,dp)<dist(q,dp)\)._  
**Definition 2**.: _DukNN Classification: Given a dual point \(dp\), a training dual points data-set \(Y\), and a set of classes \(Cl_{Y}\) where the dual points of \(Y\) belong, the classification process produces a pair (\(dp\),\(cl_{dp}\)), where \(cl_{dp}\) is the majority class to which \(dp\) belongs._  
Figure 1: An overview of trajectory segmentation and Hough-X transformation for a linear trajectory segment (TS), which consists of M points. The dual points of M points in TS are the same, for example, \(a_{1}=\ldots=a_{M},u_{1}=\ldots=u_{M}\), where (**a**) shows the y(t) line and (**b**) shows the Hough-X points.  
**Definition 3**.: _Clustering: Given a finite data-set of dual points \(\mathcal{DP}=\{dp_{1},dp_{2},\ldots,dp_{N}\}\) in \(R^{p}\), and number of clusters \(K\), the clustering procedure produces \(K\) partitions of \(\mathcal{DP}\) such that among all \(K\) partitions (clusters) \(C_{1},C_{2},\ldots,C_{K}\) find one that minimizes \(\arg\min_{C_{1},C_{2},\ldots,C_{K}=\mathcal{P}}\sum_{c=1}^{K}\sum_{dp\in C_{c} }\left\|dp-\frac{1}{|C_{c}|}\sum_{dp_{j}\in C_{c}}dp_{j}\right\|^{2}\) where \(|C_{c}|\) is the number of dual points in cluster \(C_{c}\)._  
Note that the aforementioned dual methods act as a feature extraction technique. More specifically, they extract the dual point of each of the \(x,y\) coordinates of a mobile user trajectory. The \(k\) nearest neighbors algorithm is then applied on dual points features and allowed to return dual points, whose distance from the query dual point is less than the distance from the rest of the training dual points. Considering the Hough-X transformation of attribute \(x\) or \(y\), the search area is a circle with the center being the query point and a radius such that \(k\) nearest neighbors exist. If we assume Hough-X of \((x,y)\) attributes, the \(k\) nearest neighbor search area is four-dimensional \((u_{x},a_{x},u_{y},a_{y})\) with complex hypercube geometry.
## 2 Measurement and Roughening Methods
### Roughening Methods  
Different surface roughness levels are inevitably produced during the particle refinement and surface modification. This indicates that it is possible to regulate the surface roughness of minerals and materials by adopting various roughening methods and parameters. Numerous scholars have extensively investigated several roughening techniques, with the grinding method being the most commonly used. Other methods, such as acid etching, abrasion, sand-blasting, ultrasonic pretreatments, and microwave treatments, have also been explored. The results show that these methods effectively modify the surface roughness of various minerals/materials, including calcite ([PERSON] et al., 2016; [PERSON] et al., 2016), magnesite ([PERSON] et al., 2016; [PERSON] et al., 2016), quartz ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016), glass beads ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016), malachite ([PERSON] et al., 2016), graphite ([PERSON] et al., 2016), hematite ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016), magnetite ([PERSON] et al., 2016), coal particles ([PERSON] et al., 2016; [PERSON] et al., 2016), parite ([PERSON] et al., 2016), alumina particles ([PERSON] et al., 2016), sphalerite ([PERSON] et al., 2016),apatite [24], dolomite [24], talc [32; 33], spodumene [64; 65], pyrite [66; 67], and cassiterite [68]. The surface roughness varies significantly depending on the roughening methods and parameters employed.
###### Abstract  
Background: The prevalence of falls among the older population underscores the imperative of comprehending human adaptations to gait perturbations. Dual-belt treadmills offer a controlled setting for such investigations. The purpose of this study was to examine the effect of the acceleration of one belt of the treadmill during three different phases of the gait cycle on kinematic and kinetic parameters and relate these changes to unperturbed gait. Methods: Twenty-one healthy young females walked on a treadmill in a virtual environment, in which five unexpected perturbations were applied to the left belt at the Initial Contact (IC), Mid Stance (MS), and Pre-Swing (PS) phase of the gait cycle. Data from the undisturbed gait and the first disturbance of each trial were extracted for analysis. Results: All perturbations significantly affected the gait pattern, mainly by decreasing the knee extension angle. The perturbation in the IC phase had the most significant effect, resulting in a 248.48% increase in knee flexion torque. The perturbation in the MS phase mainly affected plantar flexion torque, increasing it by 118.18%, while perturbation in the PS phase primarily increased the hip extension torque by 73.02%. Conclusions: The presence of perturbations in the IC and PS phases caused the most aggressive and significant changes in gait parameters.  
perturbation; gait; treadmill; GRAIL; Motek; OpenSim +
Footnote †: journal: Applied Sciences
# _Legionella pneumophila_--Virulence Factors and the Possibility of Infection in Dental Practice
## 4 _Legionella pneumophila_ in Dental Practice
### Resistance of L. pneumophila Biofilms to Biocides  
_L. pneumophila_ poses a constant threat to human health in anthropogenic water sources [21]. Due to the intracellular lifestyle within protozoa, it is difficult to assess whether the resistance of _L. pneumophila_ in environmental biofilms is due to the structure of the biofilm, its association with amoebae, or both [120]. However, the fact is that _L. pneumophila_, which is found in biofilms, is highly resistant to the action of biocides [74].  
Numerous disinfection methods were used to limit the growth of _L. pneumophila_, but none succeeded in complete eradication; namely, recolonization occurred very soon after treatment [120; 121]. In addition, some studies showed that the biocide action on the _L. pneumophila_ biofilm could lead to the transition of the bacterium to the VBNC state [74]. The most common biocides used in _L. pneumophila_ water disinfection protocols are chlorine and chlorine derivatives. However, they show efficacy only on planktonic cells, but not on biofilm [122].  
Two reasons for this are the resistance of _L. pneumophila_ to disinfectants; one is due to its ability to survive within the biofilm, and the other is that it possesses an intra-amoebic lifestyle [29]. Namely, vesicles containing intracellular _L. pneumophila_ released by the amoeba are resistant to biocidal treatments. It is important to note that these vesicles remain viable for several months [74]. Chlorine dioxide, unlike chlorine, can penetrate the biofilm and can also inactivate free-living amoebae which _L. pneumophila_ inhabits. Therefore, it is concluded that chlorine dioxide can be used as a secondary disinfectant to reduce the risk of Legionnaires' disease in hospital systems [19].  
The use of phages in the treatment of biofilm infections is known in many bacterial pathogens [123], so the addition of specific phages can be used to control the growth of _L. pneumophila_. However, the phage can degrade polysaccharides and destabilize the biofilm [124].  
The antimicrobial activity of silver has long been known, and silver is an increasingly frequent target of research to find new antimicrobial agents [125]. When it comes to a significant reduction in the volume of _L. pneumophila_ biofilm, silver nanoparticles have shown outstanding results [126].  
Natural compounds that have demonstrated antimicrobial efficacy on _Legionella_ strains are antimicrobial peptides, biosurfactants, and essential oils [19]. Different filtration methods are possible, but as filters have a certain lifespan, this could significantly increase the cost of maintenance in the hospital system [127].
## 4 Experiments and Analysis
### Experiments
#### 4.4.2 Experiments with E3W and Four Sub-Models
From Table 7, it can be seen that E3W presented significant improvements in Avg Accuracy, Avg Precision, Avg Recall score, and Avg F1-score, by 0.15%, 0.14%, 0.15%, and 0.12%, respectively. The results of the ablation experiment fully illustrate the validity and correctness of the E3W model.
# Continuous Wavelet Transform and CNN for Fault Detection in a Helical Gearbox
## 4 Proposed Defect Detection Models  
To solve the proposed fault detection task as a four-class classification problem, we chose to utilize the 2D-CNN model [31] due to its ability to capture spatial and temporal patterns in an image through the application of relevant kernels. The abstract features extracted by a CNN from the 2D CWT-based representations (2D grayscale images) of the measured signals should distinguish the four states of the helical gearbox.
# Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering
## 1 Introduction
In this way, both the adaptivity and generality are achieved. An online learning-based intra-frame video coding approach is proposed to exploit the texture sparsity of natural images [11]. This is denoted as a compression method based on Dictionary Learning by Online Dictionary transmitting (DLOD). In this method, to synchronize the dictionary used in the coder and decoder, the residual between the current dictionary and the previous one is necessary for sending. This will increase the rates. In this paper, we focus on how to compress seismic signals with the nonparametric Bayesian dictionary learning method via clustering. Seismic signals are generated from a seismic wave and recorded by different sensors, and are highly correlated. Clustering is introduced for seismic signal compression based on dictionary learning. A set of dictionaries can be generated, and each dictionary is used for one cluster's sparse coding. In this way, the signals in one cluster can be well-represented by their corresponding dictionaries. The dictionaries are learned by the nonparametric Bayesian dictionary learning method, which naturally infers an appropriate dictionary size for each cluster. Furthermore, the online seismic signals are utilized to train the dictionaries. Thus, the correlation of training data and testing data could be relatively high. A uniform quantizer and an adaptive arithmetic coding algorithm are used to code the sparse coefficients. Experimental results demonstrate better rate-distortion performance over other seismic signal compression schemes, which validates the effectiveness of the proposed method. The rest of this paper is organized as follows: in Section 2, we introduce a seismic signal compression method based on offline dictionary learning. A seismic signal compression method using nonparametric bayesian dictionary learning via clustering is introduced in Section 3. Experimental results are presented in Section 4, and conclusion is made in Section 5.
## 4 Activation Methods of RP
### \(Co_{2}\) Activation
#### 4.2.2 Indirect Carbonation Process  
Indirect carbonation, which includes multiple steps, often utilizes acidic and basic substances to dissolve RP in order to extract calcium. The dissolved calcium ions are then exposed to a solution containing CO\({}_{2}\) or a compound that releases carbonate ions to cause the precipitation of CaCO\({}_{3}\)[259; 260; 262]. If necessary, additional procedures such as impurity elimination and the recycling of waste solutions can be incorporated. The dissolution of RP often regulates the rate of the reaction due to the crucial role of calcium availability in the carbonation process [260; 262]. Distilled water [274], HNO\({}_{3}\)[275], CH\({}_{3}\)COOH [276], HCl [276; 277], NH\({}_{4}\)Cl solution [276], or NH\({}_{4}\)OH solution [278] are typically used to dissolve RP.  
Some researchers proposed bipolar membrane electrodialysis (BMED) to treat the waste solution [260; 277]. A bipolar membrane is a composite membrane consisting of an anion-exchange membrane and a cation-exchange membrane. A bipolar membrane has the ability to generate H\({}^{+}\) and OH\({}^{-}\) ions by allowing water to pass through the anion- and cation-exchange membranes. This feature can be utilized in electrodialysis to produce BMED. Electrodialysis is a method of separating substances using a special membrane that has varying electrical potentials. OH\({}^{-}\) and H\({}^{+}\) can be formed through the use of a bipolar membrane, allowing for the regeneration of acid and alkaline solutions [260; 279; 280].  
It should be noted that compared to the indirect method, direct carbonation has several key benefits, including reduced costs and environmental consequences due to its straightforward nature and decreased use of chemicals compared to indirect carbonation [259; 281].
# Hybrid TOA/RSS Range-Based Localization with Self-Calibration in Asynchronous Wireless Networks
## 2 Hybrid TOA/RSS Localization: Formulation and Resolution Approaches
### Joint Maximum Likelihood Position Estimation  
Let \(\mathbf{r}_{b}=[r_{1,b}\cdots r_{N,b}]^{T}\) and \(\mathbf{t}_{b}=[t_{1,b}\cdots t_{N,b}]^{T}\) denote the RSS and TOA observation vectors collected at the blind node exploiting the signals from the \(N\) anchors. For the sake of notation, we can arrange the set of unknown RSS model parameters as:  
\[\mathbf{\beta}=[P_{0}\ \ \alpha\ \sigma_{r}^{2}]^{T}, \tag{5}\]  
and, similarly, we can stack the unknown TOA parameters as:  
\[\mathbf{\theta}=[t_{1}\cdots t_{N}\ \ t_{b}\ \ \sigma_{t}^{2}]^{T}. \tag{6}\]  
Using Equation (2), (4), the joint TOA/RSS likelihood function can be written as:  
\[\Lambda(\mathbf{r}_{b},\mathbf{t}_{b}|\mathbf{x}_{b},\mathbf{\beta},\mathbf{\theta}) =\prod_{i=1}^{N}f_{r}(r_{i,b}|\mathbf{x}_{b},\mathbf{\beta})f_{t}(t_{i,b}| \mathbf{x}_{b},t_{i},t_{b},\sigma_{t}^{2})\] \[=\frac{1}{(2\pi\sigma_{r}\sigma_{t})^{N}}\mathrm{e}^{-\frac{1}{2 \sigma_{r}^{2}\sigma_{t}^{2}}\sum_{i=1}^{N}\left[(r_{i,b}-P_{0}+10\alpha\log_{ 10}\|\mathbf{x}_{i}-\mathbf{x}_{b}\|)^{2}\sigma_{t}^{2}+(t_{i,b}-\frac{\|\mathbf{x}_{i}- \mathbf{x}_{b}\|}{c}-t_{b}+t_{i})^{2}\sigma_{t}^{2}\right]}, \tag{7}\]  
where \(f_{r}(\,\cdot\,)\) and \(f_{t}(\,\cdot\,)\) denote the pdfs of the RSS and TOA measurements, respectively. It is worth noting that Equation (7) represents the actual likelihood under the assumption that both RSS and TOA information are measured from independent sources. Indeed, the experimental campaigns conducted in [52; 53] clearly show that measurements extracted from the same signal are weakly correlated; hence, assuming independence in Equation (7) is not detrimental to generality.  
The hybrid TOA/RSS ML estimation problem can be written as:  
\[\left\{\hat{\mathbf{x}}_{b},\hat{\mathbf{\beta}},\hat{\mathbf{\theta}}\right\}=\underset{ \mathbf{x}_{b},\mathbf{\beta},\mathbf{\theta}}{\arg\max}\ \Lambda(\mathbf{r}_{b},\mathbf{t}_{b}|\mathbf{x}_{b},\mathbf{\beta},\mathbf{\theta})=\underset{ \mathbf{x}_{b},\mathbf{\beta},\mathbf{\theta}}{\arg\min}\ L(\mathbf{r}_{b},\mathbf{t}_{b}|\mathbf{x}_{ b},\mathbf{\beta},\mathbf{\theta}), \tag{8}\]  
with \(L(\,\cdot\,)\) an equivalent transformation of the negative log-likelihood, i.e.,  
\[L(\mathbf{r}_{b},\mathbf{t}_{b}|\mathbf{x}_{b},\mathbf{\beta},\mathbf{\theta})=\sum_{i=1}^{N} \left[(r_{i,b}-P_{0}+10\alpha\log_{10}\|\mathbf{x}_{i}-\mathbf{x}_{b}\|)^{2}\sigma_{t }^{2}+(t_{i,b}-\frac{\|\mathbf{x}_{i}-\mathbf{x}_{b}\|}{c}-t_{b}+t_{i})^{2}\sigma_{r}^ {2}\right], \tag{9}\]  
which depends on the desired blind node position \(\mathbf{x}_{b}\), but also on all the nuisance parameters related to the measurement models in Equation (1), (3), respectively.