esa-sceva/satcom-chunk-collection huggingface dataset:
# Focus Estimation Methods for Use in Industrial SFF Imaging Systems
## 2 Materials and Methods
### Interference covered by the Studies  
This study considered the effect of interference generated through environmental factors and the choice of image acquisition parameters. In terms of interference, the following were considered: illumination, surface texture, and contrast.  
The impact of illumination on the quality of the resulting height map represents essential information for implementing SFF imaging in measurement systems. In this study, the impact of interference in the form of lighting was determined by changing the camera-illuminator geometry. The performance comparison involved different industrial illuminators: dome light (DL) (Effilux IDS4-00-150-2-W-24V), bar light (BR) (IFM O2D926), direct ring light (DRL) (VS Technology VL-LRD153180 RGB), horizontal ring light (HRL) (VS Technology VL-HR110146W) and collimated light (CL) (CCS LFX100 RD). Due to the varying amount of light reaching the camera sensor depending on the used geometry and the power of the illuminator, it was necessary to normalise the average intensity on the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Lens Parameter** & **Value** \\ \hline Focal length & 50 mm \\ Aperture & F\#2.4 \\ Minimum object distance & 200 mm \\ Max. sensor size & 1.1\({}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of fixed focal length lens parameters.  
Figure 4: Optical representation of focusing mechanism in liquid lens technology: (**a**) red arrow in focus, (**b**) green arrow in focus.  
digital image. Normalisation was carried out by controlling the exposure time parameter at a preset average intensity of 128.  
The texture impact on the quality of the resulting height map provides information about the effect of the object itself on the quality of the height map. This information is crucial when choosing an imaging technology for a specific measurement task. This publication considers three materials: a metal surface, a plastic surface, and a wooden surface (Figure 3).  
Another selected interference whose impact will be considered is the contrast in the source images. The designer has some influence over factors determining contrast in a digital image, such as the choice of illuminator or image acquisition parameters. However, despite proper selection, it is possible to obtain images with little contrast, if only due to physical limitations or properties of the object being imaged. In the study, the change in contrast in the source images was achieved by changing the sensor exposure time. Decreasing the exposure time resulted in decreasing contrast in the digital image.  
This study narrowed a range of imaged elements to flat surfaces. This approach aims to determine the effectiveness of the focus estimation operators for the specified conditions without concentrating on errors introduced by other scene reconstruction steps [28].
## 2 Model and Method
### Model of DES Optimization
#### 2.4.2 Constraint Condition of DES  
(1) Energy balance condition  
The energy balance condition refers to the energy provided by each equipment in the system under the specified operational strategy required to meet the needs of cooling, heating, and power.  
\[E_{PG.i}+E_{gt.i}-E_{er,i}\geq E_{i} \tag{4}\]  
\[C_{er.i}+C_{ar.i}\geq C_{i} \tag{5}\]  
\[H_{gt.i}\cdot\eta_{he}+H_{gb.i}\geq H_{i} \tag{6}\]  
where \(E_{PG.i}\) is the electricity supply of the power grid at time \(i\); \(E_{gt.i}\) is the power generation of the gas turbine at time \(i\); \(E_{er.i}\) is the power consumption of the electric refrigeration unit at time \(i\); \(C_{er.i}\) and \(C_{ar.i}\) are the refrigerating output of the electric refrigeration unit and the absorption refrigeration unit, respectively, at time \(i\); \(H_{gt.i}\) and \(H_{gb.i}\) are the heating output of the gas turbine and the gas-fired boiler, respectively, at time \(i\); and \(\eta_{he}\) is the efficiency of the heat exchanger.  
(2) Equipment capacity constraints  
The gas turbine is selected as the power generation unit of the optimization model. The capacities of the gas turbine \(V_{gt}\), the gas-fired boiler \(V_{gb}\), and the electric refrigeration unit \(V_{er}\) are the variables of algorithm optimization. The capacity of the gas turbine determines the capacity of absorption refrigeration unit \(V_{ar}\) and the heat recovery steam generator \(V_{hrsg}\).  
\[V_{ar}=V_{gt.H}\cdot\eta_{hrsg}\cdot\eta_{ar} \tag{7}\]  
\[V_{hrsg}=V_{gt.H}\cdot\eta_{hrsg} \tag{8}\]  
where \(\eta_{hrsg}\) and \(\eta_{ar}\) are the efficiency of the heat recovery steam generator and the absorption refrigeration unit, respectively.  
The capacity of the gas-fired boiler \(V_{gb}\) is constrained by the capacity and heat load of the gas turbine. The gas-fired boiler needs to meet the maximum heating demand in the case of insufficient heating from the gas turbine. The capacity of the electric refrigeration unit is constrained by the capacity of the absorption refrigeration unit and the cooling load requirements.  
\[V_{gb}\geq max\left(H_{i}-H_{gt.i}\right) \tag{9}\]  
\[V_{er}\geq\max(C_{i}-C_{ar.i}) \tag{10}\]
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 6 Detail Analysis of Track Condition Using Time-Frequency Analysis
### Hilbert-Huang Transform (Hht)  
A time-frequency analysis method called Hilbert-Huang transform (HHT) is not subject to the uncertainty principle on time-frequency domain mentioned above [39; 40]. Thus, more localised fault detection is possible. The Hilbert-Huang transform (HHT) has been proposed for analysing nonlinear and non-stationary data by [PERSON] et al. [40].  
The HHT consists of the empirical mode decomposition (EMD) and the Hilbert transform. The EMD breaks time domain data into intrinsic oscillatory modes called intrinsic mode functions (IMFs). An IMF satisfies that the number of local extrema and the number of zero crossings must either equal or differ by at most one and that the mean value of the envelopes of local maxima and local minima is zero at any point.  
The iterative sifting process is applied for extracting IMFs from the original signal. Once the first IMF is calculated, it is subtracted from the original signal to obtain a residual. The EMD operation is applied again for the residual. This process repeats until the residual no longer contains any oscillation modes. The instantaneous amplitude and instantaneous phase of the IMFs are obtained by the Hilbert transform for each IMF component.  
The original signal, \(s(t)\), can be expressed by an EMD operation as  
\[s(t)=\sum_{i=1}^{m}IMF_{i}(t)+R(t), \tag{4}\]  
where \(IMF_{i}(t)\) is the \(i\)th IMF and \(R(t)\) is a residual.  
It should be noted that the HHT is highly adaptive but IMFs are strongly depend on the data themselves. Thus, there is a possibility that similar source signals are decomposed in different IMFs.
## 6 Data Preprocessing and Standardization
### Data Cleansing and Noise Removal  
Several preprocessing techniques have been used to reduce noise from Roman Urdu micro text. The process included the removal of punctuations and special characters, digits removal, folding of text to lower case, removal of hashtags, extra whitespaces, and user mentions. Missing rows in Roman Urdu and encoded text format in the corpora were also handled. During punctuation removal, major punctuations (\"\(\uparrow\)\"#5\"\(\otimes\)&(\")+_r./.; \(>\)=\(\subset\)?@ [] -] {} were removed except for dot. Dot was replaced by space since it is used as a sentence delimiter and may mostly combine two otherwise separate tokens in colloquial writing patterns.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Tweet (Roman Urdu text)** & **Profanity** & **Elaboration** \\ \hline Sahi hai ab tumhare himat hai to & Hateful & An utterance indicating the sign of \\ akaile main mujhse milnay aana & & blackmailing and involves psychological and physical threat. \\ samjhata hon phir. & & \\ \hline Apni khair manao aur bas dekhtay & Hateful & An utterance indicating the sign of \\ jao, main tum ko choron gi nahen. & & blackmail and involves psychological and physical threat. \\ \hline Na shakal na akal kis ko pari hai & Hateful & An utterance involving degrading, \\ tumhain galay bandhnay ki. & & abusive, and offensive language that \\  & & is meant to insult an addressee. \\ \hline Drama acha kar leti ho tum shame & Hateful & An utterance involving degrading, \\ on you bloddy bitch chullu bhar & & abusive, and offensive language that \\ pani me doob maro. & & is meant to insult an addressee. \\ \hline jutt zameendaar aadmi hai ye to & Hateful & An utterance involving words or \\ akal bhi to utnna chalae ga na. & & practices for disadvantaged people \\  & & to insult them because of their color, \\  & & caste, culture, race, or ethnic origin. \\ \hline chal oye idea acha hey weee. & Non-hate & Expression of warm approval to \\  & & someone’s thoughts and ideas. \\ \hline Khuda karay ga tum hamesha tak- & Hateful & An expression indicting exclusion \\ leef aur pareshani main raho gay & & and showing a wish that some form \\ kabhi khushi na milay. & & of misfortune or adversity will befall \\  & & the victim. \\ \hline dono sisters twins hain kitni piyari & Non-hate & An utterance expressing admiration \\ hain na dikhnay main. & & for the physical appearance of a person. \\  & & son. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of Roman Urdu instances from Roman Urdu corpora.  
To bring uniformity in the text, the data were converted to lowercase. This helped in downsizing the huge dimensional space of textual data by treating the two terms with different cases in a similar manner. The rows with missing labels were dropped. Finally, we encoded the data, which included emojis, special symbols, and other common stray characters, using the Unicode transformation type-8 encoding. The Python re and string modules were used to convert and manage this data.
# Narrowband-Internet of Things Device-to-Device Simulation: An Open-Sourced Framework
## 3 NB-IoT D2D Framework, Model, and Simulation
### NB-IoT D2D Architecture  
Our framework was implemented using the Matrix Laboratory Programming Language (MATLAB) [34]. Figure 1 shows the GUI for our NB-IoT attocell networking simulator in the context of three significant cell scenarios: Microcell, macrocell of a rural area, and macrocell of an urban area. The NB-IoT D2D attocell network topology is composed of a grid with 24 BSs that form five cells covering a total area of 80 km\({}^{2}\). A BS is identified by a blue square. The cell range (\(R_{cell}\)) is 2 km. The cell is a hexagon bounded by BSs with another BS at the centre. The number of devices/sensor nodes in the field is \(n\), and these are placed uniformly. The number of sensor nodes (\(n\)) can be changed as needed using the GUI. A normal node is represented by a circle (o), and an advanced node resuming the role of a CH is represented by a plus symbol (+). The energy of a normal node is \(Eo\), and the energy of an advanced node equals \((1+\alpha)\times Eo\). In this implementation, we assume \(\alpha\) equals 1, i.e., the advanced node has double energy to start with. The transmitted packet is referred to by an asterisk (*), a dead node (i.e., a sensor device with a depleted battery) is denoted with a red dot (. ), and a half-depleted node is denoted by a red dot in a red circle, as depicted in Figure 2. The flowchart of the proposed MATLAB-based NB-IoT D2D simulation, which is an open-sourced framework, is depicted in Figure 3. Table 1 describes key units in the MATLAB-based NB-IoT D2D simulation framework, and Table 2 summarizes the simulation parameters. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Unit** & **Main Role** & **Explanation** \\ \hline NB\_IoT\_GUI.m & Main code and path loss model & Contains the main functions and the interface user call-backs \\ \hline NB\_IoT\_GUI.GUI & Interface & Runs the simulation, shows the network topology and results for path loss models and Signal to Interference plus Noise Ratio (SINR) \\ \hline LntcyEnrgyPrfrmnc.m & Latency and energy model & Assumes two coverage classes depending on the path loss, thus, generates the matrices for latency and energy \\ \hline queueCH.m & Cluster Heads (CH) servers’ queuing & Develops the CH queue and determines the total energy consumption for each associated device \\ \hline queueBS.m & Base Station (BS) servers’ queuing & Develops the BS queue and determines the total energy consumption for each CH \\ \hline \hline \end{tabular}
\end{table} 
Table 1: MATLAB-based Narrowband-Internet of Things (NB-IoT) Device-to-Device (D2D) simulation framework: Key components.
## 3 LE Identification Method Considering the Fluctuation of Wind Power Output
### LE Identification Process  
The WAMS monitors the electrical quantities and each circuit breaker's opening/closing information online in real-time. Once WAMS monitors a change in the power grid topology, branch \(l\) is removed, and the backup protection is activated at one or several locations in the power grid, it quickly activates the LE identification procedure.  
Firstly, the CLS of this removed branch is searched based on the improved shortest path algorithm. Then, the generalized sensitivity is quickly calculated for all branches \(k\) within the CLS, and it is worth stating that the LERPF and sensitivity can be obtained offline because they are only related to the power grid topology and line parameters. After that, \(\Delta G_{i}\) is calculated based on the measured values of the active output of the bus nodes containing the wind farm before and after branch \(l\) is removed, and the APEV and LEE of branch \(k\) within the CLS are calculated based on the active power transmitted by each branch in the power grid before branch \(l\) is removed collected by WAMS in real-time. Finally, the cause of the overloading of branches within the CLS is quickly determined based on the LE identification criterion.  
Suppose the APMV and the APEV of branch \(k\) meet the LE identification criterion. In that case, overload occurs in branch \(k\) caused by LE, and the corresponding backup protection will have an unexpected trip. At this time, the backup protection trip signal should be blocked quickly to win time for the load-shedding action of the automatic safety device, and the subsequent load-shedding strategy is designed according to the operational state of the branches within the CLS to narrow the control range and increase the speed.  
Suppose the APMV of branch \(k\) does not satisfy the LE identification criterion. In that case, it means that branch \(k\) is overloaded due to a fault within the short-circuit range, and the corresponding backup protection trips after the delay period expires.  
Since the time interval for the WAMS to dynamically update the data is 20-50 ms, and the tripping delay of the backup protection is usually more than 1 s, there is enough time to realize the LE identification function. Considering the communication system's possible failure, if the backup protection delay has been reached while the result of LE identification has not been obtained, it will be tripped directly.  
The specific flow of identifying LE is shown in Figure 5.  
Figure 4: Steps to derive the mathematical expression for APEV.
## 2 Sampling Design, Sample Quality Control, and Spatial Reference Systems
### Sample Quality Control  
Sample quality is vitally important because samples provide the fundamental information for spatial predictive modeling. Many factors may affect sample quality and they are usually dataset-specific [39; 40]. Consequently, relevant factors need to be identified for each dataset and then relevant data quality control (QC) criteria need to be developed to QC the dataset [39; 40] prior to undertaking spatial predictive modeling. For example, in Geoscience Australia's Marine Samples Database (MARS; [[http://dbforms.ga.gov.au/pls/www/npm.mars.search](http://dbforms.ga.gov.au/pls/www/npm.mars.search)]([http://dbforms.ga.gov.au/pls/www/npm.mars.search](http://dbforms.ga.gov.au/pls/www/npm.mars.search))), seabed sediment samples were initially quality controlled prior to and after entering the database according to various criteria [12; 41]. However, the quality of the samples was still affected by many factors, including data credibility (e.g., non-dredge), data accuracy (e.g., non-positive bathymetry), completeness (e.g., no missing values), etc.; hence, data quality control approaches were developed to QC the samples of seabed mud content and sand content [12; 41]. These approaches may provide examples about how to identify relevant factors and develop possible data QC criteria for a given dataset. In some instances, data noise may result from repeated measurements, and certain rules may need to be developed to clean such samples based on professional knowledge [42]. Moreover, exploratory analysis can be used to further detect abnormal samples, as detailed in Section 5.
# Effect of Nanofluids on Boiling Heat Transfer Performance
## 3 Effects of Nanofluid Physical Properties and Heated Surface on Boiling Heat Transfer
### Effect of Heated Surface on Boiling Heat Transfer of Nanofluids
Since the boiling heat transfer strength of the nanofluid is related to the number of boiling vaporization core points. So the boiling heat transfer performance of the nanoparticle deposition surface becomes critical after the boiling experiment in the pure base liquid. [PERSON] et al. [PERSON] et al. (2019) examined the boiling heat transfer of water-based SiO\({}_{2}\) nanofluids and nanoparticle deposition surfaces in deionized water, and also investigated the effect of heating surface inclination on boiling heat transfer, as shown in Figure 13. It can be seen that when the inclination angle is 0 degrees, the boiling heat transfer curve of the deionized water on the smooth surface, the water-based SiO\({}_{2}\) nanofluid on the smooth surface and the deionized water on the deposition surface is sequentially moved to the left. Nanofluids have the highest critical heat flux, followed by the deposition surface, and finally the smooth surface. It can be seen that the boiling heat transfer of the nanofluid is strong, and besides the physical properties of the nanoparticle itself, it is also affected by the heated surface. The most intuitive effect of the modification of the heated surface is to increase the heat transfer area, thereby increasing the number of vaporization core points. From the indirect point of view, it is because the modification of the heating surface changes the number of boiling bubbles generated and the operating state in addition to increasing the number of gasification core points. Therefore, the nanoparticles have a larger deposition surface, and a higher concentration of the nanofluid solution can be used, so that the thermal conductivity of the base liquid increases greatly and the nanoparticle does not rapidly deteriorate due to the porous deposition layer formed on the heated surface. In addition, the bubbles generated by the irregular surface change the movement form of the bubble due to other forces such as capillary force, and are not singly subjected to upward buoyancy.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 5 Simulation Results and Discussion  
Cooja Simulator version 3.0 was used to evaluate the performance of the routing protocol for low power and lossy networks (RPL) under the standard trickle timer algorithm, the enhanced trickle (E-trickle) algorithm, the dynamic algorithm, and the new proposed algorithm that is called the elastic hop count trickle timer algorithm.  
The experiments consist of 20 and 40 nodes that are categorized as light network density. All nodes are set in both random and grid topologies under (100 m \(\times\) 100 m) dimensions. If nodes are set randomly, it means that they are set in a random location with a diverse distance between the neighbors' nodes. However, in the case of nodes, they are  
Figure 4: (**a**). Flowchart of the proposed algorithm. (**b**). Flowchart of the proposed algorithm (Hop count function call).  
set in a grid topology, which means they are set with equal distance. Each experiment is run five times to get an accurate outcome.  
This research assumed that the transmission range of each node was 30 m. The transmission success ratio (TX) employed is 100%, with distinct values of reception success ratio (RX) of 20%, 40%, 60%, 80%, and 100%. Table 2 shows the employed parameters in this research. The values were selected as in previous studies.  
The main measurement factors used to measure the algorithm's performance are convergence time, packet delivery ratio, and power consumption.  
1. Convergence time  
The first factor in measuring an algorithm is the convergence time, which means the time spent to build up the DODAG in the algorithm.
2. Packet Delivery Ratio (PDR)  
The second factor in measuring the behavior of an algorithm is the Packet Delivery Ratio (PDR), which is the ratio of the number of messages received divided by the number of messages sent. When the ratio is one, it means it is the best value.
3. Power Consumption  
Power consumption represents the consumed energy of nodes while moving and communicating in a specific low power and lossy network.
# Social Internet of Vehicles for Smart Cities
## 2 SIoV Technologies and Components: Next Generation of Vehicles
### Self-Driving  
The aim of autonomous cars is to drive independently without human interaction and to navigate the roads safely to reduce traffic accidents. According to a report by KPMG [33], the use of autonomous vehicles could eliminate 90% of all vehicle accidents. The industry [34] defined six accreditation levels of autonomous driving: At Level 0 (no automation), the driver has control of all aspects of the driving, even if he or she is assisted by various intervention or warning systems. At Level 1 (driver assistance), the vehicle will be fully controlled by the driver. This might include specific warning applications, such as blind spot detection. Level 2 (partial automation) has a low level of automation, including lane detection and an assisted braking system. Level 3 (conditional automation) combines two functions for congested traffic, namely driving in a straight line and automatic speed reduction. Level 4 (high automation) allows limited self-driving, which requires the driver to maintain consistent observation of the roadway in selected environments, to code control when required. At Level 5 (full automation), the vehicle is capable of reaching the navigated destination without human interaction.  
Current mass-market technology offers features on accreditation Level 2 (partial automation). For example, the 2014 Mercedes S-Class comes with options for auto-parking, clever steering, lane keeping, automated acceleration/braking and fatigue monitoring [35]. In traffic jams, the 2014 BMW i3 can accelerate autonomously and brake up to 30 miles per hour [36]. The 2015 Audi A7 provides autonomous braking in traffic jams [37].  
In the future, most technology companies and automobile manufacturers predict to offer features on higher accreditation levels. For example, Tesla expects to build a vehicle that can drive 90% of distances in fully-automated mode in 2016. In 2018, Google expects to provide fully-autonomous vehicles on the market. In 2020, Mercedes-Benz, GM, Nissan, Volvo, BMW and Audi all expect to sell fully-automated/self-driving vehicles.