esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Tennis Ball Detection Algorithm Based on Robomaster EP
## 2 Related Work
### Tennis Ball Detection and Identification  
There are mainly machine learning, image processing, and deep-learning-based methods for ball detection and recognition. Machine learning methods are too complex, as balls generally have only two features, color and shape, requiring more training time. The training process is relatively complex and not suitable for tennis ball detection. Image processing methods mainly use computer vision or image sensors to process images and apply target-ranging algorithms to identify tennis balls through digital image processing techniques [18] for ranging. Standard methods include the detection of tennis balls based on the Hough transform, developing tennis ball recognition based on OpenCV [19], and using vision sensors to identify tennis balls. For example, in the image processing workflow using toolboxes such as OpenCV, the image is pre-processed, features are extracted using a correlation algorithm, different radius thresholds are set to filter the target, and finally the contour features are used to determine whether it is a tennis ball. Compared to the manual ball-picking method, the detection efficiency of the image-processing-based method is improved. However, it is affected by different detection scenes, periods, and tennis ball occlusion. For example, different detection scenes and periods interfere with tennis ball identification to a certain extent. The change of light causes some target features to be challenging to extract. The phenomenon of tennis ball occlusion leads to missed and false detection.  
Compared to machine and image processing methods, deep-learning-based methods can achieve accurate detection and recognition of tennis balls. [PERSON] et al. [20] proposed a model based on AlexNet [21] and SSD for tennis ball recognition, using deep learning to divide the image recognition into two steps and the AlexNet network model to test whether there is a tennis ball in the image.If there is a tennis ball, the method uses an SSD network model to locate the tennis ball; otherwise, it uses AlexNet to continue checking the following image. [PERSON] et al. [22] proposed a deep-learning-based tennis ball collection robot that used the YOLO model for tennis ball recognition and implemented it on an NVIDIA [23] Gitzo TX1 board. However, the traditional model has many parameters, extensive computation, low detection accuracy, and complex model structure, which could be more conducive to porting to embedded devices.
# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
## 6 Experimental Analysis and Results
### Experimental Results and Analysis
Similarly, it has95.95-82.32% of normal TPR, 95.75-81.42% of attack TPR, 4.05-18.232% of normal FPR, and 4.25-18.58% of attack FPR for ascending order of attribute sizes (from 10-115) of the dataset Kitsune [53]. Its performance also decreases proportionately with the increase in the dimension of the datasets. * [PERSON] et al. 's algorithm [35] is the most efficient in comparison with all the aforesaid algorithms. It has 98.21-96.25% of normal TPR, 96.21-93.25% of attack TPR, 2.12-3.02% of normal FPR, and 3.79-6.75% of attack FPR for ascending order of attribute sizes (from 10-42) of the dataset KDDCUP'99 [52]. Similarly, it has 98.21-90.44% of normal TPR, 96.21-89.33% of attack TPR, 1.79-9.56% of normal FPR, and 3.79-10.67% of attack FPR for ascending order of attribute sizes (from 10-115) of the dataset Kitsune [53]. Its performance also decreases proportionately with the increase in the dimension of the datasets. * The proposed algorithm (IFRSCAD) has 98.342-96.99% of normal TPR, 98.04-96.29% of attack TPR, 1.658-3.01% of normal FPR, and 1.96-3.71% of attack FPR for ascending order of attribute sizes (from 10-42) of the dataset KDDCUP'99 [52]. Similarly, it has 98.342-91.989% of normal TPR, 98.04-91.289% of attack TPR, 1.658-8.011% of normal FPR, and 1.96-8.711% of attack FPR for ascending order of attribute sizes (from 10-115) of the dataset Kitsune [53]. Its performance also decreases proportionately with the increase in the dimension of datasets. It is clear from the data that the proposed algorithm has more TPR and less FPR. The difference between normal TPR and attack TPR and normal FPR and attack FPR is also less in comparison with other methods. The performance decrement is less with the increase in dimensions. Obviously, the IFRSCAD has more average TPR and less average FPR than others. * In addition, the execution time of the IFRSCAD depends upon two factors, namely, dimension and size of the datasets. It was found that if the dimension is kept constant, the algorithm has quadratic execution time, whereas if the data size is kept constant, it runs in linear time. Therefore, the proposed algorithm's time complexity is more dependent on the data size than the number of attributes. The time-complexity graphs for constant dimension and constant data size are given, respectively, in Figures 14 and 15. Furthermore, the IFRSCAD's time-complexity is also analyzed against that of MCAD [30]. If the dimension of the dataset is assumed to be constant, the MCAD [30] runs in cubic time and IFRSCAD runs in quadratic time. Thus for large data size, the IFRSCAD outperforms MCAD [30]. The comparative analysis is presented graphically in Figure 16 below.
## 2 Methodologies  
Deep learning techniques, which possess good capabilities in automatically learning pertinent features embed in data, are examined in this study to forecast PV power output. Table 1, summarizes the pros and cons of the seven considered benchmark deep learning architectures: RNN [39], LSTM [40], GRU [41], Bi-LSTM [42], ConvLSTM [43], SAE [44], RBM [45,46], and VAE [20,47].  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Model** & **Description** & **Key Points** \\ \hline \multirow{4}{*}{**Model**} & \(\bullet\) & RNNs are able to include historical information in the forecasting process via their recurrent structure and memory units & \(\bullet\) \\  & \(\bullet\) & Simple RNN do not have gates [39]. & \(\bullet\) \\  & \(\bullet\) & The RNNs are entirely trained in a supervised way. & \(\bullet\) \\ \hline \multirow{4}{*}{**LSTM**} & \(\bullet\) & LSTM consists of three gates regulating the information flow called input, forge, and output gates [40]. & \(\bullet\) \\  & \(\bullet\) & Gate mechanism is used to store and memorize historical data features. & \(\bullet\) \\  & \(\bullet\) & GRU use two gates, while LSTM is based on three gates. & \(\bullet\) \\ \hline \multirow{4}{*}{**LSTM**} & \(\bullet\) & The major demarcation of GRU from LSTM is that only one unit is used to control both the forgetting factor and the decision to update the state unit [41]. & \(\bullet\) \\  & \(\bullet\) & GRU contains only two gates, the update, and the reset gates. & \(\bullet\) \\  & \(\bullet\) & The GRU has been widely used in time-series, data sequence (e.g., speech and text processing), temporal features extraction, prediction, and forecasting. & \(\bullet\) \\ \hline \multirow{4}{*}{**Model**} & \(\bullet\) & Compared to the LSTM model that passes the input data through the network in one direction from past to future (forward), the BiLSTM processes the input also in the backward direction from the future to the past [42]. & \(\bullet\) \\  & \(\bullet\) & This architecture improves the learning of complex temporal dependencies through double processing. & \(\bullet\) \\ \hline \multirow{4}{*}{**BiLSTM**} & \(\bullet\) & The ConvLSTM is a special variant of the traditional LSTM, in which the fully-connected layer operators are replaced with convolutional operators [43]. & \(\bullet\) \\  & \(\bullet\) & LSTM with recurrent connection to deal with data sequences. & \(\bullet\) \\ \cline{1-1}  & \(\bullet\) & The convolutional layer can deal with 2D inputs like a sequence of images. & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The considered benchmark deep learning methods.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 3 UAV-Assisted Emergency Monitoring and Response System
### System Protocols
#### 3.3.1 Day to Day Monitoring  
The temperature in each room is constantly monitored by a smart sensor attached near the entrance of the room. To maximize efficiency, a smart sensor only transmits a temperature value to the control server if the temperature value changes. The server stores the received temperature value and checks whether it is over a certain range of temperature. If the temperature value goes beyond a certain range, the server broadcasts the message \"Unsafe\" to an indoor drone located in the same floor, a security manager and all smart sensors in the building. The drone is then directed to the location of the smart sensor that sent the high temperature value to take a photo and video of the zone. Then, the drone sends the photo and video to the control server. The security manager can access the server and monitor these images to determine whether or not the situation is abnormal. If it is an abnormal situation, the \"Abnormal Situation Response Process\" is engaged. If the situation is safe, then the server broadcasts the message \"Safe\" to the indoor drone, smart sensors and a security manager's smartphone. In the absence of any \"Unsafe\" messages, all parts of the system continue with their regular daily monitoring duties.
# The Role of Electron Trajectories in XUV-Initiated High-Harmonic Generation
## 1 Introduction
In contrast to tunneling-driven HHG, here the ability to accurately control the XUV-IR delay allows us to scan the variation of the reciprocal intensity parameter within the laser cycle, following its dependence on the length of the underlying trajectory. Our experimental findings are supported by two theoretical models, the semiclassical Coulomb-corrected three-step model (CCTSM) and a numerical integration of the time-dependent Schrodinger equation (TDSE). Figure 1: Principle of extreme ultraviolet (XUV)-initiated high-harmonic generation (HHG). **(a)** The absorption of an XUV photon (violet arrow) promotes an electron to the vicinity of the ionization threshold \(I_{\mathrm{p}}\) (step 1). A strong infrared (IR) field drives the electron on a recollision trajectory (step 2), leading to the emission of an XUV photon with higher energy (pink arrow; step 3). **(b)** Experimental setup. An IR laser pulse (red waveform) is focused into a gas cell filled with xenon, producing an attosecond XUV pulse train (violet waveform). A thin aluminum filter spatially separates the XUV and IR fields. A two-segment mirror controls the delay between the two fields and refocuses the beams into the target gas cell, filled with helium. The resulting XUV-initiated high harmonics (pink waveform) are spectrally analyzed in an XUV grating spectrograph.
# Using Machine Learning and Candlestick Patterns to Predict the Outcomes of American Football Games
## 4 Experimental Results and Discussion
### Discussion and Limitations  
The overall performance of the classification-based model was not better than the comparison group, and the best method had just one more correct case. The model seems to give much weight to the input value of LD (t + 1), and its value is close to the actual result. However, the candlestick behaviour can only provide a little adjustment and correction ability according to LD (t + 1). Moreover, the classification-based model reduces the game results to a binary output regardless of the scores of both teams. Regardless of the degree by which the score leads, with a lot of lead or little lead, the result is a win. Given that this study's hybrid numerical scales and categorical variables are treated as input, the classification-based model is less able to distinguish the score's degree of leading.  
To deal with the problem of forecasting winning/losing, the crucial components are still the numerical scores and the margin of the two teams. Regression models are used to deal with numerical variables, and the predicted values are converted into win/loss. The experimental results of this study also verify that the overall accuracy of the regression-based model is better than the classification-based model and the comparison group. Many studies on win/loss prediction in NFL use a similar approach and deal with numerical variables. Examples have used the win probability [1] of two teams as the output or calculated the rating scores and ranking [3; 4; 5] of two teams. Finally, the two teams can be compared and the outcome decided.  
Moreover, the results of the experiment show that the accuracy of each team is different for the forecasting based on candlesticks and the betting comparison group, which is similar to the phenomenon of technique analysis based on the candlestick chart in the stock market. The candlestick chart of the stock composite index is used to determine the overall behaviour and common pattern. When it is applied to individual stocks for predictions, it produces a different accuracy. Although the behaviour of a sports candlestick is not as rich as that of the stock market, its pattern is not obvious and not easy to interpret. However, it can still be used in the prediction model to reflect the past behaviour of the time series to provide a valuable reference.  
Regarding the experimental results of this study, although the best prediction performance is only slightly better compared with the betting market, similar studies have not always presented better performance. As mentioned in the literature, predictive accuracy varies from sport to sport, and an accuracy of near 70% in this study would be promising and acceptable in the case of the NFL.
## 5 Other Synthetic Applications of Complex [PtH{(PMe2O)2H}(PMe2 OH)]  
The synthetic utility of the Parkins catalyst [PtH{(PMe2O)2H}(PMe2 OH)] **(1)** is not restricted to the catalytic hydration of nitriles. Using this complex, other amide-bond forming reactions could be developed. In this context, [PERSON] and co-workers found that **1** efficiently catalyzes the direct conversion of unactivated nitriles to _N_-substituted amides by reaction with both primary and secondary amines in the presence of water (Scheme 18) [77]. A mechanism involving the initial formation of an amidine intermediate, which subsequently hydrolyzes into the amide, was proposed. Indeed, when the reactions were performed in the absence of water, the corresponding amidines could be isolated.  
**Scheme 18.** Hydrolytic amidation of nitriles catalyzed by [PtH{(PMe2O)2H}(PMe2 OH)] **(1)**.  
An interesting extension of this amidation process is the quantitative formation of 2-ethyl-2-oxazoline **72** from the reaction of propionitrile with 2-aminoethanol via ring-closure of the intermediate amide **71** (Scheme 19) [77].  
**Scheme 19.** Catalytic synthesis of 2-ethyl-2-oxazoline.  
Although scarcely explored, the ability of complex **1** to promote the hydration of thiocyanates was also demonstrated [78,79]. As a representative example, treatment of the optically active thiocyanate **73** with 25 mol% of **1** in a THF/H\({}_{2}\)O mixture led to the high-yield formation of thiocarbamate **74** (Scheme 20). Remarkably, in marked contrast with the use of classical acidic hydrolysis conditions, which induced the complete racemization at the stereogenic carbonyl \(\alpha\)-carbon, the reaction proceeded without loss of chirality.  
**Scheme 20.** Catalytic hydration of an optically active thiocyanate.  
On the other hand, in the presence of catalytic amounts of [PtH{(PMe2O)2H}(PMe2 OH)] (**1**), separable mixtures of 1-ethoxysiquinolines **76** and isoquinolones **77** were synthesized by intramolecular 6-_endo-dig_ cyclization of _ortho_-alkylbenzonitriles **75** in ethanol (Scheme 21) [80]. In these reactions the Parkins catalyst activates not only the nitrile unit, but also the alkyne moiety of the substrates. The use of other alcohols, such as methanol, _iso_-propanol, or cyclopentanol, also led to mixtures of the corresponding 1-alkoxyisoquinolines and isoquinolones **77**.  
**Scheme 21.** Platinum-catalyzed intramolecular cyclization of _ortho_-alkylbenzonitriles.  
In an independent patented work, a more selective transformation of _ortho_-alkylbenzonitrile **78** was observed [81]. Thus, as shown in Scheme 22, the treatment of this substrate with 2 mol% of platinum complex **1** in refluxing methanol resulted in the exclusive formation of isoquinoline **79**.
# Institutional Adoption and Implementation of Blended Learning in the Era of Intelligent Education
## 4 Results and Discussion
### Improvement of Students' Learning Performance  
#### 4.3.1 Effect in Improving Learning Performance  
Due to the space limit, the evaluation results of 30 BL courses and 30 non-BL courses were randomly selected for comparison. Results are shown in Table 5.  
As is indicated in Table 5, according to the survey results in the fall semester of 2019, the total mean score of \"innovation, higher-order, challenge\" for the BL courses in 2019 were 0.017 points higher than that of the non-BL courses, without a significant difference (\(p=0.593>0.05\)). However, the total mean score of the BL courses in 2020 was 0.0495 points higher than that of the non-BL courses, showing a significant difference (\(p=0.032<0.05\)). Meanwhile, the \"innovation\" of the BL courses outweighed that of the non-BL courses with a significant difference (\(p=0.002<0.05\)). The \"challenge\" of the BL courses outweighed that of the non-BL courses with a significant difference (\(p=0.045<0.05\)) as well. It can be inferred that the students had a positive perception of the BL courses in developing their ability.  
The result of this part is in alignment with previous research findings that BL is effective in improving learning performance [28; 29; 30] or at least enjoys equivalent learning outcomes with conventional classroom learning [26].
# Elastic Slow Dynamics in Polycrystalline Metal Alloys
## 5 Effects of Variation of the Microstructure  
### Distributed Damage  
Effects of temperature and distributed damage were analysed on the Aluminum sample (Alu-A01). Three samples with identical geometry and properties were considered. Samples Alu-A01 and Alu-A02 are intact. The former has been tested at room temperature and at a controlled temperature equal to 35 degrees. Results reported in Figure 10 indicate a shift of the distribution towards higher relaxation times for higher temperature (compare pink and green curves in subplot (a)). The variation is not a fluctuation, since repeating the measurement on an identical sample at the higher temperature the same distribution is found (compare blue and green curves). Note that the shift is not incompatible with establishing a link between relaxation times and grain sizes, rather it seems to suggest that grains of the same size relax faster at ambient temperature, i.e., confirming the role played by thermal effects during relaxation.  
Sample Alu-A02 was then damaged (the damaged sample labelled as Alu-A02d), by treating it with a droplet of gallium, which is known to interact with aluminum producing distributed disgregation at the grain boundaries [55]. The resulting distribution of relaxation times is identical to that of the intact sample, except in magnitude. As expected, slow dynamics is almost one order of magnitude higher in the damaged sample than in the intact one. The peak position and width are however not influenced by the damage, which thus could be assumed simply to enhance the density of defects already present at the grain boundaries of the intact sample, but not inducing cracks or other features with different space scale.  
Figure 9: Indicators quantifying the evolution of the velocity variation in time during relaxation as a function of the conditioning amplitude: (**a**) \(\Delta_{M}\) is the velocity variation at \(t=0\); (**b**) \(T_{d}\) is the time at which \(\delta c/c=0.5\Delta_{M}\); (**c**) \(m\) is the slope of the curve in the log space around \(t=T_{d}\), i.e., in the logarithmic recovery phase; (**d**) parameteric plot of \(m\) vs. \(\Delta_{M}\).  
In subplot (d), the parametric plots of slope \(m\) vs. jump \(\Delta_{M}\) are shown for the studied samples, where we recall that the two parameters describe the evolution of velocity vs. time during relaxation. The linear correlation between the two parameters found for intact samples is also maintained in the case of damaged samples and the correlation coefficient is not affected by the presence of damage or by changes in temperature.
## 5 Problem Formulation of Data Collection Scheduler  
A polling cycle of a group \(T_{i}\) consists of \(K_{i}\) QFs where  
\[K_{i} = T_{i}/\Delta T_{QF} \tag{4}\] \[= T_{i}/(\Delta t_{s}\times\sum_{k=1}^{\tau_{g}}N_{k})\] (5) \[= T_{i}/(\Delta t_{s}\times N_{all}). \tag{6}\]  
Let \(C_{i,j}\) be a total number of combinations of selecting QFs which any node \(j\) in any group \(i\) uses for data transmit and its retries\(R_{i,j}\) is  
\[C_{i,j}=K_{i}\times\sum_{m=1}^{K_{i}}{}_{(K_{i}-m)}C_{min(K_{i}-m,R_{i,j})}, \tag{7}\]  
where \(min(K_{i}-m,R_{i,j})\) is the smallest number of the two arguments.  
Figure 5: A composition of a ScheduleFrame (SF).  
Total number of combinations of selecting QFs which all nodes in any group \(i\) use is  
\[C_{i,all}=\prod_{j=1}^{N_{i}}C_{ij}. \tag{8}\]  
Then, total number of combinations of selecting QFs for all groups and all nodes \(C_{all,all}\) is  
\[C_{all,all}=\prod_{i=1}^{\tau_{g}}(C_{i,all})^{\frac{\tau_{g}}{T_{i}}}. \tag{9}\]  
The number \(C_{all,all}\) depends on the number of nodes \(N_{all}\) in an IWSN, the number of collection cycles \(\tau_{g}\), communication quality, i.e., the number of total communication retries \(R_{i,j}\).  
On the other hand, maximum number of polling queries for periodic data collection of group \(i\) during a polling cycle \(T_{i}\) is\(\sum_{j=1}^{N_{i}}(1+R_{i,j})\). Let \(Q_{i,x}\) be total polling queries of the \(x\)th QF of a group \(i\) during \(T_{i}\). When the queries are uniformly generated during \(T_{i}\), \(Q_{i,x}\) is approximated by the Equation (10).  
\[Q_{i,x}\approx\frac{1}{K_{i}}\sum_{j=1}^{N_{i}}(1+R_{i,j}) \tag{10}\]  
The total polling queries of \(x\)th QF of all groups is  
\[Q_{all,x} = \sum_{i=1}^{\tau_{g}}Q_{i,x} \tag{11}\] \[\approx \sum_{i=1}^{\tau_{g}}\frac{1}{K_{i}}\sum_{j=1}^{N_{i}}(1+R_{i,j}). \tag{12}\]  
Then, we find an optimal schedule from \(C_{all,all}\) patterns of schedules in order to generate network traffic load uniformly for periodical data collection. The standard deviation of Equation (12) \(\sigma(Q_{all,x})\) is a measure of how uniform polling queries are generated during \(T_{M}\). Our objective function is  
\[\alpha=\min\{\sigma(Q_{all,x})\},where1\leq x\leq\frac{T_{M}}{\Delta t_{s} \times N_{all}}. \tag{13}\]
## 4 Validation and Discussion
### D Tunnel Stability Analysis  
Firstly, predictions of plane-strain (\(2D,\ L/D\rightarrow\infty\)) tunnel stability made by cylindrical cavity contraction solutions (\(k=1\)) are compared with those experimental results of [PERSON] [5] and [PERSON] and [PERSON] [6], as shown in Figure 3a. The figure shows that the experimental results are well contained within the predictions of finite solution (Equation (16)) and infinite solution (Equation (17)). These two analytical solutions are also compared with typical stability numbers computed from lower and upper bound limit analyses (see Figure 3b).  
It is shown that the plane-strain stability numbers calculated by [PERSON] and Assadi [8] and [PERSON], et al. [10], for tunnels with soil and geometry parameters \(\gamma D/s_{\rm u}\in[0,3]\) and \(H/D\in[1,6]\) are also satisfactorily bracketed by the infinite and finite cavity contraction solutions which locate close to the UB and LB, respectively. It can also be seen, from Figure 3, that the mean values of the stability numbers calculated by finite and infinite solutions are in good agreement with the experimental and simulated (FELA) data, and thus, Equation (18) is recommended here to predict the 2D tunnel stability.