esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation  
This section details an experimental analysis of the proposed model architectures. The experimental analysis is conducted by evaluating the performance of the model architectures in terms of accuracy, efficiency (runtime), and scalability. Accuracy is measured as the percentage of correct predictions, since the actual class values are known in advance. Runtime is calculated as the time taken to fit the model with the training set and validate the model using the test set. Scalability is the ability of the model to handle large amounts of data; that is, a model is considered as scalable if its runtime increases proportionately with the increase in input data records (tweets).  
Several experiments are carried out for both character-level CNN and character-level CNN-LSTM architectures by tuning the hyperparameters, such as the number of filters of the convolution layer, dropout rate, and the number of neurons of the fully connected/LSTM layer, in order to define the best-performing architectures for the detection of hate speech in a mix of English and Hindi-English (code-mixed) text. The performance of our models is further compared to that of [PERSON] et al.'s [7] model, which does not employ the pre-processing steps described in Section 4.1; however, it targets hate speech detection in a mix of pure and code-mixed languages, making it a closely related study to ours (please see Section 2.3).  
The proposed method was implemented using Python and its necessary associated tools (such as Natural Language Toolkit) and libraries (such as Numpy, Pandas, Scikit-Learn, Matplotlib, and Keras) on the Jupyter notebook in the Anaconda environment. The specifications of the machine used to run the experiments are Windows 10 OS, Intel Core i7, 8 GB of RAM, 1 TB HDD + 128 GB SSD, and 2 GB NVIDIA Graphic. Additionally, a virtual machine was used to run scalability experiments with the specifications of Windows 10 OS, 6-core, 64 GB of RAM, and 1 TB HDD. We would like to thank TDRA-ICT Fund for providing high-end computing machines to conduct our experiments, through the Digital Transformation Lab at Rochester Institute of Technology, Dubai (RIT-Dubai).
# Application Study of Adaptive Tracking Algorithm in Active Noise Control System of Transformer
## 2 Substation Sound Field Characteristics
In the process of noise propagation, the attenuation mainly includes geometric attenuation \(A_{div}\), attenuation atmospheric attenuation \(A_{atm}\), ground attenuation \(A_{gr}\) and barrier attenuation \(A_{bar}\). The total attenuation is the sum of the respective attenuation amounts as follows  
\[A=A_{div}+A_{atm}+A_{gr}+A_{bar} \tag{3}\]  
Among them, the four attenuation components of the calculation formula are as follows  
\[A_{div}=20\lg(r/r_{0}) \tag{4}\]  
\[A_{atm}=\frac{\alpha(r-r_{0})}{1000} \tag{5}\]  
\[A_{gr}=A_{s}+A_{r}+A_{m} \tag{6}\]  
\[A_{bar}=-10\lg[\frac{1}{3+20N_{1}}+\frac{1}{3+20N_{2}}+\frac{1}{3+20N_{3}}] \tag{7}\]  
where \(r\) is the distance from predicted point to sound source. \(r_{0}\) is the distance from reference point to sound source. \(\alpha\) is the atmospheric absorption attenuation coefficient of the frequency band noise. \(A_{s}\) is the ground factor of the sound source area. \(A_{r}\) is the ground factor of the receiving area. \(A_{m}\) is the ground factor of the intermediate area. \(N_{1}\), \(N_{2}\), \(N_{3}\) is the Fresnel number corresponding to the three diffraction paths along the rectangular obstacle. Using the measured point data and the noise attenuation model, substation sound field distribution is shown in Figure 4.  
Figure 4 shows that the transformer initial noise is 64 dB(A) to 66 dB(A). At the west station boundary, near residential building, the noise sound pressure level is still about 56 dB(A). With the distance increasing, the noise sound pressure level at south station boundary is attenuated less than 45 dB(A), which is close to environmental background noise. Due to the obstruction of the switch room and the control room, the noise level at east and north station boundary are much lower than that at the south and west boundary, and it is close to the environmental background noise. The analysis above shows that the main noise energy of the transformer noise is composed of 100 Hz to 500 Hz noise components. The five noise components are periodic noise, and contribute for more than 95% of the whole noise sound pressure level. The amplitude of the noise sound pressure level varies little, so it can be approximately considered as a constant quantity. And their phase increase or decrease continuously in the time domain. Taking the five noise components as the elimination target, the main sound pressure level of the transformer noise can be eliminated. When only the five noise components are used as reference signals, adverse effects of interference sources and other components can be avoided in the algorithm. In addition, the substation sound field distribution shows that only the west side of the substation needs noise reduction.
## 5 Network Costs
### Unforeseen Costs
#### 5.4.3 Internet Security  
For the purposes of real-time monitoring of the WSN, remote access to the gateway is granted over the Internet. By allowing external (_i.e._, outside the local network) Internet access, the gateway is exposed to the general public. While access to the gateway is protected by a username and password, unauthorized users may still attempt to access the system. In the first year of deployment, the WSN gateway received 175 unique unauthorized attempts at accessing the system and in just over two years there were over 700 unique recorded attempts.  
The significant number of attempts from outside users presented an unexpected maintenance cost for the network. For the purposes of security, a record is kept of all the addresses of users who made an unwanted attempt to access our system. These records are used to maintain a blacklist to restrict system access from future attempts.
## 3 Materials and Methods
### Adaptive Density Homogenization  
As listed in Table 1, raw trajectory data are in.txt data format, which records the car ID, time, longitude, latitude and speed for each trajectory point. The trajectory of one vehicle is reorganized as a sequential trajectory point set grouped by car ID and sorted by time. Limited by the equipped GNSS devices, unstable positional drift and varying sampling intervals often occurred to raw trajectory points. Hence, on the one hand, the trajectory points, which have no displacements with the former and next points, are first removed for avoiding unnecessary calculations. On the other hand, the vehicle trajectories, with some sub-segments (i.e., the distance between two successive points) longer than the predefined threshold, will be split at those sub-segments. In this paper, we refer to [PERSON] et al. [44] and set the threshold as 80 km/h \(\times\) 30 s by considering the most-frequent sampling interval of track points and the highest speed limit in urban areas. After that, we select those trajectories which have more than 5 track points to extract road intersections in view of data reliability.  
Moreover, as mentioned before, vehicle trajectory data are, actually, a sparse sensing way of road information due to uneven traffic flows. Generally, dense trajectories are obtained on high-grade roads and large road intersections while sparse trajectories are  
Figure 2: The workflow of the proposed method.  
collected at secondary roads and small street blocks. Hence, we propose an adaptive density homogenization method to decrease the trajectory density discrepancies at different grade roads. The adaptive density homogenization process encompasses three key steps, described as follows.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup  
### Datasets  
To evaluate the effectiveness of the proposed methods, we conduct experiments on two publicly available datasets.
The production of these particles is due to the fact that, when protons strike a nucleus in the body, the nucleus gets excited and returns to its ground state by emitting particles such as gammas or neutrons, or desintegrates into lighter nuclei [15]. Current imaging systems that detect secondary prompt gammas (PG) focus on finding the production origin, which can be achieved in several ways [16]. Further examples include passive collimation (by a pin-hole, a single slit, multiple slits or knife-edge slit cameras [17]), active collimation (Compton imaging) or using straight PG detection with scintillators (PG ray spectroscopy, PG ray timing, PG peak integration) [18]. Prompt gamma imaging based in scintillators and semiconductor detectors has gained interest in recent decades, given their increased detection efficiency. In particular, semiconductor materials such as CdTe and CZT have a high energetic and spatial resolution for gamma detection, as well as a high scattering probability if the thickness is optimised for this purpose. Furthermore, Compton camera imaging has emerged as a promising method to detect prompt gammas without the need to use physical collimation [19], thus increasing the gamma rates in the detection system. The multi-stage Compton camera concept is based on a minimum of two detection layers: a scatterer and an absorber. The scatterer should be made of a material with a high Compton scattering cross-section but thin enough to avoid multiple scattering events, whereas the absorber should have a high atomic number and energy resolution such that the scattered gamma deposits the entirety of its remaining energy. The reconstruction process is based on knowledge of the energy deposited in every layer and the location of its deposition, in order to calculate the Compton angle and the initial particle energy via Compton kinematics. By iterating the process with several events, an estimation of the location of the particle source can be achieved. A number of prompt gamma imaging studies have already been performed for different systems applied to proton beam therapy and show promising results [20; 21; 22; 23; 24]. This study aims to exploit the features of the HEXITEC detector [25] in a proton beam therapy monitoring environment, and study its suitability for use in a Compton camera based solely on HEXITEC detector layers. To achieve this, the requirements for suitability include a high spatial and energy resolution for an accurate reconstruction and a high response to gamma particles at the achievable production rates during proton therapy. The gamma response should ideally be higher than the response to other secondary particles for noise reduction. Although originally designed for material science X-ray imaging at energies up to 200 keV, this system has been used for several applications. In particular, its uses in medical physics include spectroscopic X-ray imaging [26; 27], SPECT imaging for the brain [28] and targeted alpha therapy [29], but this is the first application of the HEXITEC detector in a proton beam therapy scenario.
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
### Produced Water Stream in Real Oil Production  
Figure 1 illustrates a flowchart of the general oil extraction and produced water purification procedure. Initially, wet oil is gathered from wells via pipelines converging at point 1, leading to gas isolation stations. The oil undergoes four stages at a pressure of 28 bar, as depicted in the diagram. Upon entry into the separator, it impacts deflector plates and separating the gas, water, and oil. Level control is maintained through level and pressure control valves. After the second stage, a dehydtor is installed to extract water, which also contains dissolved salts. This process is conducted with the addition of a demulsifier and the application of a 380-volt electric current through graphite electrodes submerged in the oil above the water level. Subsequently, a desalter is installed to remove residual salts by introducing steam or pure wash water. This setup prevents conduction and disrupts the water-oil bonds, causing the residual droplets of produced water, laden with oil droplets and forming complex emulsions, to settle at the bottom. These emulsions, not effectively broken down in the earlier stages, have oil concentrations varying between 300 and 1000 ppm, which is a substantial amount necessitating treatment; therefore, purification operations commence from point 2, where the produced water, a byproduct requiring treatment due to its environmental impact and the presence of oil droplets, is extracted. Previously discussed were the traditional and laboratory processes and the shortcomings of most of these methods. An integral solution to this problem is proposed, based on the use of demulsifying agents consisting of solvents capable of interacting with oil droplets and breaking the emulsions. For this purpose, a selection of optimal materials is necessary, adhering to established principles and standards. At point 3, following the prior addition of the solvent, oil is separated in settling tanks.  
Resolving the treatment of produced water superficially is insufficient, because crude oil production involves huge amounts of produced water generated daily worldwide. Therefore, establishing a standard solution is challenging, considering the diverse conditions of oil-producing nations and the existence of wells in marine, onshore, and offshore environments. The adopted approach also includes a salt removal process at point 4 using an evaporation method and collecting the salts from the bottom through sedimentation. Condensed pure water, in the form of steam, is collected in a tank at point 5 for uses such as irrigation or general purposes, or it is re-evaporated for use in washing salts in the desalting stage. All these processes utilize the energy from gas combustion to capitalise on energy that would otherwise be wasted.
# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
## 3 LiMha
### Data Transmission  
Once the traffic load is estimated in each sector and feasible solution for energy consumption is computed and rest of the pre-requisites are fulfilled then data transmission is carried out towards the respective destination. In converge-cast routing algorithm, nodes deployed in the upper layer always pass on data to the downstream node. Nodes in outermost sector transmit data to the \(SN\) in their communication range. Then \(SN\) finds out the \(SN\) from \(C_{n-1}\)[12]. Due to the increment of load from top to bottom, heterogeneous network is considered. Let assume \(NN_{j}\) sends data from sector \(S_{n}\) to sector \(S_{n-1}\) in order to transmit data to the sink as shown in Figure 2. Distance between them is \(d_{(i,j)}\). If the distance is within the free space then data transmission proceeds else an alternative route is computed to deliver data at the destination or \(SN\). Lets assume \(k\) bit data to be transmitted by the nodes in sector \(S_{n}\) to the nodes in sector \(S_{n-1}\). Energy consumes in sending \(k\) data to the next hop can be calculated from Equation (7).  
The data transmitted by node \(i\) in sector \(S_{n}\) to node \(j\) in sector \(S_{n-1}\) is equal to \(k\times ATF_{iM_{s}}\) and the data receive by node in sector \(S_{n-1}\) is \(AT_{iS_{n}}-1\); \(NN\) in a sector \(N_{n}\) interacts with normal node in sector \(S_{n-1}\). If \(NN\)\(i\) in a sector is unable to find normal node \(j\) in sector \(S_{n-1}\), void hole occurs. In that case normal node has to find a suitable forwarder node. \(SN\) in a sector receives the data from \(NNs\) in case when void hole occurs. The situation arises when normal node does not lie in transmission  
Figure 4: Feasible Region: Bandwidth.  
range of \(SN\). Then \(NN\) sends the data to super node through its neighbor node which is close to \(SN\). If somehow neighbor node in respective sector is not available, super node collects the data by itself from normal node. After the data successful reception by \(SN\) in a sector \(S_{n}\), it transmits the data to \(SN\) in sector \(S_{n-1}\). As the time progresses, after the death of super node in sector \(S_{n}\), super node present in adjacent sector \(S\) acts as data forwarder for remaining nodes. It has enough energy for sending data to the sink directly and this process continues until all the data is successfully delivered to the sink.
## 3 Test Results and Discussion
### Ultimate Strength and Stiffness Characteristics  
Ultimate strengths of the specimens are tabulated in Table 3. The strength increase percentages were calculated with respect to that of corresponding specimens without laminates. The ultimate strength increase percentages of the CFRP-strengthened specimens were significantly, ranging from 51.4 to 67.5% for CSR1 series of specimens, and from 16.9 to 40.9% for CSR2 series of specimens. It is clear that the CFRP laminates can considerably enhance the ultimate strength, especially for the lightly reinforced slabs. The comparison between C1 series of specimens and C2 series of specimens revealed the effect of the compressive strength of concrete on the punching shear strength. Increasing the compressive strength of concrete resulted in a higher punching shear strength. Similarly, the comparison between CSR1 series of specimens and CSR2 series specimens demonstrated that an increase in the steel reinforcement increased the punching shear strength. As shown in Table 3 and Figure 7, it is observed that the second layer of the CFRP laminates was marginally effective in enhancing the ultimate strength, especially for CSR1 series of specimens.  
To evaluate the stiffness of the slab, an initial stiffness was defined in the form of secant stiffness. As observed from four specimens without laminates, slabs cracked at the load of approximately 20% of the ultimate strength. Therefore, the initial stiffness was determined by the secant passing the origin and the point corresponding to 20% of the ultimate strength. As shown in Table 3, the increase of the initial stiffness due to the laminates was considerably higher for CSR1-C1 series of specimens. The specimens without laminates had generally lower initial stiffness compared to the specimens with laminates. Nevertheless, the amount of the laminates did not noticeably change the initial stiffness.
###### Abstract
However, even at this stage, the presence of shadows in the images can have an impact on the estimation of crop traits [37]. Importantly, the influence of variation in illumination and image background on the derived vegetation indices from UAV RGB data analysis and evaluation has not been studied sufficiently [24; 28]. Moreover, despite selecting sunny weather conditions for data collection [38], variation in illumination is inevitable due to differences in time and space during data acquisition. Therefore, RGB images obtained from UAVs will inevitably be affected. Additionally, the backgrounds of rice field images obtained during different growth stages and experimental sites may change. However, there remains a knowledge gap regarding the influence of variable illumination and image background on rice LAI estimation based on RGB-derived indices. The present study aims to fill this knowledge gap. Our primary objectives were to (1) assess the impact of variable illumination on rice LAI estimation based on CIs derived from UAV RGB images and (2) assess the impact of background on the estimation of LAI.
## 5 Performance Evaluations
### Evaluation of Frequency of Global Updates
Figure 12 presents the results in terms of communication overhead (CO) when we vary the number of nodes. CO values of global updating with \(f~{}=~{}10,15,\) and \(20\) are very close to each other. On the other hand, when we increase frequency to \(5\) and \(3\), the increase in the communication overhead is very large with increasing number of nodes. The trend in Figure 12 is steeper when we increase the number of nodes; on the other hand, in Figure 12 and Figure 12, the graphs increase gradually. In Figure 12, we observe that COs of all frequencies slightly increase. The reason for this behavior is that when we increase the number of mobile nodes, more convex hull update messages are generated due to higher mobility rate. Figure 12 shows the CO results with increasing mobility speed. The figure has a very similar shape with the varying number of mobile nodes graph in Figure 12. Again, due to adaptation of timeout interval according to the speed of MN, higher speed results in more convex hull update messages in the network.
## 4 Results and Discussion  
### Validation  
The effectiveness of the developed finite element formulation was further verified by comparing the results available in the literature. [PERSON] et al. [37] evaluated the natural frequencies of a non-uniform fiber-reinforced polymer laminated composite structure by considering ply orientations [0/90]ss at the right end and [0/90] at the left end, respectively. The mechanical and geometrical properties considered for the composite fiber lamina of all these configurations were \(E_{1}\) = 30.2 GPa, \(E_{2}\) = 6.8 Gpa, \(v_{12}\) = 0.269, \(G_{12}\) = 2.68 Gpa, \(\rho_{c}\) = 1745 kg/m\({}^{3}\), and a ply thickness of 0.19 mm, and resin pocket properties were \(E\) = 3.45, \(v\) = 0.3, \(G\) = 1.33 GPa, and \(\rho\) = 1200 kg/m\({}^{3}\). Table 2 compares the natural frequencies of the tapered laminated composite plate for (1,1), (1,2), and (2,1) modes under CFFF end conditions for the tapered configuration, by using the present finite element formulations with the results presented in [PERSON] et al. [38]. Referring Table 2, the natural frequencies evaluated by the present FEM had a very close agreement with those presented in [PERSON] et al.  
Figure 4: Experimental setup.  
The time-domain signals were obtained using a finite element code through MATLAB(r). Two conditions of the composite plates, namely laminated and delaminated plates, were considered for the study. Forty nodes were arbitrarily selected and the corresponding time-domain signatures were simulated. Similarly, the laminated and delaminated composite plates were fabricated as shown in Figure 1a,b. The corresponding vibration signals were acquired from the composite plates using the accelerometer sensors. Figure 5 shows the time-domain response of the laminated and delaminated structures using numerical and experimental studies.