esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Kinetic Scrolling: Kinetic Scrolling for Large Datasets on Mobile Devices
## 5 Results and Discussion
### Discussion  
We verified the usefulness of AKS through the results of the user study. AKS can aid users in accessing a faraway target through faster scrolling and in a more efficient manner. In this section, we will discuss a few points to be considered while applying our proposed system. We describe the case of applying AKS in the perspective of the characteristics of the system, the types of data used, the user behavior, and the costs to be incurred.  
First, as mentioned in Section 3, AKS is effective when a user is familiar with the data or if the data is organized in a standard format. In other words, the user must estimate the location of the target data. It is not appropriate to apply AKS if the user is unfamiliar with the item being scrolled or cannot predict the location of the specific item. For example, AKS is not appropriate for a news application that changes the position of an item over time. In such systems, it is not necessary to consider a use case in which a user rapidly accesses specific information. Before applying AKS, it is necessary to first understand the characteristics of such a system.  
AKS is a data-independent system that does not require additional preprocessing. Moreover, as we have observed in the result of **RQ. 3**, side effects will not occur with our method. We have identified the case where the target data is far away, based on users' scrolling behaviors, in order to prevent side effects. Based on this design, AKS is triggered only when the user intends to rapidly access specific information. Therefore, the application of AKS is effective regardless of the type of data.  
AKS yielded particularly meaningful results when users wished to access data farther away, as was the tendency observed in the experimental results. This is because AKS functions when the user scrolls rapidly and repeatedly without visual feedback to access faraway data. These results indicate  
Figure 8: Number of overshoots that occurred during scrolling tasks.  
that AKS will be most effective if the dataset that the user needs to navigate through is large and if the user frequently accesses faraway data.  
Applying AKS is not expensive from the implementation perspective. However, one aspect to consider is that as mentioned earlier, the clutch time value used to trigger the action is affected by the environment, and this value can be obtained through a heuristic process. Although this is not a complicated task, it is a labor-intensive process that developers and testers are required to perform manually. It would be effective to compare this cost with the positive outcomes of applying AKS when enhancing the system.
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)
### Mahalanobis-Taguchi System (MTS) Procedures
#### 2.2.3 Larger-the-Better SNR  
LTB is formulated as in Equation (11) below, where \(t\) is the abnormal conditions and \({D_{1}}^{2}\), \({D_{2}}^{2}\),..., \({D_{l}}^{2}\) is the MDs corresponding to the abnormal situations. The SNR (for the larger-the-better criterion) corresponding to \(q\)th run of OA is given as:  
\[SNR=\eta_{q}=-10 log_{10}\left[\frac{1}{t}\ \sum_{i=1}^{l}\left(\frac{1}{D_{i}^{2}} \right)\right] \tag{11}\]  
For each variable \(X_{i}\), SNR\({}^{1}\) represents the average SNR of level 1 for \(X_{i}\) while SNR\({}^{2}\) represents the average SNR of level 2 for \(X_{i}\) throughout the vertical columns of the OA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Number of Repetition**} \\ \hline
**Combinations** & **Col 1 \& Col 2** & **Col 1 \& Col 3** & **Col 1 \& Col 7** & **Col 3 \& Col 6** \\ \hline
2 & 1 & 2 & 2 & 2 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of repetitions of level combinations.  
Thus, positive gains from Equation (12) constitute useful features while negative gains constitute otherwise. Table 3 illustrates the assessment made using the SNR to evaluate significant factors of the \(L_{8}\) OA structure.  
\[Gain=SNR^{1}-SNR^{2} \tag{12}\]  
The optimized system is then re-evaluated with the abnormal samples to validate the effectiveness of assessing the discriminant power. Once confirmed, the optimized system is used for future applications in diagnosis, classification, or forecasting purposes. Figure 2 illustrates the summary of the fundamental stages in MTS. Note that it is in Stage 4 where the optimum threshold value (MD\({}_{T}\)) of the optimized system is obtained prior to future diagnosis or classification usage.
## Disclaimer of Endorsement  
References in this manuscript to any specific commercial products, processes or services or the use of any trade, firm or corporation name are for the information and convenience of the reader and donot constitute endorsement, recommendation or favoring by the US government or National Aeronautics and Space Administration.
# Simulation of Lid-Driven Cavity Flow with Internal Circular Obstacles
## 3 Results and Discussion
### Mesh Dependence Check: Mesh Size  
In order to determine the dependence of the cavity flow of the mesh size, the simulation was performed at \(Re=2000\) with the five meshes sizes of \(32\times 32,64\times 64,128\times 128,256\times 256\), and \(512\times 512\), as shown in Figure 5. In order to validate the current LBM model, the results were also compared with the reference data obtained using a commercial software, i.e., ANSYS Fluent. As shown in the figure, the streamlines in the cavity indicate the primary (large circulation in the middle) and secondary (small local circulations in the corner) vortices depending on the mesh size.  
Figure 4: Schematic of lid-driven cavity flow with internal obstacle of various sizes \(R/L=1/16\), \(1/6\), \(1/4\), and \(2/5\).  
Figures 6 shows the vertical distribution of horizontal velocities along the centerlines, with the aim of precisely comparing the velocity components at the centerlines. In each figure, the \(x\) and \(y\) coordinates depict the corresponding axis, and u and v represent the components of the horizontal and vertical velocities at \(x\) = 0.5 and \(y\) = 0.5, respectively. To observe the effect of the mesh size, meshes ranging from \(32\times 32\) to \(512\times 512\) were used. As shown in the results obtained using FLUENT, the larger the mesh size, the closer the lines stay to each other. It can be observed in the case of FLUENT, that with a mesh size of \(128\times 128\), the lines get almost close together. In contrast, the current calculation is almost consistent with the reference data, but there is a discrepancy in the case of the \(128\times 128\) mesh, which appears not to have converged. As shown in Figure 6, the convergence did occur with a \(256\times 256\) mesh size, and thus, taking into consideration the validation of the mesh dependence, a mesh size greater than \(256\times 256\) was used for the rest of this study.  
Figure 5: Mesh dependence check in terms of the size of the grid under the \(Re\) = 2000. The results were compared with those obtained using the commercial software ANSYS Fluent.  
Figure 6: Vertical distribution of horizontal velocities with various mesh sizes.
# Method for Measuring Cooling Efficiency of Water Droplets Imping onto Hot Metal Discs
## 4 Determining Cooling Efficiency
### Confirming Droplet Evaporation Regimes  
When studying Figure 6 in detail, it may be seen that at about 104 \({}^{\circ}\)C, there is an abrupt change in the water cooling efficiency versus temperature. This abrupt change corresponds to the transfer from film evaporation below 104 \({}^{\circ}\)C to nucleate boiling above 104 \({}^{\circ}\)C. The nucleate boiling regime in this case continued up to about 124 \({}^{\circ}\)C, which may be identified as the temperature of critical heat flux, which is also named the boiling crisis [7]. Above about 124 \({}^{\circ}\)C, the regime of transition boiling prevails to about 235 \({}^{\circ}\)C. Above 235 \({}^{\circ}\)C, the droplets experienced film boiling, i.e., the Leidenfrost temperature for the aluminum discs was in this case about 235 \({}^{\circ}\)C. This result corresponds well with results reported by other researchers, e.g., the literature review conducted by [PERSON] and [PERSON] [17] with a reported Leidenfrost temperature for aluminum in the range of 235-265 \({}^{\circ}\)C.  
Figure 6: Droplet cooling rate (**blue**) and heat absorption potential (**black** dashed line) as a function of temperature. The temperature at the maximum cooling rate is marked (**red**).  
Figure 7: Dimensionless cooling efficiency (**blue**) and dimensionless cooling efficiency related to the disc temperature (**black** dashed line). The temperature of the maximum cooling efficiency is marked (**red**). Data from Figure 6.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
###### Abstract  
This research proposes a ground-breaking technique for protecting agricultural fields against animal invasion, addressing a key challenge in the agriculture industry. The suggested system guarantees real-time intrusion detection and quick reactions by combining cutting-edge sensor technologies, image processing capabilities, and the Internet of Things (IoT), successfully safeguarding crops and reducing agricultural losses. This study involves a thorough examination of five models--Inception, Xception, VGG16, AlexNet, and YoloV8--against three different datasets. The YoloV8 model emerged as the most promising, with exceptional accuracy and precision, exceeding 99% in both categories. Following that, the YoloV8 model's performance was compared to previous study findings, confirming its excellent capabilities in terms of intrusion detection in agricultural settings. Using the capabilities of the YoloV8 model, an IoT device was designed to provide real-time intrusion alarms on farms. The ESP32 cam module was used to build this gadget, which smoothly integrated this cutting-edge model to enable efficient farm security measures. The incorporation of this technology has the potential to transform farm monitoring by providing farmers with timely, actionable knowledge to prevent possible threats and protect agricultural production.  
Keywords:agricultural security; animal detection; computer vision; crop protection; early warning system; IoT; intrusion detection; image processing; machine learning; sensor technology; smart farming; wildlife intrusion; Yolo V7 +
Footnote †: journal:  
_Article_
## 2 Theoretical Model
### Back-Calculated Defect Radius  
Assuming the leakage consists of a defect with the equivalent diameter \(d_{aq}\), the following equation applies:  
\[\frac{dP_{g}}{dt}=\text{PDR}=\frac{\Delta P_{g}}{\Delta t} \tag{13}\]  
The above equation can then be transferred as follows:  
\[d_{eq}=\alpha\sqrt{\frac{4*\text{PDR}*V_{g}}{0.5*\chi*Pg*\pi*\mu*\sqrt{\frac{2 (P_{g}-P_{f})}{\rho_{s}}}}} \tag{14}\]  
where \(P_{f}=P_{atm}+\rho_{l}gH+P_{e}\), \(\alpha\) is the size-modified coefficient, which needs lots of experiments to collect and determine for each membrane module.  
From this equation, for a certain membrane system, parameters such as the volume and the pressure of the membrane gas chamber, PDR, the defect-metering coefficient, the polytropic coefficient, the water depth on the membrane, water density, and the elastic pressure will be known factors, so the membrane defect size can be calculated.  
Here, PDR is measured in the membrane integrity test for used or damaged membrane (Pa\(\cdot\)s\({}^{-1}\)); \(p_{g}\) changes in the applied test pressure (\(Pa\)) during the experiment time interval (\(\Delta t\)). The other parameters to be measured are \(V_{g}\), the hold-up volume or pressurized volume (\(L\)), and the water depth on the membrane (\(H\)).
## 4 Conclusions  
A radial compressor with test results available in the literature was designed, and numerical analysis was performed using the package program. It has been observed that the current test results and the numerical analysis results are pretty close. There is a maximum error rate of 1.98% and a minimum of 0.35% between the numerical analysis and experimental results.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Mass Flow Rate (kg/s)** & **Tandem Opt. Pr.** & **Tandem Pr.** & **Conventional Blade Pr.** \\ \hline
0.341 & 1.4499 & 1.4493 & 1.41 \\
0.377 & 1.4465 & 1.4448 & 1.4018 \\
0.509 & 1.441 & 1.4371 & 1.3691 \\
0.622 & 1.427 & 1.4153 & 1.3383 \\
0.789 & 1.3945 & 1.3896 & 1.2999 \\
0.969 & 1.3525 & 1.35 & 1.2397 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Flow rate and analysis results.  
Figure 17: Optimum design geometry.  
Figure 18: Comparison of analysis results.  
After seeing that the test results of the conventional blade radial compressor in the literature were compatible with the results of our numerical analysis, we performed the analysis of our tandem blade radial compressor with four input parameters. Design points were created within the determined lower and upper limits. Three different optimum designs with the highest output parameters, total pressure ratio, and efficiency were obtained using the MOGA optimization method. With this method, 4000 design points were initially created to find the optimum design, and optimum designs were obtained in a total of 20 iterations, each containing 800 design points. Numerical analyses were carried out according to design point one of these obtained design points. The parameter values of the design point one with the highest performance were found to be theta angle \(76.166^{\circ}\), X value 1.001 mm, clocking C angle \(5.0217^{\circ}\), and L value 15.005 mm. The efficiency of this design point 1 was found to be 94.61 and the total pressure ratio was 1.3985. As a result of these analyses at different flow rates, it has been seen that the performance of our optimum TB centrifugal compressor is better than the TB and conventional blade radial compressor.  
Methodology, M.E.S., R.B. and Z.P.; Validation, M.E.S.; Writing--original draft, M.E.S.; Writing--review & editing, R.B. and Z.P.; Supervision, M.E.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Privacy and Security Issues in Online Social Networks
## 3 Privacy and Security Threats in OSNs
### Modern Threats
#### 3.2.7 Location Leakage  
The location-leakage threat is a type of data leakage. There is a trend for various users to access a social network through mobile devices. Usually, apps are used to access an online source through a mobile device. The use of mobile devices for online access introduces the new privacy threat of location leakage. The use of mobile devices for online access encourages users to share their location information [40]. Thus, the revealing of geographic data on social-networking sites may be used by attackers to harm users.
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 4 Description of the Missions and Tasks Register and Reporter (MTRR)
### Activities Performed by the MTRR
#### 4.2.3 Receiving and Recording Environmental Reports  
The functional overview for processing an environmental report is shown in Figure 18. The flow is similar to that for the mission status reports. As before, it starts when the vehicle publishes data, in this case environmental data from the onboard sensors, to the DDS topic for environmental information (step 1). This time the Environmental Reporter component is the one that is subscribed to that topic. Consequently, it receives the published environmental data (step 2), generates an environmental report from the received data, and sends the report to the MTRR (step 3). Finally, the MTRR processes the environmental report and stores the information in the SWARMs repository through the Semantic Query (step 4).  
Figure 17: Sequence diagram for the task report method in the MTRR.  
Figure 18: Functional overview of the processing of an environmental report.  
When the MTRR receives an environmental report it stores the data from the report in the historical database and in the ontology using the interface provided by the Semantic Query. It also checks if the report includes salinity concentration information, as it requires a specific method in the interface to the Semantic Query. The full sequence diagram for this method is shown in Figure 19.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## Methods
### Performance of MIMO-GFDM  
In this section, the performance of GFDM MIMO for symbol error rate (SER) forms was evaluated. MIMO-GFDM system simulation is performed with predistorters using Matlab according to Equation (10). The system used OQAM on the AWGN channel. SER for the GFDM/OQAM system through the AWGN channel was as follows [34]:  
\[p_{AWGN}(e)=2\bigg{(}\frac{k-1}{k}\bigg{)}erfc\bigg{(}\sqrt{\gamma}\bigg{)}+- \bigg{(}\frac{k-1}{k}\bigg{)}erfc^{2}\bigg{(}\sqrt{\gamma}\bigg{)}. \tag{22}\]  
SNR system GFDM/OQAM has the following equation:  
\[\gamma=\frac{3R_{T}}{2(2^{\mu}-1)}.\frac{N_{S}E_{s}}{\zeta N_{o}} \tag{23}\]  
and  
\[R_{T}=\frac{KM}{KM+N_{CP}+N_{CS}}. \tag{24}\]  
\(\mu\) is the number of bits per symbol QAM, \(p=\sqrt{2^{\mu}}\), \(N_{CP}\) is the length of the cyclic prefix and \(N_{CS}\) cyclic suffix, \(K\) and \(M\) represent the number of subcarriers and subsymbol, \(E_{s}\) is the average energy per symbol, and \(N_{o}\) is the noise power density. Factor \(N_{s}\) has a value of 2 for OQAM and 1 for QAM.  
Meanwhile, the SNR for GFDM/OQAM systems with HPAs from Equation (19) is:  
\[\gamma=\frac{|K_{o}|^{2}|H|^{2}\sigma_{x}^{2}}{|H|^{2}\sigma_{d}^{2}+\sigma_{n }^{2}} \tag{25}\]
# Application of Compressive Sensing in the Presence of Noise for Transient Photometric Events
## 3 Noise in Compressive Sensing Measurements
### Total Noise in Detectors  
Since random, uncorrelated noise adds quadratically, total detector noise, \(n_{st}\) is given by  
\[n_{st}=\sqrt{n_{s}^{2}+n_{t}^{2}} \tag{11}\]  
where \(n_{s}\) is shot noise, and \(n_{t}\) is thermal noise. Incorporating all the noise sources, CS can be modeled as  
\[y=A(x+n_{b})+n_{st}. \tag{12}\]  
Total noise is  
\[N=An_{b}+n_{st}. \tag{13}\]  
In our simulations, we model shot noise.
## 2 Methodology
### Real-Time Robust PPP Implementation  
The real-time detection of cycle slips usually takes the TurboEdit algorithm, which uses the differences between epochs of both the geometry-free and Melbourne-Wubbena (MW) observations to identify cycle slips [35]. Because the differences are influenced by the observation interval and the elevation angle, the empirical thresholds are generally determined by some tests in advance [36]. Since it is difficult to detect all possible cycle slips and some outliers occur inevitably, especially for observations at low elevation angle, the PPP estimation would be abnormal without the robust control. As a result, the unit weight STD and the maximum residual are larger than the theoretical observation noise. Therefore, the unit weight STD can be used as the overall quality control and the maximum residual is imposed simultaneously to identify all possible outliers. The criteria of the quality control can be expressed as  
\[\begin{split}\partial_{STD}<\sigma_{0}\\ \max|V|<c\partial_{STD}\end{split} \tag{12}\]  
where \(\sigma_{0}\) is the experience value of the unit weight STD; and \(c\) is usually chosen as 3.0-6.0 [37]. In our experiments, \(\sigma_{0}\) is set as 0.020 m and \(c\) uses 3.0 based on the observation noise and the model error.  
After the PPP estimation without quality control is finished at one epoch, the normal equation and a posteriori residuals are available. The observation corresponding to the maximum residual will be identified according to Equation (9) if the unit weight STD or the maximum residual does not satisfy the condition in Equation (12). According to Equation (10), the influence of this abnormal observation will be offset against the normal equation by the use of one additional observation with the opposite weight. The influence of the unmodeled error can be easily eliminated without the complicated model assumption. After one outlier is removed, the unit weight STD and the residual are recomputed with the updated normal equation. The above procedure is carried out through adding the identified observation with opposite weight one by one until the condition is satisfied. The PPP robust estimation is realized at this epoch and the procedure can be repeated for next epoch. This quality control method is implemented into our real-time PPP software package, which was designed for real-time applications with the C++ programming language. The flow chart of the quality control using the additional observation with opposite weight and the real-time robust PPP implementation is presented in Figure 1.  
Figure 1: The flow chart of the quality control using the additional observation with opposite weight and the real-time robust precise point positioning (PPP) implementation (Eq.: Equation; STD: standard deviation; and the dashed lines denote data flow).
# Authentication Securing Methods for Mobile Identity: Issues, Solutions and Challenges
## 2 Mobile Authentication Method and Issues
### Trip Trajectory Authentication  
Different mobile phone users will have different personal trip trajectories and stay points. This type of authentication determines the validity of the present mobile device user based on his trajectory data [9]. The travel trajectory's system architecture is depicted in Figure 3, which includes the registration and authentication phases.  
In the registration phase, a user registers his information, comprising his home and workplace coordinates and trip routes, in the template library, which is dynamically expanded to accommodate the user's trip regularities. While in the authentication phase, the data gathering module collects the user's daily travel trajectories using GPS-enabled mobile devices, calculating the similarity between the sample and template trajectories in order to determine likely stay point coordinates. The results of this calculation will be used to judge whether the user is valid or not.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Usability**} & \multicolumn{2}{c}{**Culture**} & \multirow{2}{*}{**Performance**} \\ \cline{2-3} \cline{5-5}  & **Condition** & & **Impact** \\ \hline \multicolumn{3}{c}{**FINGERPRINT RECOGNITION**} \\ \hline \multirow{2}{*}{Most comfortable and fastest} & Sweating/Dry skin & Low & High \\  & Change of fingerprint structure & High & Low \\ \hline \multicolumn{3}{c}{**FACIAL RECOGNITION**} \\ \hline \multirow{2}{*}{Unpredictability of facial appearance (e.g.,: facial expressions)} & Wearing Hijab & Medium & Medium \\  & Make up & Medium & Medium \\  & Wearing glasses & Low & High \\ \hline \multicolumn{3}{c}{**IRIS/EYE RECOGNITION**} \\ \hline Cause of discomfort (e.g.,: proximity close to camera) & Wearing contact lenses & Low & High \\  & Eye blinks & Medium & Medium \\ \hline \multicolumn{3}{c}{**Impact**} \\ \hline \multirow{2}{*}{Low—affecting or altering the environment as little as possible Medium—affecting or changing the environment as much as possible High—unalterable} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact of change on biometrics [14].  
Additionally, using this type of authentication makes it hard to validate users who love to travel a lot. The frequent changes in user coordination and stay points will make it hard to authenticate.