esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Predictive Approaches for Sending Marketing Campaigns
### Predictive Approaches Based on Classification Classifiers  
[PERSON] et al. [29] determine the best time to send communications based on the information collected from exchanged emails between companies and their subscribers. The prediction of the best time is made through segmentation by subscriber types/profiles. Three models are implemented based on the user's status. The first model indicates if the email was opened. The second model corresponds to the time of the email opening, and the third model corresponds to the day that the email was opened. They use Decision Trees [30], Random Forest [31], and Naive Bayes [32]. Decision Trees obtained the best performance with a F1-score of 93% for the first model (opened/not opened), F1-score of 80.54% for the second model (opening time), and 88.63% for the last model (opening day).  
[PERSON] et al. analyze which factors might influence the opening rates of marketing emails with a financial nature [33]. Two classification models were used to determine whether a campaign was successful or not, based on the open rates. The classification models are based on (i) the names of campaigns, (ii) recipients and senders, (iii) the content of the message, (iv) the number of emails sent, (v) the number of emails delivered, and (vi) the number of open emails. They use Decision Trees [30], Random Forest [31], and Gradient Tree Boosting [34] to improve and fine-tune the parameters. Random Forest obtained the best performance for campaigns labeled as a success with a F1-score of 71% and for campaigns labeled as a failure with a F1-score of 93%. The latter value is higher because there are more failed campaigns.  
[PERSON] et al. [35] describe two classification models to predict the opening rates of emails, based on the characteristics extracted from the email and user profiles. The classification algorithms used are Decision Trees [30] and Support Vector Machines [36]. Both models are based on (i) the emails sent, (ii) the user's action, (iii) the content of the email sent, (iv) the day and the time when the email was opened, (v) the location, and (vi) the users' email domain. Decision Trees performed better, with an F1-score of 80% in the opening rates. Support Vector Machines achieved an F1-score of 74% in the opening rates. The second model assesses the domains' impact on the performance, so the domain is filtered. Decision Trees obtained an F1-score of 72% and Support Vector Machines an F1-score of 70%.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 4 Experimental Result Analysis
### Analysis of Response Amplitude Based on Flaw Position  
This section focuses on analyzing the influence of the flaw position on the response amplitude. As depicted in Figure 8, the response peaks of flaws are affected by both the flaws and basic response drifts. To accurately quantify the response amplitude, the underlying response drifts need to be eliminated. In the experiment, the response obtained under \"no defect\" conditions served as the baseline value for identifying the basic coil response drifts during scanning. Compensated responses were obtained by subtracting the essential drift value. Figure 15 shows the offset response values of flaws with different lengths at 2500 Hz excitation under \(D_{1-1}\) and \(D_{2-1}\), where the baseline is located nearby the zero line. The basis values \(R_{11}\) and \(R_{22}\) were obtained by averaging the baseline values  
Figure 14: Diagram of the relationship between flaw axial length and peak\(-\)value distance (**a**) Peak\(-\)valley distance comparison of different cross\(-\)sectional area flaws on Layer 2 (**b**) Graph of relationship between flaw axial length and averaged peak\(-\)valley distance at 2500 Hz excitation frequency.  
Figure 13: Comparison of peak\(-\)valley distances for flaws with different sectional areas (**a**) \(D_{2-3}\)_situation_ on Layer 2 (**b**) \(D_{2-6}\)_situation_ on Layer 2.  
on either side of the peak value to minimize the influence of fluctuations. Based on the compensated response values, the absolute response amplitude \(R_{a}\) was calculated ulteriorly using this formula:  
\[R_{a}=(R_{l1}+R_{l2})/2-R_{h} \tag{6}\]  
Table 2 presents the results of an experimental investigation into the effect of flaw position on the response amplitude under \(D_{1-1}\) and \(D_{2-1}\) for the same axial length. To quantify the impact of the flaw position, an uncertainty analysis was performed on the data. The analysis involved computing the absolute amplitudes and amplitude difference between the two situations. The results illustrated that the amplitude of \(D_{1-1}\) was slightly smaller than that of \(D_{2-1}\) due to the position of the flaw. Further analysis involved calculating the proportion of the amplitude difference to \(D_{2-1}\), which showed a relatively low level of difference. While the largest amplitude difference was observed for the 10 mm flaw, its impact within the range of uncertainty was still significantly lower than that of a one-unit increase in cross-sectional area. Therefore, this suggests that the impact of the flaw position on the response amplitude is relatively small within the experimental scope.
## 4 Results and Discussion
### On the Training Set  
#### 4.3.1 Learning Curves  
The performance of the proposed model under different data set sizes is now considered. Figure 14 shows the average performance over 20 independent runs in the testing set for the scenario RF-GAN when only a given percentage of faulty observations are available for training. More concretely, the following percentages were considered \(i=\)1, 2, 4, 6, 8, 10, 20, 60, 80, and 100 %. For instance, when \(i=4\%\), the number of training examples in each faulty state is \(14,000\times 0.04=560\).  
Figure 11: Recall indicators for the different scenarios: (**a**) RF-i; and (**b**) RF-b2; (**c**) RF-GAN; (**d**) RF-GAN1; (**e**) RF-GAN2; and (**f**) SMOTE.  
Figure 12: F1-score for scenario: (**a**) RF-i; and (**b**) RF-b2; (**c**) RF-GAN; (**d**) RF-GAN1; (**e**) RF-GAN2; and (**f**) SMOTE.  
Figure 13: The confusion matrix for: scenario: (**a**) RF-i; and (**b**) RF-b2; (**c**) RF-GAN; (**d**) RF-GAN1; (**e**) RF-GAN2; and (**f**) SMOTE.  
It can be seen that the average accuracy increased from 56.25% to 97.05% by increasing the availability of faulty data from 1% (140 examples) to 100% (14,000 examples) in each fault type. There was a strong increased in performance up to 20%; after that point, the improvement in accuracy was slower and slower until about 80%. After this value, the improvement was neglectable. That is, adding more data after a certain point hardly improves the performance.
## 3 Available Techniques to Reduce the Bandwidth of the 360-Degree Video
### Dynamic Adaptive HTTP Streaming (DASH) Framework
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Step** & **Process** \\ \hline Stitching & Stitch videos collected by many cameras/an omnidirectional camera onto diverse planar models such as cubic and affine transformation models match up the various camera images, merging and distorting the views to a sphere’s surface [3]. For successful coding and transmission, the 360-degree sphere is projected to a 2D planar format such as Cubic Mapping Projection (CMP) and Equirectangular Projection (ERP). \\ \hline Encoding and segmentation & The video file is segmented into smaller parts of a few seconds in length by the origin server. Each section is encoded in numerous bitrate or quality level variants. \\ \hline Delivery & The encoded video segments are sent out to client devices over a content delivery network (CDN). \\ \hline Decoding, rendering and play & Decodes the streamed data. With adaptive bitrate streaming, it plays the video and automatically adjusts the quality of the picture according to the network condition/user’s views at the client device. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Major steps in the DASH streaming process. Figure 4: Equirectangular projection (EMP) and cube map projection (CMP) comparison. Figure 3: DASH-OMAF architecture network. Clients can stream omnidirectional video from a DASH SRD or OMAF compliant server. The server will deliver segments with different viewport-dependent projections or independent tiles based on the choices of the client, which will be further discussed in the next sections. The client then downloads the appropriate segments, potentially discarding low viewing probability segments or downloads with lower quality to save bandwidth. Next, the features of HEVC of fast Field of View (FoV) switching allow the client to request the segments based on users' head movements in high quality [26], users can even zoom into the region of interest within the 360-degree video [27], providing a smooth user experience with minimal server-side changes. In recent years, some researchers have enhanced the Quality of Experience (QoE) of 360 videos streaming with the DASH architecture [28]. At any one point in a VR 360-degree movie, the user can at most see a portion of the 360-degree film. As a result, sending the entire picture wastes bandwidth and processing power. With the DASH-based viewpoint of adaptive transmission, these problems may be resolved. The client must pre-download the video material to ensure seamless playing, which needs the client predicting the user's future viewpoint. Based on HTTP 2.0, a real-time video streaming technology with low latency has been developed by [PERSON], [PERSON] [29]. The MPEG DASH prototype implements HTTP 2.0 server push functionality to actively deliver live video from the server to the client with low latency whereas [PERSON], [PERSON] [30] suggested an efficient adaptive VR video stream approach based on the DASH transport architecture via HTTP/2 that implements stream prioritization and stream termination.
# Effect of Nanofluids on Boiling Heat Transfer Performance
## 3 Effects of Nanofluid Physical Properties and Heated Surface on Boiling Heat Transfer
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nanoparticles** & **Base Liquid** & **Concentation** & **Heated Surface** & **CHF/HTC** & **Resasons** \\ \hline \multirow{4}{*}{Al-O\({}_{3}\)} & BG & 0.1, 0.2, 0.3 wt.\% & stainless steel cylinder & HTC deterioration & The surface average roughness is reduced and the number of bubble nucleation is reduced. \\ \cline{2-6}  & EG/DW & 0.05, 0.1, 0.25, 0.3, 0.75, 1 vol.\% & copper cylinder & HTC enhancement, when concentration is 0.75\%, HTC enhance 64\% & In addition to the increased properties of the base fluid, changes in the state of the heated surface are the main reasons. \\ \cline{2-6}  & RI4 lb & 0.001, 0.01 vol.\% & Microchannel surfaces & HTC enhancement & The low pressure causes an increase in the heat transfer coefficient. \\ \cline{2-6}  & RI4 lb & 0.001, 0.01 vol.\% & copper surface & HTC enhancement & The addition of SDS resulted in less deposition of nanoparticles. \\ \hline \multirow{4}{*}{CuO} & & 0.1, 0.2, 0.3 wt.\% & stainless steel rod & CHF enhancement & Nanoparticle deposition causes changes in surface roughness and vaporization core points. \\ \cline{2-6}  & Water & 0.01, 0.02 wt.\% & surface of rod heater & HTC enhancement & SDS significantly reduces surface tension. \\ \cline{2-6}  & Water & 0.1, 0.2, 0.3, 0.4 wt.\% & stainless steel cylinder & HTC enhancement & Surficatens increase surface wettability and contact angle of the bubbles with the surface is reduced. \\ \cline{2-6}  & & 0.001, 0.01, 0.05, 0.075, 0.2 wt.\% & flat heater plate & HTC enhancement & The deposited nanoparticles have a positive effect on heat transfer. \\ \cline{2-6}  & & pentane & 0.005, 0.01 vol.\% & circular brass surfaces & HTC enhancement & Surface trenches are the dominant condition for increasing nuclear boiling. \\ \cline{2-6}  & EG/DW & 0.1, 0.2, 0.3 wt.\% & cylindrical cartridge heater & HTC enhancement. When concentration is 0.5\%, HTC enhanced 55\% & Nanoparticle deposits alter surface roughness and wettability, changes bubble shape and and behavior. \\ \hline \multirow{4}{*}{Fe\({}_{2}\)O\({}_{3}\)} & & 0.02, 0.1 vol.\% copper surface & copper surface & CHF enhancement 26-37\% & CHF enhancement is due to the complete wetting behavior exhibited by nanoparticles. \\ \cline{2-6}  & Water & & & HTC deterioration is due to the increase in wettability and thickness of the porous layer structure. \\ \cline{2-6}  & & 0.05-1 vol.\% & cylindrical heater & CHF enhancement & Nanoparticle deposition causes an increase in surface roughness. \\ \hline \multirow{4}{*}{Fe\({}_{3}\)O\({}_{4}\)} & Water & 0.01, 0.05, 0.075, 0.1, 0.2, 0.4 vol.\% & cylindrical copper surface & When concentration is 0.1\%, the HTC enhanced 43\% & Surface characteristics have a significant effect on boiling heat transfer. \\ \cline{2-6}  & EG/DW & 0.01, 0.05, 0.1 vol.\% & horizontal Ni-Cr wire & CHF enhancement & The deposited layer increases surface wettability, resulting in significant CHF enhancement. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 5 Cone Beam Computed Tomography--Current Use in Orthodontics
The radiation beam falling on the tissues has the shape of a cone, and the examination allows for volumetric imaging of a given structure [29]. Both the lamp and the detector move around the patient. Pulse exposure, which is more often used in dentistry, helps to reduce the total radiation dose during one examination. In many aspects, cone beam tomography allows overcoming the limitations of classic CT [27; 28]. It allows, depending on the need, imaging of the entire head or only a small part of the masticatory system. A single dose of X-rays concentrated in cone beams is up to 15 times lower than in the case of regular CT, which is important when frequent irradiation is necessary to assess the progress of treatment, especially in young people, and importantly, the use of low-dose CBCT protocols does not affect the quality of the obtained images [40]. There are also protocols for the use of ultra-low-dose CBCT, e.g., for pre- and postoperative assessment of alveolar cleft in children, to protect them from the negative effects of frequent exposure to X-rays [75; 76; 77]. CBCT also allows viewing two-dimensional images simultaneously in the sagittal, oblique, or coronal plane [61]. Other advantages of CBCT include the following: significantly lower cost, significantly shortened examination time resulting from the possibility of obtaining a qualitative image during one rotation of the device using head stabilizers, and the possibility of multiple reproduction of images on different devices [24]. In endodontics, CBCT using a limited field of view (FOV) is used in the diagnosis of periapical pathologies and imaging of root canals, in implantology for planning implant placement, assessment of peri-implant fenestration and dehiscence, in periodonontology for the assessment of periodontal structures, and in maxillofacial surgery for assessment of the pathology of the third molars of the mandible and temporomandibular joint joints [78]. Another important application is the imaging of the airway patency, the obstruction of which may be one of the causes of some malocclusions and adenoidal facial defects, as well as sleep disorders and potentially dangerous obstructive sleep apnea [79]. Cone beam tomography, as one of the most important imaging methods in orthodontics and dentistry, is a branch of diagnostics that is worth and should be developed (Figure 5).
## 5 Results and Analysis
### Network Lifetime  
Different definitions of network lifetime exit. In this paper, we define the network lifetime as the period from when the nodes begin to transmit sensed data till the time when the network connectivity drops to 70%, considering that the lack of connectivity may cause severely ineffective transmission. Figure 11 shows the results of network lifetime versus different scanning angles. Obviously, the performance of RRP and DRS is superior to that of simple-link protocol under each angle. As mentioned before, the computed set of paths from sensor nodes to the BS in simple-link forms an oriented forest with roots in the CHs.  
Figure 11: Network lifetime _vs._ scanning angle.  
The death of a node will cause a disconnection between its offspring and the BS. The situation will get even worse if the dead node is a CH. However, on one hand, nodes in the DRS are isolated from each other by configuring the network as a star topology. On the other hand, those dying nodes in RRP are also separated from others in reconfigurable routing. The death of a node in both DRS and RRP will not lead to a tremendous drop in connectivity, which helps prolong the network lifetime. In the comparison with RRP and DRS, DRS only applies direct communication while RRP tries to slow down the energy consumption through combining direct and multi-hop communications. Eventually, RRP achieves about 51.85%, 49.29%, 30.51%, and 15.94% of network lifetime extension over DRS under the scanning angles of \(\dfrac{\pi}{6}\), \(\dfrac{\pi}{4}\), \(\dfrac{\pi}{3}\), and \(\dfrac{\pi}{2}\), respectively.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Machine Learning Algorithms  
We normalized the biosignals before generating the features to ensure that the absolute magnitudes of some variables did not bias the feature generation. After the features were generated, we normalized the feature variables to ensure each feature variable had equivalent weight in the principal component analysis for dimension reduction. Five different ML models were developed using the normalized latent variables, including the PS classification model, the APS classification model during SS activities, the APS classification model during TR activities, the APS classification model during SB activities, and the EE estimation model. Data were randomized and separated into a training (85%) and testing (15%) data set. The training data set is used for model development and hyperparameter optimization. The testing data set is used to report the final ML outcomes. Various ML algorithms were tested for classification, including, naive Bayes (NB), k-nearest neighbor (k-NN), decision tree (DT), ensemble learning (EL), support vector machine (SVM), and linear discriminant (LD). Additionally, Gaussian process regression (GPR) was used for EE estimation model development. The k-NN, EL, and SVM models achieve better accuracy relative to the other models. The use of neural network (NN) or deep learning (DL) techniques is an active research area, though these techniques typically require a sufficient number of training samples to appropriately determine the model parameters. Therefore, the performance of NN/DL approaches is limited ftoor the relatively smaller data sets employed in biomedical and health research. The algorithms used in this work are introduced briefly in the following subsections.
## 2 Interference Avoidance in Standard Protocols
Like WirelessHART, ISA100 uses TDMA along with collision avoidance mechanisms, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) and Clear Channel Assessment (CCA) [16], resulting in increased data transmission reliability. Before packets are transmitted, the transmitter listens on the channel on which it intends to transmit in order to assess if the channel is clear. If the channel is not clear, the transmitter backs off for a random amount of time, after which it attempts to retransmit the packet. Devices communicate according to various channel hopping techniques, with each subsequent transmission utilizing the next channel defined in the hopping sequence. Different devices use different offsets in the hopping sequence, resulting in interleaved hopping of devices operating in a wireless subnet. Every data packet needs to be acknowledged by the receiving device, and unacknowledged packets are retransmitted on a different channel over a different frequency. Hopping techniques include channel blacklisting and adaptive hopping. Channel blacklisting is a centralized decision made by the system manager, while adaptive hopping is a localized decision based on statistics of wireless parameters collected by each device. Adaptive hopping allows wireless devices to adapt their hopping sequences based on the quality of communication with specific neighbours.
## 2 Development of Fiber-Reinforced Mortar for Proposed Loop Joint
### Material Tests
#### 2.3.2 Test Results  
Figure 4 presents material test results: (a) is a picture of a test piece after a split test and (b) to (d) are the results of the material tests. The vertical axes in Figure 4b-d show the compressive strength, tensile strength, and static elastic modulus of mortar, respectively, while the horizontal axes show the fiber types and their volume contents. The blue and red bars show the test results of the mortar at 14 days and 28 days of age, respectively. Figure 4a shows a fracture interface in the test piece due to split tensile force. At the fracture interface, fibers were bridging two surfaces to resist the split tensile force--the cross-linking effect of the fiber reinforcement.  
Figure 4b shows that the compressive strength of all specimens was larger than 60 N/mm\({}^{2}\), and the difference in compressive strength between the 28-day and 14-day mortar samples was not significant. The split tensile strength of all specimens, as shown in Figure 4c, was larger than 6 N/mm\({}^{2}\), which was approximately twice that of the plain concrete. In addition, both the compressive and tensile strengths of same-age specimens with PVA fibers were relatively higher than those of the specimens with PP fibers. This is because the tensile strength of a PVA fiber is approximately twice that of a PP fiber. In particular, larger tensile strength of the fiber increased split tensile strength of fiber-reinforced mortar. Furthermore, Figure 4d shows that the modulus of elasticity of all specimens was in the range of 29-34 kN/mm\({}^{2}\), which was close to that of the plain concrete. On the basis of these material test results, mortar with 2.8 vol\(\%\) PVA fibers, which showed the highest compressive and tensile strengths, was selected as the material in the narrow loop joint in this study.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fiber Type** & **Diameter** & **Length** & **Tensile Strength** & **Elastic Modulus** & **Density** \\  & **(mm)** & **(mm)** & **(MPa)** & **(GPa)** & **(g/cm\({}^{3}\))** \\ \hline PVA & 0.66 & 30 & 900 & 23 & 1.3 \\ PP & 0.7 & 30 & 500 & 8 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of PVA fiber and PP fiber [29; 30].  
Figure 4: Material test results: (a) test piece after split test and material test results of fiber-reinforced mortar: (b) compressive strength; (c) split tensile strength; (d) modulus of elasticity.