esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Optical Channel Model  
In this work, the combined effect of AT and PE is studied for performance evaluation. The PDF of an FSO channel is modeled as  
\[h_{s}=h_{a,s}h_{p,s}, \tag{5}\]  
where \(h_{s},\ s\in\{u_{i},j\}\) defines the channel fading coefficients for an authentic user and jammer, \(h_{a,s}\) is the attenuation due to AT, and \(h_{p,s}\) is the attenuation due to PE. The jamming channel is considered to be optical as well, since we have considered the jammer to be an optical transmitter which aims to disrupt an authorized FSO communication. The saturated AT is defined by negative exponential statistics, i.e., the PDF of \(h_{a,s}\) is [2]  
\[f_{h_{a,s}}(h_{a,s})=\frac{1}{\lambda}\text{exp}(-\frac{h_{a,s}}{\lambda}). \tag{6}\]  
Here, \(\lambda=\mathbb{E}[h_{a,s}]=1\), and \(\mathbb{E}[\cdot]\) is the expectation operator. The PDF of \(h_{p,s}\) is given by [30]  
\[f_{h_{p,s}}(h_{p,s})=\frac{\zeta_{s}^{2}}{A_{0}^{\zeta_{s}^{2}}}h_{p,s}^{\zeta _{s}^{2}-1},\quad 0\leq h_{p,s}\leq A_{0}, \tag{7}\]where \(A_{0}=[\text{erf}(v)]^{2}\), \(v=\sqrt{\pi/2}a/w_{z}\), \(\zeta_{s}=w_{zeq}/2\sigma_{s}\), \(a\) represents the radius of the detection aperture, \(w_{z}\) denotes the beam-waist (radius calculated at \(e^{-2}\)), \(w_{zeq}^{2}=w_{z}^{2}\sqrt{\pi}\text{erf}(v)\)\(/[2\text{exp}(-v^{2})]\) is the equivalent beam-width at the receiver, \(\sigma_{s}\) is the pointing error displacement standard deviation (jitter) at the receiver, and \(\text{erf}(\cdot)\) is the error function [31]. Since \(\zeta_{s}\), which characterizes the PE, is inversely proportional to \(\sigma_{s}\), the smaller value of \(\zeta_{s}\) would symbolize greater PE severity. Now, the combined PDF of \(h_{s}\) can be calculated by  
\[f_{h_{s}}(h_{s})=\int f_{h_{s}|h_{a,s}}(h_{s}|h_{a,s})f_{h_{a,s}}(h_{a,s})dh_{ a,s}=\frac{\zeta_{s}^{2}}{A_{0}^{\zeta_{s}^{2}}}h_{s}^{\zeta_{s}^{2}-1}\Gamma \bigg{(}1-\zeta_{s}^{2},\frac{h_{s}}{A_{0}}\bigg{)}, \tag{8}\]  
where \(\Gamma(\cdot)\) is the incomplete gamma function [32]. In the considered FSO system, the jammer needs to transmit an optical signal to interrupt the regular communication; hence, the jamming channel is also regarded as optical.
###### Abstract  
Variational Mode Decomposition (VMD) provides a robust and feasible scheme for the analysis of mechanical non-stationary signals based on the variational principle, but this method still has no adaptability, which greatly limits the application of this method in bearing fault diagnosis. To solve this problem effectively, this paper proposes a novel fluctuation entropy (FE) guided-VMD method based on the essential characteristics of fault impulse signals. The FE reported in this paper not only considers the order of amplitude values but also considers the variation of amplitude, and hence it can comprehensively characterize the transient and fluctuation characteristics of rolling bearing fault impulse signal. On the basis of establishing FE, the FE-based fitness functions are then conducted, after which the mode number and balance parameter can be adaptively determined. Meanwhile, an adaptive neighborhood statistical model is developed to further reduce the noise of the mode component containing fault information so as to highlight the periodic impulse component more significantly and improve the diagnostic accuracy. Simulation and case analysis show that this research is effective and quite accurate in fault mode separation and fault feature enhancement. Compared with the traditional VMD method and the current common diagnosis methods, the proposed method has obvious advantages in the comprehensive utilization of fault impulse information and enhanced diagnosis.  
variational mode decomposition; fluctuation entropy; neighborhood statistical model; rolling bearing; fault diagnosis
## 4 Proposed Approach and Experiments
### Experiment 3: Enhancing Node Embedding Using GAT with Node2 Vec and GraphSAGE  
In Experiment 3, GAT enhances the initial embeddings generated by Node2 Vec and GraphSAGE with attention mechanisms. The initial embeddings understand the structural relationships of the nodes but may lack context information. GAT contextually enriches the embeddings by computing the contributions of neighboring nodes with attention weights. For example, in cases where an API call indicates malware behavior, GAT can assign a higher weight to this node. This process is achieved by directly feeding the embeddings from node2 vec/GraphSAGE into the GAT layers. As a result, the resulting attention-enhanced embeddings become more meaningful and suitable for classification. This experiment evaluates the embedding improvement ability of GAT and the performance of these embeddings when used with more complex classifiers. The embedding \(h_{v}\) for each node is generated with Node2 Vec or GraphSAGE. GAT enhances these embeddings with information from neighboring nodes:  
\[Z_{v}=\sigma\left(\sum_{u\in N(v)}\alpha_{xu}W(h_{u})\right) \tag{16}\]  
For each neighbor \(u\) of node \(v\), the neighbor's feature vector \(h_{u}\) is first multiplied by the weight matrix (W). This transforms the node's features to account for their influence on node \(v\). This transformed feature vector \(W(h_{u})\) is then multiplied by the attention coefficient \(\alpha_{xu}\). This expresses how much the neighbor node contributes to node \(v\) relative to its importance. The contributions of all neighbors of node \(v\) are summed N(v). This process allows node v to contextually obtain information from its neighbors and incorporate it into its representation. After the addition process, the resulting total value is inserted into an activation function \(\sigma\).
## 3 Experimental Setup
### Instrumentation  
The characteristics of the main employed instruments necessary to recover the hydraulic performances of the testing model along with the testing conditions are provided in Table 1. On the one hand, the test rig is equipped with an electromagnetic flowmeter for the discharge Q, two differential pressure transducers for the head H and the setting level H\({}_{\text{S}}\) respectively and four additional capacitive absolute pressure transducers for the static pressure at the wall M\({}_{1,2,3,A}\). The latest are connected through wall static pressure collectors, as may be seen in Figure 4. A PT100 transducer for the water temperature T and three optical tachometers for the rotational speed of the recirculation pumps Np\({}_{1,2,3}\) come to complete the list of instruments.  
On the other hand, the axial microturbine is equipped with two incremental encoders and two torque meters, one for each counter-rotating runner, necessary for the driving of the electrical generators and for the computation of the mechanical power. Finally, an incremental encoder and a high-precision electrical multimeter are used to drive the PAT's electrical generator and to compute the electrical power.  
Regarding the measurement of quantities related to the test rig (including the hydraulic power), a National Instruments (NI) CompactRIO 9074 autonomous digitizer, equipped with various acquisition/control modules, is used. A NI cDAQ 9174 digitizer is then employed to drive the testing model and to record the values of the rotational speed of the runners, of the mechanical torque of the runners (microturbine) and of the electrical power of the generator (PAT).  
In the case of classical static point-by-point method, both digitizers perform measurements and compute the average and the standard deviation values for all parameters over 8 s at 50 Hz (user-configurable, depending on the stability of the operating condition). In the case of dynamic measurements, a second NI cDAQ-9174 digitizer has been installed to acquire synchronized dynamic signals of the sensors in parallel with the existing acquisition/control systems of the test rig and of the testing model. Its acquisition frequency has been set to 100 Hz, considered fast enough to cope with the frequency response of the employed sensors.
# Blind Compensation of \(I/q\) Impairments in Wireless Transceivers
## 6 Conclusions  
In this paper, the issue of transmitter and receiver \(I/Q\) imbalances has been addressed. Using statistical properties of the communication signals, an empirical PDF-based blind estimation method has been proposed to mitigate the effects of frequency independent \(I/Q\) imbalance in wireless transceivers.  
A PDF of the impaired signal was estimated at the receiver, and the accuracy of this function was evaluated. Once the derived PDF was found to be accurate, the imbalance parameters were calculated by maximizing the PDF in terms of these parameters, resulting in four nonlinear equations. A nonlinear optimization technique was used to solve for the imbalance parameters, and the performance was evaluated.  
Simulation results show that the proposed model resulted in reduced error, reasonable image suppression, and a very low BER, validating the modeling and estimation capability of the proposed method. The proposed model was also tested in a real-world experimental setup and performed reasonably well, meeting the desired mitigation performance.  
The authors would like to thank the iRadio Lab and its sponsors Alberta Innovates Technology Futures, Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Program.  
[PERSON] devised the mitigation methodology and conducted simulations and measurements to validate the performance of the proposed approach. [PERSON] and [PERSON] helped with the technical discussions and preparation of the manuscript.  
The authors declare no conflict of interest.
## 2 Mathematical Modelling of Investigated Power System
### Design of the Foipid Controller  
The dynamics model for a general nonlinear system can be expressed as [4]:  
\[y^{(n)}=\varepsilon(t)+\tilde{\alpha}\cdot u_{i}(t) \tag{16}\]  
where \(n\geq 1\), \(\tilde{\alpha}\) is the input gain, and \(\varepsilon(t)\) denotes the unknown disturbances. If \(n=2\), and \(\varepsilon(t)\), \(\tilde{\alpha}\) are identified items, then the FOPID technique can be suggested as in Equation (17).  
\[u_{FOiPID}(t)=\frac{1}{\tilde{\alpha}}\left(-\varepsilon(t)+\dot{y}^{*}+K_{p} \varepsilon_{i}(t)+K_{i}\frac{d^{-\lambda}\varepsilon_{i}(t)}{dt^{-\lambda}}+ K_{d}\frac{d^{\mu}\varepsilon_{i}(t)}{dt^{\mu}}\right) \tag{17}\]  
Therefore, the steady error of the plant can be attained by adjusting the factors \(K_{p}\), \(K_{i}\), \(K_{d}\) and \(\lambda\) and \(\mu\). The estimated value of the \(\hat{\varepsilon}(t)\) can be obtained by employing the ESO approach [23]. The ESO has more advantages in a control system such as those used to estimate the internal and external uncertainties, which can be considered as:  
\[\left\{\begin{array}{l}e_{1}=z_{1}-\Delta f\\ \dot{z}_{1}=z_{2}-\beta_{1}fal(\
u,\rho_{1},\sigma)\\ \dot{z}_{2}=z_{3}-\beta_{2}fal(\
u,\rho_{2},\sigma)+b\cdot u_{FOIPID}\\ \dot{z}_{3}=-\beta_{3}fal(\
u,\rho_{3},\sigma)\\ \dot{\varepsilon}(t)=z_{3}\end{array}\right. \tag{18}\]  
where \(z_{1}\), \(z_{2}\) and \(z_{3}\) represent the state of ESO; \(\beta_{1}\), \(\beta_{2}\) and \(\beta_{3}\) represent the observer gains, \(e_{1}\) is the observer error.  
\[fal(\
u,\rho,\sigma)=\left\{\begin{array}{l}|\
u|^{p}\,\text{sign}(\
u)\quad,|\
u|>\sigma\\ \
u/\sigma^{1-\rho}\quad\quad\quad,\text{otherwise}\end{array}\right.\quad 0 \leq\rho\leq 1,\ \sigma>0 \tag{19}\]
# Cluster-Based Knowledge Graph and Entity-Relation Representation on Tourism Economical Sentiments
## 3 Methodology
### Dataset Collection  
Using tweepy [25], an open-source Python [26] module, a total of 100,000 tweets were gathered and mined over the course of four months. Tweepy facilitates access to the Python-based Twitter APIs [27], which employ consumer keys and private access tokens for authentication. The datetime library was used to include a custom script that was created to precisely extract 150 tweets every day and kept in a python list. When the search term \"tourist\" was used, all of the tweets containing related terms like \"economy\" and \"tourism\" were grouped together. A CSV file containing the tweets from 9061 different individuals was collected. An example of the dataset, which includes the characteristics Index, Datetime, and Tweets, is displayed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Date/Time** & **Date Accessed** & **Tweet** \\ \hline
31 December 2019 & 11 May 2022 & India’s tourism industry was the only promising aspect of economy, now that too has got a severe hit with Kashmir shut down in August and the CAA effect on Eastern India and other parts of India. What’s happening to our Country’s economy! \\ \hline
31 December 2019 & 11 May 2022 & [[https://t.co/GDK6](https://t.co/GDK6) faOrW3]([https://t.co/GDK6](https://t.co/GDK6) faOrW3) (Tourism becomes Taiwan’s economy-Does the U.S. know that strengthening Taiwan tourism and immigration is a priority) \\ \hline
31 December 2019 & 11 May 2022 & @[PERSON] @[PERSON] @OryxMaps @ElectionMapsCo @politicsluo @LoganZT1 If New England is weird, then VT is super-weird. It too has a lot of WWC voters and not too many educated suburbanites. But its tourism-based economy (and especially its skiing, with the associated concerns about climate change) will keep it solidly in the blue column. 3/? \\ \hline
31 December 2019 & 11 May 2022 & @SportingTruth. @DanMacPherson You know the bulk of the estimated $100 million has already entered the economy through tourism (and flow on), right? \\ \hline
31 December 2019 & 11 May 2022 & Expanded air services to \#SanJuan expected to add \$124m to PR economy in 2020 \#Tourism [[https://t.co/5](https://t.co/5) XSFsLfTxV]([https://t.co/5](https://t.co/5) XSFsLfTxV) [[https://t.co/NYXtuMOeE7](https://t.co/NYXtuMOeE7)]([https://t.co/NYXtuMOeE7](https://t.co/NYXtuMOeE7)) \\ \hline
31 December 2019 & 11 May 2022 & @mumbaiidise @AUhackeray It’s a very good idea to boost economy in 4\(\times\) speed. Create more jobs, reduce crimes \&amp; also tourism \\ \hline
31 December 2019 & 11 May 2022 & @travelfish @[PERSON] I also found it to be an annoying/useful look at global tourism-for the exact same reasons as you. [PERSON] is good at pinpointing problems, but her fixation with solving things by embracing high-end tourism reveals a lack of familiarity with the nuances of the travel economy.  
\end{table}
Table 1: Dataset Overview.  
Figure 1: Methodology Overview.
## 4 Arsenic Removal Processes
### Membrane Processes  
[PERSON] et al. [108] have recently reviewed the application of nanofiltration membrane technologies and their advantages and disadvantages relative to other methods. The membrane processes used for arsenic removal mainly include reverse osmosis [94; 95] and electrodialysis [96]. While these processes are effective, they are generally more costly. Nonetheless, they are capable of removing arsenic through filtration, electric repulsion, and adsorption of arsenic-bearing compounds. The size exclusion property of a chosen membrane enables rejection of arsenic compounds larger than its pore size [107]. Besides the size of the compounds, the rejection by the membrane can be influenced by other factors. In some instances, arsenic compounds that are 1-2 orders of magnitude smaller than the membrane pore size have been rejected, thus suggesting that besides physical straining, other removal mechanisms may be involved [107]. Two factors that play significant roles in arsenic rejection by a membrane are shape and chemical characteristics of the arsenic compounds. Removal of arsenic compounds on membranes can also be accomplished through repulsion or surface adsorption depending on the charge and hydrophobicity of the membrane and the feed water [109]. The achievement of removal efficiency of 97% for As(V) and 92% for As(III) in a single pass by reverse osmosis has been reported [107].
## 3 Results and Discussion
### Effect of Molecular Distillation Parameters on the Extent of Soybean Oil Oxidation  
Hydroperoxides are the most important intermediates of unsaturated fatty acid oxidation and the main products of primary oxidation. These species are very unstable and are readily transformed to secondary oxidation products at high temperature, which increases decomposition efficiency. Therefore, PV can be viewed as an indicator of oil oxidative stability [28]. Figure 2 shows the effects of distillation parameters on the peroxide and \(p\)-anisidine values of soybean oil. All characterizations were carried out according to the recommended AOCS standard scheme for oil quality assessment. PV decreased with increasing evaporation temperature and scraper speed. For example, as the evaporation temperature and scraper speed increased from 120 to 200 \({}^{\circ}\)C and from 180 to 260 rpm, respectively, PV decreased from 2.71 to 1.99 meqO2/kg (Figure 2A) and 3.81 to 2.09 meqO2/kg (Figure 2B), respectively. The initial reduction of PV was attributed to the decomposition of the thermally unstable lipid hydroperoxide, which is the initial oxidation product of oil [29]. Since low-molecular-weight secondary oxides are distilled as the light phase, the p-anisidine value of soybean oil behaved similarly to PV as the evaporation temperature increased from 120 to 180 \({}^{\circ}\)C, whereas an increase of \(p\)-anisidine value was observed as the evaporation temperature further increased from 180 to 200 \({}^{\circ}\)C. This behavior was similar to that previously reported for other vegetable oils and was ascribed to the degradation of traces of natural bioactive substances, which generated some small molecules including aldehyde, ketone, acid, such as 2-alkenal, 2,4-ahelan, and hence increased the \(p\)-anisidine value [16]. Since high distillation temperatures are believed to facilitate the loss of natural micronutrients from soybean oil, the distillation temperature of 180 \({}^{\circ}\)C was concluded to be suitable for practical application. Figure 2B shows that both PV and \(p\)-anisidine value rapidly decreased as the scraper speed increased from 180 to 260 rpm. Low scraper speeds precluded the formation of a uniform liquid film on the evaporation surface, decreasing the efficiency of mass transfer and reducing separation efficiency. As the scraper speed increased, so did the centrifugal force, which brought the wiper closer to the evaporation surface and increased its effect on the liquid film. Consequently, the thickness of the liquid film decreased and became more uniform, which increased the efficiency of mass/heat transfer. At a certain speed, distribution uniformity became saturated, which resulted in a slow PV decrease. Figure 2C shows that both PV and \(p\)-anisidine value increased with increasing feed flow rate. At low feed flow rates, materials resided on the evaporator surface for a longer time, which resulted in
# Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
## 4 Network Planning and Optimisation
### Ray Tracing Algorithm  
To improve the accuracy of the links between the nodes, a multi-bouncing ray tracing method is implemented. The following algorithm resides in creating possible links in Step 3 of Figure 5. The proposed algorithm is divided into three sub-steps and presented in Figure 6, where a set of rays launched, and interception nodes are presented.  
* Ray Launching: Each ray is launched every \(\theta\) degrees starting for the azimuth of the transmitter antenna and rotating either clockwise or anti-clockwise depending on the location of the receiver node. In these simulations, the value of \(\theta\) is 10 degree.
* Ray tracing: For each ray, two reflections are accounted. Each ray is a construction of consecutive points every meter until the maximum distance is reached, maximum PL is achieved, and the maximum number of rebounds are counted or until a receiver node is reached. For bouncing rays, each reflection is calculated based on the angle of arrival of the ray and the angle of the bouncing wall.
* Ray interception: As each ray is composed of points, each point evaluated is a receiver node within a distance of \(r=d*\tan(\frac{\theta}{2})\). If a node is within that radius, a possible link is created for the pair of Tx-RX. Instead, if after 250 points (250 m) an Rx is not intercepted, the next ray is evaluated until the whole antenna aperture is scanned.
## 3 Results
### The Post-Treatment Assessment and Comparison to Healthy Controls
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Myoton PRO Parameters**} & \multicolumn{2}{c}{**Parkinson’s Disease**} & \multirow{2}{*}{**Control Group (N = 42)**} & \multirow{2}{*}{\(p\)**} \\  & **Patients (N = 49)** & & \\ \hline Frequency (Hz), mean (SD) & 16.46 (4.36) & 15.14 (3.64) & **0.001** \\ Stiffness (N/m), mean (SD) & 393.16 (7.48) & 302.03 (88.06) & **0.02** \\ Decrement, mean (SD) & 1.92 (0.59) & 1.67 (0.49) & **0.001** \\ Relaxation (ms), mean (SD) & 20.53 (7.65) & 21.49 (6.78) & 0.26 \\ Creep, mean (SD) & 1.30 (0.46) & 1.34 (0.41) & 0.35 \\ \hline \hline \end{tabular}  
N: number of patients; SD: standard deviation; Bold values: \(p<0.05\) indicates statistical significance.  
\end{table} 
Table 5: Myotonometer parameters of femoral biceps in Parkinson’s disease patients (pre drug administration) and the control group (first measurement).
# Enhancing Tactile Internet Reliability: AI-Driven Resilience in NG-EPON Networks
## 2 Related Work
Analyzing these traces may be very challenging using conventional methods, especially to distinguish subscribers unambiguously [30]. It can be very time-consuming, since the engineer needs to remove the noise manually, which can increase the MTTR and reduce the detection and localization accuracy. One of the strategies to effectively manage and interpret the OTDR traces is for network operators to use baseline measurements, saving the measurements when the network is functioning normally. In this way, network operators create a reference point for future comparisons if faults occur in the ODN. Moreover, maintaining and organizing a database of reference points for all OTDR traces can help with quick retrieval and analysis during troubleshooting. Additionally, network operators must ensure that the network engineers are well-trained in interpreting OTDR traces and using the tools by conducting regular training sessions to stay updated. All these combined techniques still depend on the network engineers. Furthermore, before any fault occurs in the ODN, some anomalies can also appear in the network condition. Network operators can use various visualization tools such as a Bit Error Rate (BER) analyzer, Optical Time Domain Visualizer, and Optical Spectrum Analyzer. These tools can show the performance of optical signal delivery. An eye diagram is used to measure the signal quality. Ideally, an eye diagram would consist of two parallel lines with instantaneous rise and fall times, making them virtually invisible. The eye diagram can show vital parameters such as timing jitter and inter-symbol interference [31]. Combining both OTDR trace analysis and the eye diagram can improve the early detection of faults in the ODN. Consequently, in this paper, we propose automatic detection and localization using an ML algorithm by incorporating OTDR trace analysis data and eye diagram analyzer data. By incorporating ML algorithms, we can improve the accuracy and efficiency of detecting and localizing fiber faults. ML can process vast amounts of data, identifying patterns and detecting anomalies much faster with greater precision than network engineers. By leveraging ML, network operators can reduce their reliance on network engineers for fault  
Figure 1: Example of OTDR traces. detection and localization, leading to quicker resolutions and increased network reliability (as illustrated in the proposed Smart Resilience Architecture in \"Figure 4\").