esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To address these problems, there is an urgent need to develop new techniques to optimize the effect of ILU factorization, reduce the error, and achieve parallel computing, enabling effective control of changes in storage and computation. With the widespread application of graph theory techniques in matrix processing, numerous matrix preconditioning algorithms based on graph-theoretic methods have been developed, which has also led to research in the direction of solving the above problems. For instance, [PERSON] et al. [37] introduced an efficient graph reordering algorithm that is grounded in graph theory. This algorithm optimizes matrices following ILU decomposition preconditioning through a multilevel graph coarsening strategy, significantly enhancing solution stability and processing speed. However, the method is somewhat limited in terms of applicability, and to apply it to a wider range of problems would require adjusting the parameter settings and increasing the complexity, which contradicts our idea of wanting to simplify preconditioning. Subsequently, [PERSON] et al. [38] proposed a graph-theoretic method for matrix reordering. Their approach effectively reorders sparse matrices by constructing a two-layer chunked diagonal framework through the poly-chromatic coloring of subdomain adjacency graphs. However, its current applicability is still insufficient for the vast majority of symmetric and asymmetric problems. In this paper, an efficient matrix preconditioning algorithm, G-Pre, is developed by adding a graph partitioning algorithm and a graph reordering algorithm before the ILU factorization process. The algorithm implements the partitioning and concentration of non-zero elements of the matrix, which allows the ILU factorization to be executed in parallel in different partitions and improves the computational data access efficiency. The feasibility of the proposed algorithm is demonstrated through numerical experiments. The rest of the paper is structured as follows. In Section 2, symmetric and asymmetric matrix solution problems are briefly introduced with examples of static analysis problems and fluid dynamics problems. Then, commonly used solution algorithms and preconditioning methods for symmetric and asymmetric matrices are reviewed. Section 3 presents the preconditioning algorithm based on graph theory. Section 4 details the numerical experimental procedure and an analysis of the results, testing the effectiveness of the preconditioning algorithm using an actual finite element simulation model. Finally, Section 5 discusses related work and concludes the study.
## 5 Evaluation
### Discussion  
In the evaluation, we show that our selective hybrid fuzzing approach can reduce the number of non-critical branches for concolic execution to solve and improve coverage performance and the bug finding ability in hybrid fuzzing. However, there are still some unsolved problems related to this topic. Firstly, we analyzed the solvability by static analysis, which may have a false-positive problem, because of implicit data flow. A possible solution is to analyze the solvability by dynamic analysis, which may introduce extra overhead. Secondly, we use taint analysis to get the complexity information, which could be hindered by the over-tainting and under-tainting problems. This is the limitation of the taint analysis tool. A possible solution is to implement it with another taint analysis  
Figure 5: Coverage improvement by SHFuzz compared with QSYM shown by four applications.  
tool that has better precision; however, this will also introduce more overhead. Thirdly, our block-edge coverage calculation method is more precise than basic block coverage; however, it is still a coarse way to calculate coverage. The best solution would be to use path coverage or add more edges to calculate the coverage. In general, we trade accuracy for efficiency. Despite the unsolved problems, our work is the first attempt to solve the branch selection problem in synchronization-based hybrid fuzzing. We will leave these problems to further study.  
Another thing to mention is the overhead of the SHFuzz algorithm. The main extra overhead is caused by running dynamic taint analysis. In our observation, for all 20 applications, the average time cost for one input is less than 2 s. Since we only execute taint analysis for each input once, the overall time overhead cost by the SHFuzz algorithm will approximately be 30 min (considering analyzing 1000 inputs for each application), which is acceptable for the evaluation time of 12 h.
# Gain-Scheduled Model Predictive Control for
###### Abstract  
The cart-inverted pendulum system (CIPS) is a typical example of underactuated mechanical systems. For the CIPS with friction and disturbances, a gain-scheduled model predictive control method is proposed to achieve the upright stabilization objective of the single inverted pendulum (SIP) while controlling the cart to reach a desired new position. To this end, first, a dynamic equation of the CIPS with friction and disturbances is formulated based on the Newton-Euler equation. On the basis of the dynamic equation of the CIPS, its motion characteristics and control process are analyzed. Next, the given dynamic equation of the CIPS is linearized to obtain a series of linearized models at seven different pendulum angles. Then, seven model predictive controllers (MPCs) are designed based on the above-linearized models, respectively. Introducing the idea of the gain-schedule, a gain-scheduled MPC (GSMPC) is designed to switch one of these seven MPCs to realize the control objective of the CIPS, according to the actual pendulum angle of the SIP during the control process. Finally, multi-group simulations that consider the friction and disturbances of the CIPS are implemented to demonstrate the effectiveness of the proposed gain-scheduled model predictive control method.  
Keywords:gain-schedule; model predictive control; cart-inverted-pendulum; underactuated mechanical system; stabilization control +
Footnote †: journal: Applied
# A Distance-Encoded Bloom Filter for Fast NDN Name Lookup
## 4 Evaluation
Since the hash table contains only prefix nodes, lookups for such non-prefix nodes result in failures, leading to an increased number of hash table accesses. While  
Figure 16: Average number of Bloom filter accesses. Figure 17: Average number of hash table accesses. DEB also stores only prefix nodes in the hash table, it can predict whether a node is a prefix node using the Bloom filter, resulting in a lower hash table lookup failure rate. Figure 18 compares the hash table lookup failure rates of different lookup schemes. The results are obtained using various Bloom filter sizes. Since a larger Bloom filter reduces the false positive rate (FPR), the hash table lookup failure rate also decreases. As discussed earlier, schemes BS-a-p and BS-m-p, which store only prefix nodes in the hash table, exhibit significantly higher failure rates compared to other schemes. Unlike the Bloom filter, which resides in on-chip memory, the hash table is stored in off-chip memory. Accessing off-chip memory is the most time-consuming operation and is a critical factor in affecting overall performance. Moreover, a single hash table lookup is not always complete with just one off-chip memory access. In cases of hash collisions, additional memory accesses may be required to check the overflow area. Figure 19 presents the average number of off-chip memory accesses per name lookup. As the number of nodes stored in the hash table increases, hash collisions also increase, leading to more off-chip memory accesses. However, since DEB stores only prefix nodes in the hash table, it experiences fewer hash collisions. As a result, DEB achieves the best average memory access count among all the schemes. While BS-a-p also stores only prefix nodes in the hash table, its high hash table lookup failure rate and excessive access count result in poor overall memory access performance.
# Robust Engineering for the Design of Resilient Manufacturing Systems
## 3 Methods and Tools
### Design of Experiments (DOE)--Taguchi Method
The overall mean represents the average of all the mean values calculated and presented in the main effects plot. For the calculation of the means, Equation (3) is utilized. \[m=\frac{1}{n}\sum_{i=1}^{n}n_{i} \tag{3}\]  
For each factor, the individual mean has to be defined. Therefore, the S/N ratios that resulted from the experiments that a factor participated in the particular level are summed up. For instance, with the use of Equation (4), the average of factor A at level 3 can be calculated. \[m_{A3}=\frac{1}{3}(n_{7}+n_{8}+n_{9}) \tag{4}\]  
Subsequently, the plots of factor effects are constructed where the deviation caused from every factor to the overall mean is depicted graphically. **Step 7:** Analysis of variance (ANOVA)  
Analysis of variance which practically is the decomposition of variance can provide a deeper understanding of the relative effect of different factors. The magnitude of each factor on the objective function \(\eta\) can be determined. However, attention should be given to the inferences that will arise from these calculations. The Taguchi method does not attempt to make any probability statements about the significance of a factor as commonly happens in statistics [37]. Consequently, for the analysis of variance basic values have to be calculated, by applying Equations (5)-(10). \[SS=\sum_{i=1}^{n}(n_{i}-m)^{2} \tag{5}\]  
\[SS_{A}=3(m_{A1}-m)^{2}+3(m_{A2}-m)^{2}+3(m_{A3}-m)^{2} \tag{6}\]  
\[MS_{factor}=\frac{Sum\ of\ squares\ due\ to\ each\ factor}{Degrees\ of\ freedom} \tag{7}\]  
\[F_{ratio}=\frac{MS_{factor}}{Error\ mean\ square} \tag{8}\]\[N_{Taguchi}=1+\sum_{i-1}^{n}(L_{i}-1) \tag{1}\]0  
\[N_{Taguchi}=1+\sum_{i-1}^{n}(L_{i}-1) \tag{1}\]1  
where \(SS\) is the total sum of is squares, \(SS\_A\) is the sum of squares due to factor A, \(MS_{factor}\) is the mean square for each factor and \(\sigma_{e}^{2}\) is the error variance. The Equation (6) refers to the factor \(A\) but the formula is applied respectively for all the factors as also the Equations (7)-(9). \(F\) ratio represents the variance ratio. A large value of \(F\) means the effect of that factor is large compared to the error variance. It can be used to rank order factors. It should be mentioned that in robust design \(F\) ratio is used only for the qualitative understanding of the relative factors.
# Performance of UAV-to-Ground FSO Communications with APD and Pointing Errors
## 3 Symbol Error Rate Calculation  
This section studies the SER calculation of the above system in both weak and strong atmospheric turbulence channels, considering the pointing error and fluctuations from the FSO Tx mounted on the UAV to the FSO Rx.  
The generalized ASER expression for evaluating the UAV-based FSO system over the LN and GG atmospheric fading channels can be expressed by  
\[\text{SER}=\int_{0}^{\infty}\!\!\text{P}_{e}(\text{Y})\times f_{\text{Y}}( \text{Y})dY, \tag{29}\]  
where \(\text{P}_{e}(\text{Y})\) represents the conditional error probability (CEP) and \(f_{\text{Y}}(\text{Y})\) is the PDF of SNR, Y. By employing general \(\left(M_{I}\times M_{Q}\right)\)-QAM constellations with two independent \(M_{I}\) in-phase and \(M_{Q}\) quadrature signal amplitudes, the CEP is given by [13, 25]  
\[\begin{split}\text{P}_{e}(\text{Y})&=2q(M_{I})Q \Big{(}A_{I}\sqrt{\text{Y}}\Big{)}+2q\big{(}M_{Q}\big{)}Q\Big{(}A_{Q}\sqrt{ \text{Y}}\Big{)}\\ &-4q(M_{I})q\big{(}M_{Q}\big{)}Q\Big{(}A_{I}\sqrt{\text{Y}}\Big{)} Q\Big{(}A_{Q}\sqrt{\text{Y}}\Big{)}.\end{split} \tag{30}\]  
In the above equation, where \(q(x)=1-x^{-1}\), the Gaussian Q-function is defined by  
\[Q(x)=0.5\text{erfc}\Big{(}x/\sqrt{2}\Big{)}=1/\sqrt{2\pi}\int_{x}^{\infty}\exp \Big{(}-t^{2}/2\Big{)}dt. \tag{31}\]  
where \(Q(x)\) relates to the terms of the complementary error function \(\text{erfc}(\cdot)\). \(A_{I}\) and \(A_{Q}\) which can be calculated from \(M_{I}\), \(M_{Q}\) are in-phase, quadrature distances. They are defined as \(A_{I}=\left[6\Big{/}\left(M_{I}^{2}-1\right)+d_{IQ}^{2}\Big{(}M_{Q}^{2}-1 \Big{)}\right]^{\frac{1}{2}}\), and \(A_{Q}=\left[6d_{IQ}^{2}\Big{/}\left(M_{I}^{2}-1\right)+d_{IQ}^{2}\Big{(}M_{Q}^ {2}-1\Big{)}\right]^{\frac{1}{2}}\) in which \(d_{IQ}=\frac{d_{Q}}{d_{I}}\).  
The SER is then calculated by  
\[\begin{split}\text{SER}&=2q(M_{I})\int_{0}^{\infty} Q(A_{I}\sqrt{\text{Y}})f_{\text{Y}}(\text{Y})dY+2q(M_{Q})\int_{0}^{\infty}Q(A_{Q} \sqrt{\text{Y}})f_{\text{Y}}(\text{Y})dY\\ &-4q(M_{I})q(M_{Q}
## 2 Optimization Design Process and Modeling Method
### Numerical Method
#### 2.3.1 Establishing the Calculation Domain  
The hydraulic performance of the model pump is predicted by means of a CFD process in this study. The computational domain of the original pump is shown in Figure 3, which is comprised of four components, namely the inlet, impeller, pump chamber, and volute. In order to consider the full development of turbulence, the inlet and volute outlet pipes are properly extended. The inlet domain is directly connected to the impeller domain with a rotor-stator interface. The impeller domain is also directly connected to the chamber domain with a rotor-stator interface.
# The Role of Smart Meters in Enabling Real-Time Energy Services for Households: The Italian Case
## 4 Services for Final Users
### Awareness  
This category includes all those services aiming at informing the final customers about contractual information, special events (e.g., network issues), and energy consumption/production, with the final goal of modifying their energy usage towards smarter and more environmentally friendly behavior.  
Thanks to a simple display, all the details of the supply contract can be visualized directly at the customer's premises; DSOs can update this information in case of contractual changes. Moreover, the energy consumed or produced can be shown as a table or graph pairing the power (or energy) exchanged with the time of usage. The customer, made aware about costly peak-hours, can decide to shift deferrable loads such as a dishwasher cycle or an electric vehicle recharge. With more complex technology, the same display can show the corresponding daily or monthly total cost, even reporting an estimate for the remaining period. Increasing device features and data sampling time, the display could report appliance anomalies detected thanks to disaggregation algorithms. Information about blackout events or scheduled network maintenance can be displayed as close as possible to the event.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Technological Evolution of DES in Healthcare
#### 4.5.2 IoT Integration and Real-Time Data Collection  
The adoption of Internet of Things (IoT) devices has revolutionized data collection in healthcare DES models. [PERSON] et al. [56] demonstrated how IoT-enabled healthcare environments facilitate automated patient tracking and real-time monitoring of resource utilization in their comprehensive review of healthcare information systems. Supporting this evolution, [PERSON] et al. [57] provided evidence that integrating IoT with healthcare simulations enhances data collection accuracy and enables real-time model updates.
## 3 Performance Comparison
### Block Fuel Comparison  
Figure 6 shows the results in terms of block fuel consumption of each designed configuration for both BW and TW. The results have been split into either wingspan groups, as this parameter may be crucial for aircraft operativity features. Indeed, having a wingspan smaller than 24 m (hence, being compatible with ICAO Aerodrome Code 'B' [61]) can expand the number of accessible regional airports [60]; this factor holds significance in the regional market scenario as it enables the exploration of new routes and uncharted opportunities [62].  
Considering configurations where b < 24 m, a disparity between the BW and the TW emerges, as only a few hybrid-electric TW configurations were identified since increases in the MTOW trigger rapid increases in the (TW) wingspan. Conversely, considering the same  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Electric Component** & **Specific Power/Energy** & **Efficiency** \\ \hline Battery & 500 Wh/kg & 0.96 \\ Electric motor & 13 kW/kg & 0.96 \\ Inverter & 19 kW/kg & 0.98 \\ Wire & 352 kWm/kg & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected electric chain parameters.  
Figure 6: Block fuel vs. MTOW for the designed configuration; BW (**left**) and TW (**right**).  
MTOWs, several BW configurations comply with the ICAO 'B' constraint, as the higher lifting capability of the BW system allows for generating the same lift requiring a smaller wingspan. A second main outcome from the trends in Figure 6 indicates that both aircraft can reduce fuel consumption by increasing the MTOW, as a higher MTOW implies the possibility to embark on a higher battery mass, hence reducing fuel consumption, consistent with the detailed findings presented in the recent authors' work [21]. From Figure 6, it is also evident that the BW configurations exhibit lower absolute values of block fuel and a larger slope of the block fuel reduction compared to the TW configurations. This aspect is also illustrated in Figure 7, which presents the statistical analysis of the block fuel output for both BW and TW, providing a comprehensive overview of the dataset: For each dataset, the chart showcases the median, lower, and upper quartiles, as well as the minimum and maximum values. In this context, the analysis reveals that the maximum value of the interquartile range for BW configurations is lower than the minimum value for TW configurations: this outcome emphasizes that a significant majority of BW configurations burn less fuel compared to TW aircraft.  
This specific outcome is related to three main aspects: (_i_) the lower structural mass of the BW lifting system; (_ii_) its higher lift-to-drag ratio; and (_iii_) a better usage of the hybrid-electric propulsion system, as detailed in the following.
## 4 Experiments and Results  
### Comparision between AAM and DB-ASM  
IMM Face DB was used to evaluate the performance of DB-ASM and AAM [19]. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used in measuring the fitting error. The former is defined in Equation (12). Here, \(n\) refers to the total frames and \(y_{i}\) indicate the ground truth:  
\[\text{MAE}=\ \frac{1}{n}\sum_{i=1}^{n}|f_{i}-y_{i}|=\frac{1}{n}\sum_{i=1}^{n}|e_{ i}| \tag{12}\]  
value for the pose, whereas \(f_{i}\) refers to the estimated pose. The latter is defined as Equation (13).  
\[\text{RMSE}\left(\text{s}\right)=\sqrt{\frac{1}{v}\sum_{i=1}^{v}\left(s^{xi}-s _{gt}^{xi}\right)^{2}+\left(s^{yi}-s_{gt}^{yi}\right)^{2}} \tag{13}\]  
\(s_{gt}\) = ground truth, \(s\) shape point.  
Figure 5: System Flow Chart.  
Figure 6: The criteria for determining driver’s state upon changes in nodding and blinking HMM (Hidden Markov Model)states.  
Results indicate that DB-ASM outperforms AAM in terms of MAE as well as RMSE, as shown in Table 2, partly because DB-ASM is a recent face model. In any case, this is good news since we are going to adopt DB-ASM as our basic face model for the present study.  
In addition, it is well known that DB-ASM is robust against partial occlusion cases. Our demonstration confirms that it is very reliable even against several occlusion cases, such as 20% and 40% occlusion, respectively, as illustrated in Figure 7. We have seen that the overall fitting is severely damaged when the same occlusions are applied to AAM.
## 4 Prediction Model and Numerical Testing
### Model Validation and Testing  
A confusion matrix is commonly used to evaluate the performance of DNN models. In this study, testing was conducted on the road section passing through gantry 01F10906S, located on the Changhua Interchange, which connects the southbound lanes of National Freeway No. 1 and National Freeway No. 3. After the developed DNN model was trained, its performance was evaluated using the following indicators based on the true-positive (TP), false-negative (FN), false-positive (FP), and true-negative (TN) rates:  
1. Sensitivity: The ratio of true traffic accidents predicted by the model to all true traffic accidents: \[\text{Sensitivity}=\frac{TP}{TP+FN}\] (10)
2. Precision: The ratio of true traffic accidents predicted by the model to all traffic accidents predicted by the model: \[\text{Precision}=\frac{TP}{TP+FP}\] (11)
3. False alarm rate: The ratio of false traffic accidents predicted by the model to all no-accident instances: \[\text{FAR}=\frac{FP}{FP+TN}\] (12)
4. Accuracy: The ratio of true traffic accidents to no-accident instances correctly predicted by the model: \[\text{Accuracy}=\frac{TP+TN}{TP+FN+FP+TN}\] (13)  
Table 3 summarizes the results of the confusion matrix and aforementioned indicators. In both the training and the validation stages, the developed model achieved an accuracy of 94%, a sensitivity of 88%, a precision of <10%, and a false alarm rate of <6%. The above values show that this model predicts accurately, and the false alarm rate is low. When the model is trained and verified, the real-time data of the detection facilities can be used to predict accident symptoms. If a symptom is found, the predicted result can be broadcast,  
for example on a changeable message sign or a smart mobile phone. In this way, highway users are informed in advance, so that serious accidents are reduced and the safety of road users is improved.
## Appendix 1: Derivation of the Isochronous Condition (General Case)--Equation 16  
As it is obvious that the path length is the same for any ray having the same incident angle, \(\theta_{1}\), on the first prism, calculations are done in the particular case of a ray intercepting the apex, A\({}_{1}\), of prism 1, as shown on Figure 5.  
However, as it is easier to understand the significance of each angle in the general case, we add Figure 10, where the following angles appear:  
* \(\beta\) apex angle of the prisms;
* \(\theta_{1}\) incident angle on prism 1;
* \(\theta_{2}\) refracted angle in prism 1;
* \(\theta_{3}\) incident angle on the output face of prism 1;
* \(\theta_{4}\) refracted angle after prism 1 (in air);
* \(\theta_{5}\) incident angle on prism 2;
* \(\theta_{6}\) refracted angle in prism 2;
* \(\theta_{7}=\theta_{1}\) incident angle on output face of prism 2.  
Figure 10: Incident and refracted angles of the prism pair set-up in the general case.  
Clearly, the same angles can be defined in Figure 5. Using [PERSON]'s law at each interface with elementary geometrics, the following relations are obtained:  
\[\begin{cases}\sin\theta_{1}=n\sin\theta_{2}\\ \sin\theta_{4}=n\sin\left(\beta-\theta_{2}\right)\\ \theta_{3}=\beta-\theta_{2}\\ \theta_{6}=\theta_{3}\\ \theta_{7}=\theta_{1}\end{cases} \tag{10}\]  
Equation 9 gives the spectral phase, \(\phi\left(\omega\right),\) as a function of geometrical parameters, a,b, and the angle, \(\theta_{4}\) (Figure 5). The variation of the group delay, \(\Delta\tau_{\mathrm{g}}\), when applying the electric field, \(E\), is given by:  
\[\Delta\tau_{g}=\frac{\partial\Delta\phi}{\partial\omega}=\frac{\partial}{ \partial\omega}\left(\phi\left(\omega,E\right)-\phi\left(\omega,0\right)\right) \tag{11}\]  
This equation can be rewritten as:  
\[c\Delta\tau_{g}=A+B \tag{12}\]  
where:  
\[\begin{split}& A=b\left(\sin\theta_{40}-\sin\theta_{4}\right)+a \left(\cos\theta_{40}-\cos\theta_{4}\right)\\ & B=b\omega\frac{\partial}{\partial\omega}\left(\sin\theta_{40}- \sin\theta_{4}\right)+a\omega\frac{\partial}{\partial\omega}\left(\cos\theta _{40}-\cos\theta_{4}\right)\end{split} \tag{13}\]  
In these equations, \(\theta_{4}\) and \(\theta_{40}\) are, respectively, the output angle of prism 1, as defined in Figure 5, when an electric field is applied and when it is not. The isochronous condition corresponds to \(\Delta\tau_{\mathrm{g}}=0\) (no group delay variation due to the application of the electric field).