esa-sceva/satcom-chunk-collection huggingface dataset:
# Collecting a Large Scale Dataset for Classifying Fake News Tweets Using Weak Supervision
## 4 Approach
### Tweet-Level Features  
For tweet-level features, we again first collect all information directly available from the API, e.g., number of retweets and retweets, as well as whether the tweet was reported to be sensitive (see [[https://help.twitter.com/en/safety-and-security/sensitive-media](https://help.twitter.com/en/safety-and-security/sensitive-media)]([https://help.twitter.com/en/safety-and-security/sensitive-media](https://help.twitter.com/en/safety-and-security/sensitive-media)), accessed on 26 April 2021).  
In addition, we create a number of additional features characterizing a tweet, e.g.,  
* features characterizing the tweet's contents, such as URLs, hashtags, user mentions, and emojis. For emojis, we also distinguish face positive, face negative, and face neutral emojis ([[http://unicode.org/emoji/charts/full-emoji-list.html](http://unicode.org/emoji/charts/full-emoji-list.html)]([http://unicode.org/emoji/charts/full-emoji-list.html](http://unicode.org/emoji/charts/full-emoji-list.html)), accessed on 20 May 2017, and [[http://datagenetics.com/blog/october52012/index.html](http://datagenetics.com/blog/october52012/index.html)]([http://datagenetics.com/blog/october52012/index.html](http://datagenetics.com/blog/october52012/index.html)), accessed on 9 May 2017);
* features characterizing the tweet's language, such as punctuation characters, character repetitions, and ratio of uppercase letters;
* linguistic features, such as ratio of nouns, verbs, and adjectives (using _WordNet_[29] and _NLTK_[30], number of slang words, determined using _Webpodia_ ([[http://www.webpodia.com/quick_ref/Twitter_Dictionary_Guide.asp](http://www.webpodia.com/quick_ref/Twitter_Dictionary_Guide.asp)]([http://www.webpodia.com/quick_ref/Twitter_Dictionary_Guide.asp](http://www.webpodia.com/quick_ref/Twitter_Dictionary_Guide.asp)), accessed on 27 April 2021) and _noslang.com_ ([[http://www.noslang.com/dictionary/](http://www.noslang.com/dictionary/)]([http://www.noslang.com/dictionary/](http://www.noslang.com/dictionary/)), accessed on 27 April 2021), and existence of spelling mistakes (determined using the _pyenchant_ ([[https://pypi.org/project/pyenchant/](https://pypi.org/project/pyenchant/)]([https://pypi.org/project/pyenchant/](https://pypi.org/project/pyenchant/)), accessed on 28 April 2021) library).  
In total, we create 69 tweet-level features. The full list of features is depicted in Tables 13 and 14. In order to make the approach applicable in real-time scenarios and be able to immediately classify new tweets, we remove time-dependent attributes (i.e., number of retweets and number of favorites).
## 3 Results
### Effects of Different Vibration Sources on the Operator's Seat
#### 3.2.1 Vibrations Generated by Tractor Wheel-Soil Contact  
The irregularity of the soil and of the tires generates a vertical motion of the axle and consequently vibrates the operator's seat, the level of which depends on the inertial and elastic properties of the tractor including the presence of suspension systems.  
Figure 6 shows the transmissibility from the rear and front wheels of the tractor to the operator's seat. Results refer to tractors with seat suspension only (3-DoFs tractor in Figure 1a); suspension systems for the seat and front axle (4-DoFs tractor in Figure 1b); and  
Figure 5: Power spectral density of the numerical and experimental acceleration at the operator’s seat of the tractor with an operative speed \(v=6.3\) km/h on the rougher track.  
suspension system for the seat, axle, and cabin (6-DoFs tractor in Figure 1c) introduced in Section 2.2.1.  
The transmissibility \(z_{1}/z_{R}\) in Figure 6a shows that the vibrations due to the rear wheel are amplified at the operator's seat at frequencies below 2 Hz, and they are attenuated in the remaining range of frequencies. The transmissibility \(z_{1}/z_{F}\) shows, instead, that the vibrations due to the front wheel are attenuated in the whole range of frequencies.  
Figure 6b shows the influence of the front axle suspension on the transmissibility between the front wheels and the seat. Note that the suspension on the front axle does not influence the transmissibility \(z_{1}/z_{R}\) between the rear wheels and the seat, since it does not affect this vibration transmission path. In the cases under examination, the suspension of the cabin does not affect the transmissibility below 6 Hz, whereas it is effective for higher frequencies. It is in fact generally used to improve the acoustic comfort of the operator at frequencies above 20 Hz. In general, suspension systems provide good isolation at higher frequencies, but they amplify vibrations at low frequencies. The effect of the axle suspension is clearly shown in the transmissibility \(z_{1}/z_{F}\).  
Figure 7 shows the transmissibility ratios--with or without the damper \(c_{3}\)--of the front wheels \(z_{F}\) to the vertical displacement of the seat \(z_{1}\), the vertical displacement of the frame \(z_{2}\), and the rotation of the frame \(\theta_{2}\), respectively. Note that all the other parameters were set to the values in Table 1.  
Figure 7a highlights that the damping of the front axle suspension \(c_{3}\) significantly reduces the vibrations transmitted to the operator's seat (\(z_{1}/z_{F}\)). Figure 7b,c shows that the reduction of the vibrations transmitted to the operator is due to the attenuation of the vibrations transmitted to the frame.  
Figure 6: Transmissibility between the tractor wheels and the operator’s seat for different tractors: (a) rear wheel \(z_{1}/z_{R}\); (b) front wheel \(z_{1}/z_{F}\).
## 5 Results and Discussion
### Power Supply Performance  
As already indicated, a step-down converter is needed in the power supply to charge the supercapacitor (up to 2.5 V) from the voltage delivered by the PV panel. This goal is achieved by including an integrated circuit by Maxim (MAX5090C [39]), which gives rise to the waveforms shown in Figure 7 during the charging process. In this figure, the capacitor has already reached its maximum voltage.  
Figure 7: Waveforms associated to the operation of the step-down converter.  
Charging of the supercapacitor is achieved within a reasonable time interval. Figure 8 shows the waveforms corresponding to the initial charging process assuming that the supercapacitor is initially discharged and reaches 2.5 V. The time required for this is some 20 s assuming a maximum panel voltage of 20 V. Notice that this process will only take place the first time the system is connected to the PV panel. From then on, the system succeeds in keeping the supercapacitor almost fully charged--as long as there is enough sunlight.  
Once the voltage across the supercapacitor has reached a minimum value, the step-up converter starts operating and producing a constant supply voltage. In the prototype developed, this voltage has been chosen to be 3.3 V, since that is a requirement of the XBee module and can also be used with the microcontroller. In this case, Microchip's MCP1624 integrated circuit [40] has been selected to generate the 3.3-volt voltage. In order to determine whether the performance of the power supply was adequate, it was connected to a preliminary prototype of a communication module.  
The consumption of the _Smart Module_ is not constant. Most of the time it will be idle until the time comes to send the information to the Central Node. At this instant, the device wakes up from sleep and its consumption increases. This could be seen in Figure 6, which showed that the power supply implemented is stable during these transitions from sleep to active mode and vice versa. Figure 9 shows a close up of the current consumption where the wake-up signal can be seen.  
The supply for the XBee system has been tested during some months without power cuts in a small-scale PV plant available at our facilities. Figure 10 shows one of the _Smart Modules_ designed attached to a photovoltaic panel whose current and voltage were being monitored. As indicated above, this _Smart Module_ was supplied by the circuitry designed. Figure 11 illustrates the appropriate operation of the power supply by showing a period of four working days. All throughout this time, the voltage required by the system (labeled XBee in the Figure) is kept constant. Note also that the voltage across the supercapacitor never goes under 1.3 V, which means that 27% of the initial energy is still stored in the ultracapacitor. The low solar radiation present during the test should also be noticed.  
Figure 8: Initial charging of the supercapacitor. Considering a PV panel voltage of 20 V, it takes less than 20 s to have the supercapacitor fully charged.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 7 Performance Evaluation
### Energy Consumption Estimation  
To accomplish its mission in the affected area, the drone consumes energy to fly, scans the area, collects the targeted data, ciphers the message and forwards it to \(CS\) via other drones. All these operations are powered by the drone's battery, which has restricted capacity. Thus, one of the main aims of our proposed scheme is to be lightweight and efficient in terms of energy consumption. Although encrypting and transmitting the readings consumes power, other drone parameters, such as the drone's weight and hovering speed, can deplete the drone battery. Thus, planning the drone's mission, including defining the battery threshold level, should take these parameters into consideration. The energy threshold is the power level that the drone has to finish the mission and return to the \(CS\) by reaching it; this threshold should be higher than the battery recharging alarm level. In our proposed scheme, the drone is flying for 30 min; it hovers over the disaster area for around 15 min while the remaining time is evenly split between flying to the disaster area and back to the recharging station. This flying time is expected to be safe in terms of power consumption.  
In this section, we investigate the energy consumption trend if the communication and computation burdens vary, assuming drones with equal weight and velocity. We use the data for Mavic series drones by DJI (Shenzhen, China, [[https://www.dji.com/products/mavic](https://www.dji.com/products/mavic)]([https://www.dji.com/products/mavic](https://www.dji.com/products/mavic))) which feature flying time of 30 min or more under a light load. Figure 11 shows the battery consumption pattern with Drone \(x\) working as a cluster head for the group for a period of time besides its main mission (monitoring the area and collecting information) while drone \(y\) only scans the area and aggregates data. As shown, the energy consumption curve is divided into three phases: the first one is the period of flying from the \(CS\) to the area. The two drones fly at the same speed so that the curves are identical (We assume that the drones fly at a high velocity from the station to the area; this is the reason for the  
Figure 10: Computation overhead for a group Ring-LWE-based proposed scheme versus an RSA-based case.  
high energy consumption). When the drones reach their assigned locations, the second phase begins; the drone reduces its hovering speed to approximately 50-60%, i.e., notice that the consumption is lower than the first stage. Then, the drones start their missions. Clearly, Drone \(x\) produces higher loads and consumes more energy than Drone \(y\) because it performs more tasks than Drone \(y\). However, the difference between the two power consumption curves is small due to the light communication and computation complexity of our proposed scheme. Finally, the drones fly back to the station, i.e., at a high velocity as the first phase. The drones have finished their missions and landed before reaching the threshold level.
# Design of Multi-Mode Antenna Array for Use in Next-Generation Mobile Handsets
## 2 Design and Configuration of the Proposed 5G Antenna Array  
The schematic of the designed MIMO handset antenna is plotted in Figure 1. As shown, it is composed of four PIFA pairs that have been deployed at different corners of the mainboard. The presented handset antenna is designed on an FR-4 dielectric with a relative permittivity of 4.4, loss tangent of 0.026 and a thickness of \(\rm{h_{x}}\) = 1.6 mm. Each PIFA element is fed by a 50 ohm discrete feeding technique extended from the ground plane to the antenna feedline. The values of the design details are listed in Table 1.
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
## 2 Intelligent Systems at Home
### Case Study: Networking of Intelligent Systems  
This paper considers the multi-hop network deployment practical scenario for an intelligent environment represented in Figure 1. Even though the diagram represents a network deployment in a home, such a deployment could also take place in assisted living homes, foster homes, hostels, student residences, hotels, or any other analogous buildings. As shown in Figure 1, some wireless access nodes (ANi) and mobile nodes (MNij) are included in the network. The MNij, as the ANi, can also include some sensors depending on the application of the network. The ANi in Figure 1 are deployed all through the home to provide wireless network connectivity to the MNij. Many types of sensor are included in systems for intelligent environments. They are usually attached to both access and mobile nodes with wireless connectivity. The network in Figure 1 can also include some environmental sensors in the rooms, as for instance, temperature sensors for the centralized air-conditioner system or fire alarms. The nodes would centralize the measured data into a data sink (DS). The alarms and control commands that might be necessary after the data processing would be generated in the DS. Furthermore, several functionalities in the computer connected to the first and the last nodes of the network eased the monitoring process. For instance, the test commands were generated in the network tester (NW tester) and the resultant data were aggregated in the DS.  
There are several reasons to deploy a wireless network of access nodes instead of considering the wired solution. On the one hand, the deployment costs of a wireless network are lower than the costs of the wired approach. On the other hand, the installation of the network access nodes is complex, and might be risky, so a wireless network is arranged whenever the budget restrictions, convenience, or other issues make this approach the most suitable one.  
Generally speaking, the devices in intelligent environments are interconnected and they form a communication network. Hence, a communication standard must be found to provide connectivity to such devices. Bluetooth [24] is a standard that satisfies these requirements.  
Then, in the case shown in Figure 1, every \(\rm MN_{ij}\) connects to the most appropriate \(\rm AN_{i}\) periodically, once the programmed inactivity period is finished. The requirements in latency depend on the specific application and the type of sensor. As an example, temperature or humidity sensors are usually included in applications with longer monitoring intervals than presence sensors. In case the wireless communication standard offers many low power working modes, as it happens in the case of Bluetooth, it is necessary to choose wisely the low power mode for the best performance of the system.
## 3 Basic Theory  
### Wavelet Threshold Denoising  
Fundamental theory of wavelet denoising can be concluded as follows: wavelet decomposition is firstly conducted on the noisy signal, then wavelet coefficients that belong to useful signal are kept and others are eliminated, and finally inverse wavelet transform is operated to reconstruct the remainder coefficients.  
Assume that the noisy signal series \(x=\{x_{1},x_{2},x_{3},...,x_{k}\}\) can be expressed as follows:  
\[x_{i}=s_{i}+n_{i} \tag{1}\]  
where \(i\) = 1, 2, 3,..., \(k\), \(s\) = {\(s_{1}\), \(s_{2}\), \(s_{3}\),..., \(s_{k}\)} is the useful initial signal and \(n\) = {\(n_{1}\), \(n_{2}\), \(s_{3}\),..., \(n_{k}\)} is noise signal.  
Then \(x\) is decomposed by \(J\) levels WT and the \(i\)-th wavelet coefficient in \(j\)-th can be presented as \(d_{ij}\), where \(j\) = 1, 2, 3,..., \(j\). Since WT is a kind of linear transform, wavelet coefficients of \(x\) are consisted of ones decomposed by \(s\), denoted as \(U_{ij}\), and that of \(n\), called \(V_{ij}\). The purpose of wavelet denoising is to eliminate \(V_{ij}\) and obtain the estimate signal \(\hat{s}\) of the noisy signal. The ideal \(\hat{s}\) has a minimum mean square error with \(s\) under the premise of eliminating noise component furthest. The mean square error (MSE) \(\hat{\varepsilon}\) can be calculated as follows:  
\[\hat{\varepsilon},s]=\frac{1}{k}\,|\,|\,\hat{s}-s\,|\,|\,=\frac{1}{k}\sum_{i=1 }^{k}\,[\hat{s}_{i}-s_{i}]^{2} \tag{2}\]  
The threshold during the denoising process is calculated according to SURE model, which can be obtained as follows:  
\[\lambda_{j}=\mathrm{MAD}(|d_{i,j}|)/q \tag{3}\]  
where \(\lambda_{j}\) denotes the threshold of \(j\)-th level, \(\mathrm{MAD}(\cdot)\) is a median value function and the value range of \(q\) is [0.4, 1] in general.  
There are two typical threshold functions during the wavelet denoising process, the first called hard-threshold:  
\[\hat{d}_{i,j}=\left\{\begin{array}{cc}d_{i,j},&\mathrm{for}\,\,\,|d_{i,j}| \geqslant\lambda_{j}\\ 0,&\mathrm{otherwise}\end{array}\right. \tag{4}\]  
where \(\hat{d}_{i,j}\) donates the wavelet coefficient of denoised signal. The other function is called soft-threshold:  
\[\hat{d}_{i,j}=\left\{\begin{array}{cc}\mathrm{sgn}(d_{i,j})(|d_{i,j}|- \lambda_{j}),&\mathrm{for}\,\,\,|d_{i,j}|\geqslant\lambda_{j}\\ 0,&\mathrm{otherwise}\end{array}\right. \tag{5}\]  
where \(\mathrm{sgn}(\cdot)\) is sign function, which returns \(1\) if the element is greater than \(0\); \(0\) if it equals \(0\); and \(-1\) if it is less than \(0\).  
The estimated signal is reconstructed through inverse wavelet transform on \(\hat{d}_{i,j}\). It can be seen that the key point of the denoising process is selecting appropriate threshold to minimize Equation (2).
# A New Under-Sampling Method to Face Class Overlap and Imbalance
## 4 Experimental Set-Up
### Reference Under-Sampling Algorithms and Classifiers  
Besides the DBMIST-US method with a maximum imbalanced ratio set to 2, we also implemented some state-of-the-art under-sampling algorithms already described in Section 2: CNN, ENN, TL, NCL, OSS, SBC, and ClusterOSS. In addition, the experiments also included a collection of methods that are popular in the literature related to class imbalance (see Table 2).  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & **Data Set** & **Class Distribution** & **\#Instances** & **IR** \\ \hline
1 & yeast-0-5-6-7-9\_vs\_4 & 51–477 & 528 & 9.35 \\
2 & glass-0-1-6\_vs\_2 & 17–175 & 192 & 10.29 \\
3 & glass2 & 17–197 & 214 & 11.59 \\
4 & shuttle-cd-vs-c4 & 123–1706 & 1829 & 13.87 \\
5 & yeast-1\_vs\_7 & 30–429 & 459 & 14.30 \\
6 & glass4 & 13–201 & 214 & 15.47 \\
7 & ecoli4 & 20–316 & 336 & 15.80 \\
8 & page-blocks-1-3\_vs\_4 & 28–444 & 472 & 15.86 \\
9 & glass-0-1-6\_vs\_5 & 9–175 & 184 & 19.44 \\
10 & yeast-1-4-5-8\_vs\_7 & 30–663 & 693 & 22.10 \\
11 & glass5 & 9–205 & 214 & 22.78 \\
12 & yeast-2\_vs\_8 & 20–462 & 482 & 23.10 \\
13 & flare-F & 43–1023 & 1066 & 23.79 \\
14 & yeast4 & 51–1433 & 1484 & 28.10 \\
15 & yeast-1-2-8-9\_vs\_7 & 30–917 & 947 & 30.57 \\
16 & yeast5 & 44–1440 & 1484 & 32.73 \\
17 & ecoli-0-1-3-7\_vs\_2-6 & 7–274 & 281 & 39.14 \\
18 & abalone-17\_vs\_7-8-9-10 & 58–2280 & 2338 & 39.31 \\
19 & yeast6 & 35–1449 & 1484 & 41.40 \\
20 & poker-8-9\_vs\_5 & 25–2050 & 2075 & 82.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the real-life data sets.  
Figure 2: Scatter plots of the synthetic data sets with 0% and 70% of noise (points in gray color are for noisy data). (**a**) subclus-0; (**b**) clover-0; (**c**) paw-0; (**d**) subclus-70; (**e**) clover-70; (**f**) paw-70.  
To investigate the robustness of the experimental results and obtain fair comparisons between the under-sampling algorithms, we evaluated the performance across three popular classification models of different nature: an instance-based learner (nearest neighbor, 1 NN), a decision tree (non-pruned J48) and a linear classifier (support vector machine with a linear kernel, SVM). The parameters of these classifiers throughout the experiments were the default values of the WEKA open source machine learning software [61].  
The evaluation of the under-sampling algorithms was estimated by 10-fold cross-validation in order to avoid biased results [62]. Each original data set was randomly divided into 10 stratified parts: for each fold, nine blocks were used as a training set, and the remaining portion was used for testing each of the three classifiers that were trained by the different under-sampling strategies. Finally, the performance measures of each method were averaged over the 10 trials.
# EBIC Imaging of Conductive Paths Formed in Graphene Oxide as a Result of Resistive Switching
## 2 Materials and Methods  
The GO-based MIS structures investigated in this work are similar to those used in [21]. GO films with a thickness of 40 nm were synthesized by oxidation of graphite using the modified Hummers' method [33; 34; 35] and deposited onto n-type Si substrates (the phosphorus concentration of \(10^{15}\)/cm\({}^{3}\)) by spin-coating the GO-based sol of 5 mg/mL at a rotation speed of 3000 rpm. The comprehensive characterization of the GO films has been carried out elsewhere [32]. For investigation of the resistive switching characteristics in the GO films, the top electrodes with a diameter of 1 mm were prepared by Al thermal evaporation and the bottom contact was formed by scratching AlGa paste into the rear side of Si. The EBIC study was carried out at room temperature with a scanning electron microscope JSM-840 SEM (Jeol) with a current amplifier Keithley 428. The beam energy was in the range from 10 keV to 35 keV and the beam current was lower than \(10^{-10}\) A to minimize the irradiation dose during the EBIC imaging. During the EBIC measurements, the Al electrode was grounded and the bias was switched off. The current-voltage (I-V) switching characteristics of the MIS structures placed into the SEM chamber were measured using a Keithley 2450 SourceMeter while an electron beam was turned off. The depth dependence of the e-beam deposited energy was calculated by the Monte Carlo method using an algorithm developed in [36, 37] and the GO parameters reported in [38].