esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Key Performance Indicators for T20--A Novel Hybrid Analytical Approach
## 2 Materials and Methods
### Data Analysis
Lasso logistic regression is a penalising logistic regression that estimates the regression coefficients by maximising the log-likelihood function (or the sum of squared residuals) plus a penalty term, which penalises when more features are added to a model. A critical property of this algorithm is that the estimates of the regression coefficients are sparse, which means that many coefficients are exactly 0; i.e., it automatically deletes less important covariates in the model [37]. The overall analysis in the current study adopted a hybrid approach that combined rankings of the features, based on variable importance, from the random forest model with lasso logistic regression. More specifically, the final features selected were those that had both the highest importance rankings and were selected via lasso logistic regression, i.e., features with non-zero regression coefficients. These features were identified as the KPIs that could be used to fit a new random forest model for future prediction. In the current study, the random forest algorithm was run using the R function ranger, wherein the parameters were num.trees = 1000, importance = permutation, and oob.error = True. The lasso logistic regression was run using the R function glmnet, with lambda = lambda.min, which was obtained via cross-validation using the R function cv.glmnet. All the other parameters in both R functions took the default values. The prediction accuracy of the model, based on the KPIs obtained, was estimated using leave-one-out cross-validation (LOOCV). Since there were 78 data points in the dataset, there were also 78 cross-validation runs in LOOCV. The hybrid approach outperforms single-method models. It first uses random forest to capture complex, non-linear and higher-order interactions among PIs and rank them in order of importance. Then, lasso logistic regression prunes irrelevant variables by shrinking weak predictors to zero. Finally, a new random forest is trained on the refined set, and strong predictive accuracy is achieved with fewer, but more meaningful, KPIs. The hybrid model provided a balance between model complexity and performance. It remained relatively interpretable while still delivering robust results, as observed in a pseudo-code in Figure 2.
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 4 Analysis
### Analysis of Optimal Fidelity  
The fidelity is widely used within the quantum computation and quantum information community, and we discuss the quantum multi-user broadcast protocol for the \"Platform as a Service\" model. In our study, the algorithm discussed above is sufficient to complete each step of the computation with higher fidelity than \(1\to M\) phase-covariant \(QCM\) by Equation (5).  
The quantum multicast router obtains measurement outcome for \(\ket{\Psi_{0}}_{out}\), and publicly announces the results. \(S_{1}\), \(S_{2}\),..., \(S_{i}\),..., \(S_{N}(N<M)\) carry out the unitary transformation \(\sigma\) separately on their qubits. The final states of qubits for \(S_{i}(i=1,2,\cdots,N)\) equal to the original state \(\ket{\Psi_{0}}=\frac{1}{\sqrt{2}}(\ket{0}+e^{i\varphi}\ket{1})\). We now wish that the optimal phase-covariant cloning machine can be achieved. Let us see fidelity, which is found to take the form  
\[F=\frac{1}{2}[1+\eta(1,N)]=\begin{cases}1/2+\sqrt{N(N+2)}/4N,&\text{$N$ is even,}\\ 1/2+(N+1)/4N,&\text{$N$ is odd,}\end{cases} \tag{15}\]  
where  
\[\eta(1,N)=\sum_{j=0}^{N-1}\alpha_{j}\alpha_{N-1-j}\frac{C_{N-1}^{j}}{\sqrt{C_ {N}^{j}C_{N}^{j+1}}}. \tag{16}\]
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 5 Results and Discussion
### Multi-Level Multinomial Logistic Regression  
This study presents the results of the Multi-Level Multinomial Logistic Regression analysis, focusing on the impact of various variables on collision severity. First, 'Collision Type (Head-On)' is examined, which yields odds ratios of 1.9 for fatality, 3.6 for major injuries, and 1.2 for minor injuries, as shown in Table 3. These findings underscore the heightened risk of fatality and major injuries in head-on collisions, with a comparatively modest elevation in minor injuries. For 'Collision Type (Rear-End)', odds ratios of 0.75 for fatality, 0.65 for major injuries, and 1.23 for minor injuries are observed. This reveals a lower likelihood of fatality and major injuries in rear-end collisions, possibly due to their lower impact force. Minor injuries show a slight elevation. Focusing on Traffic Control, collisions with traffic control measures have odds ratios of 46 for fatality, 431 for major injuries, and 1 for minor injuries compared to those without such control. This emphasizes the substantial protective role of traffic control in reducing the risk of major injuries, while minor injuries maintain a similar likelihood. Regarding the seatbelt usage ('Belted (No)'), the odds ratios is 16 for fatality, 10 for major injuries, and 4 for minor injuries, compared to the baseline 'Belted (Yes)' category, highlighting the importance of seatbelt use in averting severe outcomes. 'Bike (Yes)' involvement in collisions results in odds ratios of 13 for fatality, 67 for major injuries, and 41 for minor injuries, underlining the heightened vulnerability associated with bicycle-involved collisions. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Multinomial Logistic Regression** & **Multi-Level Multinomial Logistic Regression** \\  & **Regression** & **Logistic Regression** \\ \hline AIC & 427,549 & 421,118 \\ BIC & 428,464 & 422,365 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model performance.
# Surface-Codes-Based Quantum Communication Networks
## 5 Illustrative Numerical Results
The total transmission length can be now estimated by:  
\[L_{\text{tot}}\ =\ NL_{\text{eff}}|\text{ln}\,T|. \tag{6}\]  
For \(T=0.95\), by setting the qubit error probability to \(p=10^{-4}\), we can see from Figure 9 that the achievable total transmission distance for normalized SKR of \(10^{-6}\) is \(L_{\text{tot}}=252\times 30.606\times|\text{ln}0.95|=395.61\) km. On the other hand, for \(T=0.85\), by setting the qubit error probability to \(p=10^{-4}\), we can see from Figure 10 that the achievable total transmission distance for normalized SKR of \(10^{-15}\) (typical for discrete variable QKD schemes [2]) is \(L_{\text{tot}}=208\times 30.606\times|\text{ln}0.85|=1034.61\) km, and this results is comparable to the recently proposed hybrid QKD-postquantum cryptography scheme [22; 23]. By employing higher complexity quantum sum-product algorithm [1] in each stage, instead of simple syndrome decoding, the total transmission distance well beyond 1000 km can be achieved. Typical QKD transmission distances are significantly shorter, even when the most advanced twin-field QKD schemes are used [24].
## 5 The Detail of Our Protocol
### Routing Heads Selection  
Since CHs in the first unit are very close to BS, they transmit data to BS directly. As shown in Equation (25), the units closer to BS have more CHs than the ones farther from BS. Let \(m_{i}\)= 2\(\times\)[\(c_{i-1}\)/\(c_{i}\)] (1 \(<\) \(i\) \(\leqslant\) \(m\)). For any CH \(j\) in unit \(i\) (expressed as \(ch_{i,j}\)), it randomly selects \(m_{i}\) CHs from set \(Sch_{i-1}\) as its routing head candidates and stores them into set \(S_{hc}\). In addition, the candidate with the most residual energy will be selected as its final routing head. This random candidate selection mechanism can mitigate the unbalance of inter-cluster traffic. The set \(Sch_{i-1}\) (1 \(<\) \(i\) \(\leqslant\) \(m\)) is expressed as follows:  
\[Sch_{i-1}=\left\{ch_{i-1,k}\left|dis\left(ch_{i,j},ch_{i-1,k}\right)\right. \leqslant\) \(dis\left(ch_{i,j},BS\right)\ \ -d_{i-2\to BS},ch_{i-1,k}\text{ is any CH }k\text{ in unit }i-1\right\} \tag{43}\]  
where \(dis(ch_{i,j},\)\(ch_{i-1,k})\) is the distance between CH \(ch_{i,j}\) and \(ch_{i-1,k}\); \(dis(ch_{i,j},\) BS) is the distance from CH \(ch_{i,j}\) to BS and \(d_{i-2\to BS}\) is the average distance from the nodes in unit \(i\)-2 to BS (\(d_{0\to BS}\)= 0).
## 4 Results
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \(\mathbf{0.6 Re_{nom}}\) & \(\mathbf{0.75 Re_{nom}}\) & \(\mathbf{0.9 Re_{nom}}\) & \(\mathbf{1.1 Re_{nom}}\) & \(\mathbf{1.25 Re_{nom}}\) & \(\mathbf{1.4 Re_{nom}}\) \\ \hline \(PR=1.5\) & 0.272 (1) & 0.272 & 0.272 & 0.271 & 0.273 & 0.275 \\  & & (\(-0.07\%\)) & (\(+0.00\%\)) & (\(-0.42\%\)) & (\(+0.59\%\)) & (\(+1.12\%\)) \\  & & 0.300 & 0.299 & 0.302 & 0.303 & 0.304 \\ \(PR=2.0\) & 0.303 (1) & (\(-1.02\%\)) & (\(-1.22\%\)) & (\(-0.39\%\)) & (\(-0.03\%\)) & (\(+0.19\%\)) \\  & & 0.317 (1) & 0.317 & 0.321 & 0.321 & 0.321 \\ \(PR=2.5\) & 0.317 (1) & (\(-0.03\%\)) & (\(-0.03\%\)) & (\(+1.06\%\)) & (\(+1.18\%\)) & (\(+1.23\%\)) \\  & & 0.327 & 0.327 & 0.327 & 0.329 & 0.330 \\ \(PR=3.0\) & 0.326 (1) & (\(+0.30\%\)) & (\(+0.43\%\)) & (\(+0.35\%\)) & (\(+0.96\%\)) & (\(+1.20\%\)) \\  & & 0.332 & 0.332 & 0.334 & 0.335 & 0.335 \\  & & (\(+0.03\%\)) & (\(+0.28\%\)) & (\(+0.72\%\)) & (\(+1.00\%\)) & (\(+1.11\%\)) \\  & & 0.334 & 0.335 & 0.336 & 0.337 & 0.338 \\ \(PR=4.0\) & 0.334 (1) & (\(+0.19\%\)) & (\(+0.43\%\)) & (\(+0.76\%\)) & (\(+1.10\%\)) & (\(+1.22\%\)) \\  & & 0.336 & 0.338 & 0.339 & 0.339 & 0.340 \\  & & (\(+0.25\%\)) & (\(+0.78\%\)) & (\(+0.91\%\)) & (\(+1.05\%\)) & (\(+1.28\%\)) \\ \(PR=5.0\) & 0.337 (1) & 0.339 & 0.338 & 0.339 & 0.340 & 0.341 \\  & & (\(+0.59\%\)) & (\(+0.25\%\)) & (\(+0.65\%\)) & (\(+0.79\%\)) & (\(+1.26\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental discharge coefficient for each test Reynolds number and pressure ratio (stepped labyrinth). Figure 6: Discharge coefficient as a function of the overall pressure ratio for different values of the flow Reynolds number.
## 2 Materials and Methods
### Random Forest Algorithm  
The decision tree method was initially introduced by [PERSON] [40]. It is popular for being a reliable non-parametric statistical approach for both regression and classification issues. It was an ensemble approach for achieving prediction accuracy based on the outcomes of various trees [41]. RF combines the projected values from each tree in the forest to provide the best result for each new observation. Each tree in the forest serves as an important member of the RF's ultimate decision [42].  
Three phases summarize the crux of the RF model for regression:  
1. Based on the dataset, generate bootstrap samples that are the number of trees in the forest (ntree).
2. Create an unpruned regression tree for each bootstrap sample by picking predictors at random (mtry). Choose the optimal split among those factors.
3. Assemble the anticipated values of the trees to forecast fresh observations (ntree). The average value of the projected values by each tree in the forest was utilized to solve the regression problem as well as forecast fragmentation and blast-induced PPV.  
The error rate can be obtained from the training dataset in two ways, using out-of-bag (OOB) and aggregate of OBB.  
It is a popular approach in machine learning for tackling regression and classification problems. It was developed by [PERSON] [43]. The K-NN method finds the testing point and classifies it based on its nearest neighbors (k-neighbors). From the training data, the algorithm teaches nothing. It solely retains the weights of its functional space neighbors. When anticipating a new observation, it looks for comparable findings and computes the closeness to those neighbors.  
In geosciences, the K-NN algorithm has been widely utilized to predict rock fragmentation, back break, and ground vibration [33]. KNN primarily employs the weighted average value of the k-nearest neighbors; the computation begins by calculating the distance between the uncertain and labeled neighbors using Equation (6), and then the neighbors' numbers are re-arranged by raising the distance and RMSE using the cross-validation method. Finally, the average inverse distance between K-Nearest Neighbors will be computed.  
\[\text{D}(\text{x}_{\text{tr}},\ \text{x}_{\text{t}})=\sum_{i=1}^{n}\text{w}_{n}( \text{x}_{\text{tr},n}-\text{x}_{\text{t},n}) \tag{6}\]  
Above n denotes the number of features, \(\text{x}_{\text{tr},n}\) & \(\text{x}_{\text{t},n}\) represent the nth feature of training and testing data, and \(\text{W}_{n}\) presents the weight of the nth feature.
# Video Streaming Adaptive QoS Routing with Resource Reservation (VQoSRR) Model for SDN Networks
## 3 The Proposed VQoSRR Framework
### The VQoSRR QoS Routing Mechanism
#### 3.2.2 Installing or Updating Flow Path (IUFP) Algorithm  
This algorithm is in charge of receiving the switch packet in request, and pushing the path rules into the flow tables for new incoming flow, or updating tables if the configuration rules of already proceeding flow changes. In addition, the IUFP is responsible for admitting or rejecting flow requests because the VQoSRR must route the video stream along a path that can accommodate its QoS requirements, such as meeting packet loss and bandwidth thresholds. Otherwise, it indicates that the QoS currently requested cannot be admitted. Algorithm 1 illustrates this scenario.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flow id** & **First Path** & **Alternative Path** & **Path Type** & **Path Count** & \begin{tabular}{c} **Flow** \\ **Admission** \\ \end{tabular} \\ \hline Flow 1 & S1-S3-S5 & S1-S2-S4-S5 & \
\begin{tabular}{c} meet two \\ metrics \\ \end{tabular} & 2 & Admitted \\ \hline Flow 2 & - & - & - & - & Rejected \\ \hline Flow 3 & S1-S2-S4-S7 & null &
\begin{tabular}{c} meet one \\ metrics \\ \end{tabular} & 1 & Admitted \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example of the flow path cache and flags.
## 4 Discussion
### Methane Emissions Regulated by Microbial Community under Different Substrates
The acetate production in this study also increased with the addition of DF, but most fumarate in this study could have been converted to propionate. [PERSON] and [PERSON] [40] found that monensin can also convert acetate to propionate. [PERSON] [41] demonstrated a significant correlation between methane production and A:P. [PERSON] et al. [12] revealed significant declines, of7%, in CH4 emissions from lactating dairy cows fed 60:40 forage-to-concentrate total mixed ratio (TMR) supplemented with monensin. The addition of MSS and DF also has a significant impact on the rumen microbial community. The addition of MSS and DF decreased the relative abundance of Eturyarchaeota and Firmicutes but increased the relative abundance of Bacteroidetes. These were consistent with [PERSON] et al. [10], who found that monensin inhibits Gram-positive bacteria and protozoa, while reducing the relative abundance of methanogens. [PERSON] et al. [42] found that the number of methanogens significantly decreased in the rumen fluid samples of sheep fed 20 g/kg DF. _Methanobrevbincter_ was the dominant methanogen genus in this study. [PERSON] and [PERSON] [43] showed that the number of _Methanobrevbincter_ accounts for nearly two thirds of rumen archaea. Consistent with our previous research result, H2/CO2 is the main pathway of methanogenesis [23]. _S. ruminantium, Veillonella parvula, F. succinogenes, and Wolinella succinogenes_ are thought to be the major fumarate reducing bacteria in the rumen [36], while _R. albus_ and _R. flarefaciens_ are the main cellulocytic bacterial species [44]. Fibolytic bacteria are the main hydrogen production bacteria in rumen fermentation, while fumarate reducing bacteria are the main hydrogen consuming bacteria. Fumarate can stimulate the competition between fumaric acid reducing bacteria and methanogens for hydrogen or formate, and thus reduce the methane production of rumen. The addition of MSS and DF, alone and in combination, increased the number of starch degrading bacteria (_Prevotella_ and _Selenomonas_) and fumarate reducing bacteria (_Fibrobacter_ and _Selenomonas_), decreased the number of methanogens, while no significant effect was observed on fibolytic bacteria (_R. albus_ and _R. flarefaciens_). [PERSON] et al. [17] reported that the addition of DF to high-forage diets increased the number of _S. ruminantium_ and fumarate-reducing bacteria, but the number of fungi, protozoa, and fibolytic bacteria were not affected. [PERSON] et al. [36] reported the addition of 30 mM fumarate increased the growth of _F. succinogenes, S. ruminantium, S. lactilytica, V. parvula_ and _W. succinogenes_ in vitro cultures. [PERSON] et al. [45] reported no significant impact on the abundance of cellulolytic bacteria by supplementing with fumarate in Rusitec fermenters. [PERSON] et al. [46] found monensin decreased the effectiveness of H2 to CH4 formation, mainly by enhancing the amylolytic activity of inhibiting cellulolytic activity.
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 4 Numerical Results
### Effect of SNR and Rate on PER, Throughput, and Delay
With a specific retransmission rate \(R\) and SNR, each scheduled user has an average PER reliability, as can be seen in Figure 3. On the contrary, packet-level delay  
Figure 3: Packet error rate performance comparison between D-NOMA-HARQ and S-NOMA-HARQ with \(w_{1}=0.6\) (\(w_{2}=1-w_{1}\)) with CC-HARQ, when \(m=1\), \(a_{1}=0.5\), and \(a_{2}=0.4\), at various rates. derived in (8) is not an average delay measure and depends upon the number of scheduled packets. However, the error rate of each of \(N\) scheduled packets can be found by (5). The D-NOMA-HARQ design does not allow a packet to be delayed by more than one time slot. In contrast, each retransmission in S-NOMA-HARQ delays all the subsequent packets by one slot. Therefore the delay performance of D-NOMA-HARQ is much better than S-NOMA-HARQ. In both S-NOMA-HARQ and D-NOMA-HARQ a packet is discarded if the maximum retransmission limit is reached and the receiver is unable to decode. Figure 4: Throughput performance comparison between D-NOMA-HARQ and S-NOMA-HARQ with \(w_{1}=0.6\) and \(w_{1}=0.6\) (\(w_{2}=1-w_{1}\)) with CC-HARQ, when \(m=1,\alpha_{1}=0.5\), and \(\alpha_{2}=0.4\), at various rates. Figure 5: Delay performance comparison between D-NOMA-HARQ and S-NOMA-HARQ with \(w_{1}=0.6\) (\(w_{2}=1-w_{1}\)) with CC-HARQ when \(m=1\), \(\alpha_{1}=0.5\), and \(\alpha_{2}=0.4\), at various SNRs and rates. As shown in Figure 3, at rate \(R=1\) and SNR = 12 dB, S-NOMA-HARQ achieves \(\text{PER}\approx 10^{-8}\); however, it delays a significantly high number of packets (shown in Figure 5). In contrast, D-NOMA HARQ provides reliability of \(\text{PER}\approx 10^{-4}\) with a much lower packet-level latency guarantee. Also, by increasing the SNR or reducing the rate, D-NOMA-HARQ can provide the desired packet-level performance without violating the packet level delay deadlines. For example, as can be seen in Figure 3, at rate \(R=0.5\) with SNR \(\approx 4\) dB, D-NOMA-HARQ achieves \(\text{PER}\approx 10^{-6}\). In contrast, S-NOMA-HARQ achieves a much better PER. However, Figure 5 shows that at rate \(R=0.5\) and SNR = 4 dB, the delay performance of S-NOMA-HARQ is much inferior than D-NOMA-HARQ. More specifically, S-NOMA-HARQ achieves the delay performance of D-NOMA-HARQ at \(R=0.5\) at SNR = 8.1 dB (additional 4.1 dB SNR for each user) as shown in Figure 5. Whereas D-NOMA-HARQ achieves the PER performance of S-NOMA-HARQ at \(R=0.5\) with only 1 dB and 3.5 dB additional SNR for user 1 and 2, respectively, as shown in Figure 3. This proves that D-NOMA-HARQ is much superior to S-NOMA-HARQ in providing a certain level of PER reliability and packet-level delay performance.
## 3 Experimental Verification
### Bearing Outer Race Fault Diagnosis
To quantitatively evaluate the enhancement, the mean relative error (MRE) is used. The MRE is computed by \(1/m\sum_{i=1}^{m}\left|f_{i}-f_{reali}\right|/f_{reali}\), where \(f_{i}\) and \(f_{reali}\) are the extracted IF values at time \(\tau_{i}\) and the true frequency value at \(\tau_{i}\), respectively, and \(m\) represents the number of time instants. Mean relative errors (MREs) of multiple IFs extracted by the FPO algorithm from traditional STFT-resulting TFRs, and the enhanced TFRs of both lower and resonant frequency band signals are listed in Table 2. It can be observed that the MREs of the IF ridges \(f_{1},f_{2},f_{3}\) and \(f_{4}\) extracted from the enhanced TFR are less than those of the IF ridges pre-\(f_{1}\), pre-\(f_{2}\), pre-\(f_{3}\) and pre-\(f_{4}\) extracted from the STFT-resulting TFR, indicating that the IF ridges from improved TFR are more accurate than the ones from STFT-resulting TFR. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**F Ridges** & **Pre-\(f_{1}\)** & **\(f_{1}\)** & **Pre-\(f_{2}\)** & **\(f_{2}\)** & **Pre-\(f_{3}\)** & **\(f_{3}\)** & **Pre-\(f_{4}\)** & **\(f_{4}\)** \\ \hline MRE & 0.1717 & 0.1109 & 0.1149 & 0.0631 & 0.0982 & 0.0898 & 0.0778 & 0.0281 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MREs of extracted IF ridges of outer race fault signal. Figure 10: Processing results of the low and resonant frequency band signals of the outer race fault signal using the proposed FMLT: **(a)** FMLT-resulting TFR of the lower frequency band signal, **(b)** FMLT-resulting TFR of the resonant frequency band signal, **(c)** extracted IF ridges from **(a)** using FPO, and **(d)** extracted IF ridges from **(b)** using FPO. Figure 9: Outer race fault case: illustration of the constructed frequencies of bases of FMLT. The bearing fault diagnosis can then be performed based on these extracted IF ridges. Instead of resampling, arbitrary points at the same time instant are captured, as shown in Figure 10c,d. It can be calculated that a point on the topped IF ridge doubles the one on the bottomed IF ridge for both Figure 10c,d. The ratio of the point on the bottomed IF ridge in Figure 10d to the point of the bottomed IF ridge in Figure 10c equals 3.51 and the ratio of the point on the topped IF in Figure 10d to the one of the topped IF ridge in Figure 10c equals 3.49, which are approximately identical to the fault characteristic coefficient of the outer race fault 3.57 in Table 1. It then can be concluded that the bearing has a fault on the outer race.
## 3 System Integration
### RTL Integration  
In the specification phase, the signal interface for each block and the output rate from each block are defined that helps in the chain integration. MATLAB is used to generate a text file, which the test bench reads a symbol per clock cycle from. The RTL system is tested with the same way used in MATLAB as a python script is used to run MATLAB, generating the files, then ModelSIM is run to simulate the design. Finally, MATLAB is run again to calculate the BER and the BLER that are illustrated in Figure 15.  
Figure 14: BLER vs. SNRfor MATLAB and RTL designed model compared with ideal model.