esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Key Performance Indicators for T20--A Novel Hybrid Analytical Approach
## 4 Discussion  
Prediction forecasting of cricket match outcomes is crucial in the field of machine learning [39]. Researchers have explored the various techniques in the field of machine learning and artificial intelligence to address the challenges of predictive outcomes in cricket [40]. The aim of the study was to analyse the KPIs in T20 cricket matches and develop a predictive model that enhances the ability to predict match outcomes. A hybrid model, developed by combining variable importance and features ranking from the random forest algorithm with lasso logistic regression, was used to predict match outcome and player performance. This hybrid model was found to be adequate with an accuracy power of 85.9%. Sixteen KPIs were found to be most relevant in predicting cricket match outcomes. The following sections will discuss the sixteen KPIs identified in the present  
Figure 3: Distribution of KPI feature counts based on LOOCV.  
study under the subheadings of batting, bowling, and fielding. In comparison to traditional statistical methods such as effect analysis (e.g., Cohen's d) to distinguish the performance difference between winning and losing teams [12; 13; 14; 15; 16], the statistical analyses applied in these studies were appropriate but lacked clarity [12; 13; 14; 15; 16]. The current model used in the study provides greater predictive accuracy of 85.9% and accounts for non-linear interactions. Previous studies illustrate the limitations of single-method approaches. [PERSON] and [PERSON] [41] obtained 80% accuracy with a logistic model in One-Day Internationals (ODIs); yet its assumptions reflect the nature of 50-over play, reducing transferability to T20 [41]. Within the Indian Premier League (IPL), [PERSON] et al. [42] applied a random forest and reported 60% accuracy, while [PERSON] et al. [43] used k-nearest neighbours and achieved 62% accuracy. Both results display how a single algorithm struggles to accommodate the diverse nature of T20 [42; 43]. [PERSON] et al. analysed ODI innings by innings; the accuracy increased from 63.6% during pre-match to 81.8% during the second innings, but the sequential design ignored the simultaneous influence of batting, bowling, and fielding variables that characterise T20 cricket [44]. The present study surpasses those benchmarks and offers flexibility for the dynamic format of T20.
## 3 Spectrum Management Issues
### A. Carrier Aggregation
#### 3.1.4 Transmission Performance  
Another study [99] has proposed a novel design of the receiver for CA LTE-A and NR for the current 5G network. It has implemented the cascade-shutoff low-noise transconductance amplifier method. Consequently, the proposed generic receiver structure supports inter-band and intra-band CA with various CA scenarios, and the single-gm receiver design provides good linearity, especially for out-of-band blockers; however, it is limited to Single-Input-Multiple-Output scenarios. The authorsin [100] have proposed a latency-efficient Code-Division Multiplexing (CDM) CA approach, which is based on the least-squares approximation. This approach helps mitigate the Peak-To-Average Power Ratio (PAPR) for the scenario of 5G NR Mobile Fronthaul (MFH). The results show that the approach can reduce the number of iteration and latency with efficient transmission performance.  
Below is Table 1, which summarizes the techniques, advantages, and limitations of the above-discussed studies.
## 2 Related Work  
### Virtual Reality and 360 Video  
Virtual Reality, even though it is not a new technology, was not available to a broad audience until recent years. Additionally, Virtual Reality was usually a computer generated space in which the user would be placed. Technological advancements made filming of 360 video possible, and opened a whole new area of video storytelling. However, the main tool for any video storytelling was shot framing which is now completely gone since everything is now in frame, but the basic rules of cinematography should still apply in the same way.  
These were the first things we had to tackle technically, through the same approach we can find in [5]. Even though basic rules of cinematography do apply, we had to consider the fact that no lights, light stands, microphones and similar things can be placed anywhere on the location of shooting. All of that has to be a part of the actual scene. Another issue we had to consider was the distance of the action from the camera. In conventional video storytelling, framing and cuts in editing allow us to change our distance from the action happening without losing the attention of the viewer. However, in 360 video things change, especially as the video is viewed with a VR headset. Objects that are very close to the viewer can cause uneasiness and if they get too far away, the viewer loses interest. [PERSON], in [6] did a study with 26 participants, and the results show that viewers preferred the action at 3 m distance from the camera. Our own tests showed that action beyond 3 m is not so appealing to the viewer, but we also observed that action happening close to the camera can add to the emotions the viewer is experiencing. It can contribute to experiencing a deep connection to the video. Therefore, we decided to film in a smaller space for our first VR experimental film \"Nine dissidents\". Camera placement was very close to main actors, who were sitting in the improvised court room.  
Other 360 video guidelines considered the BBC, Jaunt and Oculus guidelines [7], and these also proved optimal in our test, such as preferred camera height, shot length, and so forth. Both directors we worked with pointed out that directing a VR video has more similarities with theatre directing than film directing, but also some uniqueness of its own. Other researchers also explore these similarities between theatre and VR noting that theatre directors use space in a complex way for narrative purposes, which can also be used in VR directing [8].
## 2 Connectivity in Colombia
### WiBACK Rural Backhaul Network: Technological Comparison  
WiBACK provides the technology to connect rural areas with limited or no Internet connection to remote network infrastructure while at the same time being able to cope with geographical disparities as well as the exposure to harsh environmental conditions [12]. In Table 1, the main characteristics of WiBACK nodes are compared with similar wireless backhaul technologies that use the 5 GHz unlicensed frequency band to communicate. The selected technologies are Cambium Networks PTP 550 [13], Mimosa B5c [14], and Mikrotik RB921 UAGS-5 SHPacT-NM [15].  
Comparing the characteristic's values among technologies in Table 1, similar values can be seen in Receive Sensitivity, Transmit Power, and Latency. However, in Throughput, Power consumption, and Capacity for independent link values, great advantages can  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristic**} & **WiBACK Node-2-** & **Cambium Networks** & **Mikrotik** \\  & **Connect-II [16]** & **PTP 550 [13]** & **Mimosa B5c [14]** & **RB921 UAGS-** \\  & & & & **SSHPacT-NM [15]** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Frequencies \\ Receive sensitivity \\ Transmit power (maximum) \\ \end{tabular} } & 5180–5800 MHz & 5170–5980 MHz & 4900–6200 MHz & 4900–6200 MHz \\  & \(-\)96 dBm & \(-\)92 dBm & \(-\)93 dBm & \(-\)96 dBm \\  & 30 dBm & 26 dBm & 30 dBm & 33 dBm \\  & 400 Mbps & 1.4 Gbps & 1.5 Gbps & 1.3 Gbps \\  & 15 W & 30 W & 20 W & 25 W \\  & Latency & 2 ms & 3 ms & 1 ms & Not specified \\  & 2 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: WiBACK technological comparison.  
be highlighted. Regarding Throughput, WiBACK technology is at least 30% lower than the other technological options under comparison. Another significant advantage of WiBACK technology is related to Power Consumption and Capacity for independent link characteristics. According to WiBACK technical data sheet, each node has an average consumption of 9 W DC (maximum 15 W DC) for two point-to-point wireless links. Unlike the other technological options under comparison, WiBACK maximum power consumption is almost half the value, and Capacity for independent link is doubled. WiBACK capacity to establish two individual wireless links makes it ideal to function as a signal repeater node. It means that two independent nodes, one to receive and one to transmit, are not needed to be grouped on the same tower to retransmit the received wireless signal. This characteristic decreases the amount of equipment needed for network implementation depending on its topology.  
Finally, WiBACK is a plug-and-play technology, easy to build and operate, which means that its installation time is lower than the other technological options under comparison. The most demanding part of setting up a network with WiBACK is the planning and the physical effort of mounting the WiBACK nodes [12].
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 2 Related Work  
To estimate the channel impulse response (CIR) at the CP-OFDM receiver, some conventional channel estimation methods are used in order to reduce the variance of noise and the channel estimation errors. The first method is the pilot-based LS algorithm [8; 9]. It directly divides the received signals from the pilot symbols in the frequency domain, and the amount of computation is very small. However, the LS estimation ignores the effect of additive white Gaussian noise (AWGN). In the actual OFDM communication system, the channel estimation is sensitive to the effect of AWGN [10]. When the variance of noise is large, the accuracy of channel estimation is reduced to a large extent. The second method is the minimum mean square error (MMSE) method [11]. The MMSE algorithm has a good effect on suppressing ICI and AWGN. The MMSE algorithm uses the statistical characteristics of noise and the channel matrix; therefore, its computational complexity is high. The MMSE algorithm involves the inverse operation of the matrix. The inverse operation is more complex as the number of subcarriers increases. The third method is linear MMSE (LMMSE) estimation [12; 13]. The LMMSE method can exhibit good performance under multipath channels; however, the LMMSE estimator suffers a performance floor at a high signal-to-noise ratio (SNR). The LMMSE method [14] can suppress the AWGN in the time domain, but it also has disadvantages. The construction of the autocorrelation matrix of the CIR requires a priori knowledge of the power and the delay of each channel. If the prior knowledge of the wireless channel is unknown, the channel statistical characteristics will not be matched with the actual transmission channel. The inverse of the channel matrix in the LMMSE algorithm increases the calculation complexity.  
The fourth method is the IMMSE method [15]. It simplifies the matrix calculation of the MMSE and LMMSE methods; therefore, it can be utilized in the hardware of the OFDM system. The fifth method is the threshold value channel estimation method [16]. It can be adopted to the slow multipath fading channel, but the disadvantage is that it is difficult to determine the suitable value under the multipath transmission channels with a larger Doppler spread. The sixth method is the Chi-square distribution-based channel estimation method [17]. The false alarms are removed in the channel estimation results; therefore, more accurate channel estimation results are obtained. The seventh method is the Haar wavelet channel estimation method [18]. In the literature [18], the Haar wavelet method can suppress the noise effectively based on a time-domain threshold, which is a standard deviation of noise obtained by wavelet decomposition. At the same time, the proposed Haar wavelet method improves the data transmission rate and spectrum efficiency while suppressing the AWGN effectively.
## 3 Methodology
### [PERSON]'s Approach to Yield Estimation
#### 3.1.1 Normalized Difference Vegetation Index  
RS data is widely utilised for vegetation mapping and monitoring. Due to the structure of 'green' leaves, healthy vegetation exhibits the greatest reflection at near-infrared wavelengths and the greatest absorption at red wavelengths due to the presence of chlorophyll [22]. NDVI is generated from satellite data combinations and calculated using the formula below.  
\[\text{NDVI}=\frac{\text{NIR}-\text{R}}{\text{NIR}+\text{R}} \tag{3}\]  
Here, NIR refers to light reflected in the near-infrared spectrum, and R refers to light reflected in the red region of the spectrum.  
In general, NDVI values range from \(-1\) to 1 [22]. Negative values indicate water bodies such as clouds, water, and snow, positive values close to 0 indicate rocks, bare land, buildings, and roads, and values greater than 0.2, depending on the region, indicate green areas. Large values (0.6 to 0.8) reflect temperate and tropical woods, whereas medium values (0.2 to 0.3) represent shrubs and meadows. Areas of empty rock, sand, or snow correspond to NDVI values of less than 0.1 [22].
## 3 Results and Discussion
### Material-Independent Scaling Relation
In obtaining Equation (14), it has been assumed that, over small ranges of temperature, the viscosity varies with temperature as [25,40,64]  
\[\eta=\eta^{0}\exp\bigl{[}-\alpha_{\eta}(T-T_{0})\bigr{]} \tag{16}\]  
Figure 9: (**a**) Temperature rise (\(\overline{T}_{\mathrm{w}}-T_{0}\)) and (**b**) dimensionless temperature rise \(\Theta=\alpha_{\eta}\big{(}\overline{T}_{\mathrm{w}}-T_{0}\big{)}\) vs. Nahme number Na for the five thermoplastic melts studied. Best-fit line through the data supports the approximate scaling relation \(\Theta\sim\mathrm{Na}^{0.72}\). Thus, a rise in fluid temperature can result in a reduced fluid viscosity by the relative amount  
\[\frac{\eta^{0}-\eta}{\eta^{0}}=\Theta=\alpha_{\eta}\big{(}\overline{T}_{\rm w}-T _{0}\big{)} \tag{17}\]  
Therefore, \(\Theta\) may be interpreted as the ratio of the amount of viscosity correction (\(\eta^{0}-\eta\)) to the uncorrected viscosity \(\eta^{0}\). Accordingly, in Equation (15), the power-law dependence manifested by \(\Theta\) can be interpreted as follows: An increase in the Na number causes the fluid temperature rise due to viscous heating as well as the resultant viscosity change (or viscosity correction needed). For instance, when \(\rm Na=1\) and \(10\), the maximum percentage errors in the viscosity are approximately \(5\) and \(20\%\), respectively. To check the prediction of the power-law scaling relation (Equation (15)) for the temperature rises reported in Figure 8, we substitute the expression for the Na number into Equation (15) and get  
\[\overline{T}_{\rm w}-T_{0}=\frac{c_{1}}{\alpha_{\eta}}\Bigg{(}\frac{R^{2} \gamma_{\rm a}^{2}\alpha_{\eta}\eta^{0}}{k}\Bigg{)}^{c_{2}} \tag{18}\]  
The derived correlation can be directly applied to correct the melt viscosities from a capillary die having similar dimensions so as to minimize the computational effort required. Here, an expression for \(\alpha_{\eta}\) in the power-law regime can be found by equating the temperature shifting factors in Equations (6) and (16), and one arrives at the following,  
\[\alpha_{\eta}=\frac{nA_{1}(T-T_{\rm r})}{(A_{2}+T-T_{\rm r})(T-T_{0})} \tag{19}\]  
in which the values of the Cross-WLF model parameters can be used. For an accurate determination of \(\alpha_{\eta}\), \(T_{\rm r}\) needs to be close to \(T_{0}\). For \(n=1\), Equation (19) reduces to the expression for Newtonian fluids. Equation (18) is for the convenience of making calculations and to show the extent of agreement with the simulation data. The temperature increase determined from the equation can be seen in Figure 8 (lines), where the accuracy of the predictions is acceptable for most of the materials studied. For some purposes, the prediction of Equation (18) for the mean fluid temperature rise in thermally developing capillary flows may be adequate, especially for order-of-magnitude estimates.
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 3 Results of Predicting the Number of Heatwave-Related Patients
### Hyper-Parameter Optimization  
The experiment was conducted using the Scikit-learn (v.0.22.2) Python package [45] to implement the RF; the hardware platform was an Intel (R) Core (TM) i9-9900k 3.60 GHz CPU with 32 GB of RAM. The out-of-bag (OOB) error is mainly used to measure errors in machine learning models, such as bootstrap aggregation (bagging), which can be substituted for test errors [46]. The lowest MAE was found for the training, OOB, and test errors as n-tree and max depth increased, and is shown in Figure 3. When the number of decision trees was more than 100, all graphs remained almost unchanged, and when the number of decision trees was 181, the lowest OOB error was found (4.59). The training and test errors at this time were 1.67 and 3.94, respectively.  
Figure 2: Contribution ranking importance of the 12 independent variables in the random forest (RF)-based variable reduction algorithm from the Boruta package [44] in R.  
On the other hand, when the number of decision trees was 181, the maximum depth of the decision tree remained constant from over 40, and the lowest OOB error (4.58) was found when the depth was 46. The train error and test error at this time were 1.69 and 3.94, respectively. Therefore, the hyperparameters were determined with 181 decision trees and 46 tree depths.
# Time Series Clustering Model based on DTW for Classifying Car Parks
## 2 Data and methodology
### Framework for Classifying Car Parks
#### 2.2.2 Dtw  
DTW is a family of algorithms which compute the local stretch or compression to apply to the time axes of two time series objects in order to optimally map one (query) onto the other (reference), and is good at calculating the distance between non-equal-length sequences. Figure 3 shows how to calculate the distance between two time series objects with different time length.  
Suppose that two time series sequences are \(Q\) and \(C\), where \(Q=q_{1},q_{2},\cdots,q_{i},\cdots,q_{n}\), \(C=c_{1},c_{2},\cdots,c_{j},\cdots,c_{m}\), and their lengths are \(n\) and \(m\), respectively. The traditional Euclidean distance (ED) as distance is defined in Equation (2) and the DTW for time series sequences is defined as Equation (3), which is an extension of ED for a local (non-linear) alignment. In order to obtain the DTW distance between two time series sequences, we construct a \(n\)-by-\(m\) path matrix where the element \((i^{th},j^{th})\) contains the distance \(d(q_{i},c_{j})\) between the two points \(q_{i}\) and \(c_{j}\), \(d\big{(}q_{i},c_{j}\big{)}=\big{|}q_{i}-c_{j}\big{|}\). Each path matrix element \((i^{th},j^{th})\) corresponds to the alignment between the points \(q_{i}\) and \(c_{j}\). The best match between two time series sequences is the one with the lowest distance path after aligning one time series sequence to the other. Therefore, the optimal warping path can be found by using recursive formula given by Equation (3).  
\[ED(Q,C)=\sqrt{\sum_{i=1}^{n}\big{(}q_{i}-c_{j}\big{)}^{2}} \tag{2}\]  
\[DTW(Q,C)=\sqrt{\omega(i,j)} \tag{3}\]  
where \(\omega(i,j)\) is the cumulative distance.  
This path is very efficient to evaluate the following recurrence which defines the cumulative distance \(\omega(i,j)\) as the distance \(d(i,j)\) in the current cell and the minimum of the cumulative distances of the adjacent elements:  
\[\omega(i,j)=d\big{(}q_{i},c_{j}\big{)}+\min\{\omega\,(i-1,j-1),\omega(i-1,j), \omega(i,j-1)\} \tag{4}\]  
here, \(min\) represents the minimum of the cumulative distances of the adjacent elements.  
Figure 3: Distance measurement method dynamic time warping (DTW).
# A Two-Phase Approach for Predicting Highway Passenger Volume
## 2 Literature Review
This means that the prediction of intercity passenger volume can be only conducted in a few cities [34]. In contrast, the highway has better accessibility and connects to all kinds of cities, expanding the study scope of predicting intercity passenger volume [35]. As previously stated, intercity passenger volume is largely determined by impact factors. Thus, the process of extracting significant impact factors at first, and then analyzing the interaction between intercity passenger volume and impact factors with deep learning algorithms, is practical for predicting intercity passenger volume but has rarely been studied in the existing research. From the above analysis, the relationship between intercity passenger volume and urban attributes has been widely studied, and some typical models have been developed to predict passenger volume. Nevertheless, some limitations still exist in previous research and need further improvement, which are listed as follows:  
1. Due to the restrictions of the research data, most existing research predicted intercity passenger volume from a single city or transportation corridor. As a result, the current achievements are difficult to apply to intercity transportation between all kinds of cities. 2. Existing research only focuses on common urban attributes such as the population or the economy. However, more urban attributes related to the quality of residents' lives, resources, and environment were neglected for lacking the available data and quantitative indicators, causing the inaccurate prediction of intercity passenger volume, especially in some tourism-driven cities and resource-driven cities. Moreover, the selection process of significant attributes also received less attention. 3. Microcosmic datasets collected from traffic surveys have been widely used for studying the choice of transportation mode in intercity trips but is not practical to predict intercity passenger volume. In contrast, the macroscopic datasets of urban attributes provided a novel approach to predict the intercity passenger volume, but have rarely been used in the existing literature.
# The Influence of Accidental Overheating on the Microstructure and Hardness of the Inconel 718 Alloy
## 1 Introduction
Additionally, the influence of welding processes has been analyzed [54; 55]. The effect of overheating temperature on thermal cycling creep properties has been studied in reference [56], while the influence of strain rate on flow stress behavior is addressed in reference [57]. [PERSON] conducted a study on the effect of extreme overheating on the microstructural degradation of superalloy 718, using heating tests at 1300 \({}^{\circ}\)C for different durations [58]. Furthermore, another study investigated the effect of overheating temperatures ranging from 1100 to 1300 \({}^{\circ}\)C [59; 60]. Inconel 718 is known for its high corrosion resistance and excellent mechanical properties at temperatures up to approximately 600 \({}^{\circ}\)C. Above 600 \({}^{\circ}\)C, the strengthening phases (\(\gamma^{\prime}\) and \(\gamma^{\prime\prime}\)) begin to dissolve, a phenomenon associated with a significant reduction in hardness and creep resistance. Additionally, the protective oxide layer becomes unstable and prone to degradation, limiting the lifespan of components. Certain equipment, such as that used in the nuclear energy sector, operating at temperatures between 350 and 500 \({}^{\circ}\)C, may experience accidental temperature spikes, which, although brief and promptly eliminated, can recur. Research presented in the literature has highlighted the influence of overheating during processes such as welding, friction, and high-temperature plastic deformation. This study investigated the impact of repeated accidental overheating on the microstructure, oxide layer formation, and hardness of the Inconel 718 alloy within a temperature range that aligns with the recommended standards, extending up to 400 \({}^{\circ}\)C above the alloy's maximum operating temperature.