esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulations
(**a**) desired command and actual states; (**b**) tracking errors. Figure 6: Comparison of the desired command and system response in the \(z\)-direction for different approaches. (**a**) desired command and actual states; (**b**) tracking errors. Figure 8: Comparison of computed and truly allowed controls and rotor speeds for the IBC. (**a**) comparison of the computed and truly allowed controls (IBC); (**b**) square of computed rotor speeds; (**c**) truly allowed rotor speeds. Figure 7: Comparison of computed and truly allowed controls and rotor speeds for the CPID. (**a**) comparison of the computed and truly allowed controls (CPID); (**b**) square of computed rotor speeds; (**c**) truly allowed rotor speeds. Figure 9: Comparison of computed and truly allowed controls and rotor speeds for the proposed AIBC. (**a**) comparison of the computed and truly allowed controls (AIBC); (**b**) square of computed rotor speeds; (**c**) truly allowed rotor speeds. Figure 7-9 compare the computed and truly allowed controls for CPID, IBC, and AIBC, respectively. In Figure 7a, the computed force and torque both are denoted as a green line and are obtained from (37) and (A7); in Figure 8a, the computed force and torque both are denoted as a green line and are obtained from (37) and (54); the control laws shown in Figure 9a are obtained from (37) and (94), respectively. From the actuator configuration, the square of the computed rotor speed is computed from (4). Note that negative rotor speeds are not allowed in the practical scenarios. To meet the real situation, the negative rotor speed will be saturated by \(\Omega_{min}\) and then, by (2), the truly allowed control force and torque are generated by the rotor speeds reversely, as shown in Figures 7a, 8a, and 9a with a brown line, magenta line, and blue line, respectively. It can be found that the computed controls cannot be achieved due to the control signal saturation. The root of the matter is the amplified control signal due to differentiating the noisy feedback states. A similar phenomenon can be observed in Figure 8.  
in Figure 9a. Figure 10 compares the computed controls for different approaches. The results reveal that the proposed AIBC has a smooth-feasible profile, which is helpful for controller implementations. Figure 11 shows the virtual controls of the IBC and AIBC. Results indicate that the control chattering can be significantly reduced by utilizing the proposed AIBC rather than using IBC with APD. Meanwhile, the proposed AIBC enables allowing larger control gains to improve the trajectory tracking performance because it without involves any numerical differentiation. It is worth pointing out that applying larger control gains will induce more series control chattering since the numerical differentiation is used for estimating the time derivative of virtual controls. In summary, the proposed AIBC has a smoother profile, which is superior from a control realization point of view.
## 5 Evaluation and Results
### Experimental Setup and Evaluation
The sparsity rate K is set to 25% at 5 min sampling interval: (**a**) Example SWIFTNET data trace; (**b**) Example data trace from default adaptive prediction method; (**c**) A snapshot of real and predicted sensor readings. Overall, both Figures 8 and 9, we can observe the differences between the curves of SWIFTNET and NLMS. In particular, the NLMS curves are much smoother than the SWIFTNET curves due to the reason that, in SWIFTNET, only \(K\) elements are randomly selected during the data acquisition process. This signal is further compressed using the online generated LFSR matrix. According to the CS theory, the larger the value of \(K\), the smoother would be the reconstructed signal, _i.e._, the curve. The choice of \(K\) in this instance could depend on how often the radio of the nodes would be kept in \"ON\" mode to allow data acquisition. Keeping the radio in this state for a longer period during data acquisition could  
Figure 9: Signal reconstruction from Node 2, for a 2-day wildfire monitoring test-burn using a 45 \({}^{\circ}\)C _threshold_ cut-off point. The sparsity rate K is set to 25% at 5 min sampling interval: (**a**) Example SWIFTNET data trace. Fire is generated at different time intervals to test the efficacy of SWIFTNET to different operational mode; (**b**) Example data trace from default adaptive prediction method. mean more energy would be consumed on each node, hence a tradeoff between the accuracy of the signal reconstruction and the energy consumption in the deployed system. Next, we briefly examine the choice of the prediction models used for the wildfire application.
## 3 Results
### Comparison and Analysis of Forecast Results
#### 3.2.2 Analysis of Prediction Results  
The final training results are output as two obj model files and one txt prediction classification point cloud file. The two obj point clouds are Ground Truth and Predict, which are the true and predicted values, respectively. Visualization can be performed using software platforms such as MeshLab or Rhinoceros. Given that MeshLab can complete the visualization of points and surfaces more continuously, MeshLab software was chosen in this paper for post-prediction visualization. In total, 61 pieces of data were selected for testing in 618 datasets with 9:1 for prediction, and those with certain complexity and representativeness were selected for visualization, as shown in Figure 6.  
From Figure 6, we can intuitively see that Ground Truth and Predict are highly compatible, which means that the model was trained successfully. On the other hand, it also shows that the subway station building type has some stable design logic and spatial characteristics that can be searched using the model.  
From the visualization comparison between Ground Truth and Predict, we can see that the trained model, with certain intelligence, not only presents the volume of each partition in Predict, which is basically the same as in Ground Truth, but also automatically strengthens certain features, such as Shanghai Line 11 Swimming Pool Station, Chengdu Line 1 Tongzilin Station, and Chengdu Line 2 Baigoulin Station. Predict amplifies the volume of the traffic core, while the actual situation in Ground Truth is that the traffic core is not obvious, which is an interesting phenomenon, indicating that PointNet++ thinks the traffic core needs to be reinforced. This is entirely the result of the model self-learning after a large amount of data training. However, in the case of Beijing Line 1 Princess Tomb Station, Predict shows poor results, with interlocking paid and non-paid zones, confusion at the platform level and tracks, and confusion in the traffic core, but this is still in place. Secondly, the reason for the unsatisfactory prediction results of Beijing Line 1 Princess Mound Station may be related to the special characteristics of Beijing Metro Line 1, where there are only two large steps as traffic spaces and non-paying areas on both sides. However, the vast majority of metro stations have two or three traffic spaces in the center, so the analysis is due to the fact that there are too few of these metro stations in the dataset, resulting in the neural network not learning these characteristics.  
Another point to note is that theoretically, model training does not vary depending on whether the metro station is complex or not or whether it is a side platform, island, side hybrid station, platform-island platform, or a more complex interchange station. All types can be learned by the model. For example, Shanghai Line 10 Siping Road Station is an interchange station, but Predict still shows high prediction quality. This shows that the training process for this model is relatively complete and comprehensive.
## 4 Results
### Dependence of the Results on the Points Chosen  
The choices of positions for receivers will impact the final results of a calibration. It can be seen that our results are good, with an average error of less than 45 mm in the worst case, using only six receiver positions. It should be noted that equally or more importantly than the number of points used is the positions of those points. In general, the following recommendations should be followed to obtain optimal results:  
* Choose the points distributed over the coverage area of the emitters.
* Do not choose points very close to the boundary of coverage for any of the emitters, since at those points the distortion of both the lens and the PSD itself makes those measurements have greater error than the rest.  
To test the influence of the choice of the points, several combinations of six emitters have been analyzed, while always following the discovered criteria. The results were analyzed for case 1 without knowing the height, and the results were practically the same, with differences of less than 10% compared to those obtained in the Tables 5 and 6.  
Another factor that can influence the calibration result is the number of points chosen. As a general rule, the more points chosen, provided they are chosen appropriately, the better the results. A test was carried out in which nine points were chosen, evenly distributed in three rows and three columns out of the 44 available. In this test, case 1 was analyzed a total of 100 times, obtaining the values shown in Table 7. The mean and SD values of the mean errors of the total of the 100 emulations performed are shown.  
When comparing the results of the Tables 5 and 6 with the results of Table 7, it can be observed that as the number of points used in the calibration increased, the results improved. Specifically, there was a 5 mm improvement in the mean error.  
The choice of the number of points to perform the calibration is a trade-off: the more points used, the better results can be expected to be, but the time to acquire the signals will also increase. Additionally, note that depending on the system configuration, there will be a point at which increasing the number of points in the calibration does not appreciably improve the results.  
In this work, we have mostly shown the results when using six points, as we considered that it is a good trade-off solution since similar results to the formal calibration were obtained using a fraction of the time and resources.
# Static Analysis for ECMAScript String Manipulation Programs
## 5 Abstract Semantics of ECMAScript String Operations
### Abstract Semantics of length  
The abstract semantics of length should return a value, of the integer domain \(\mathsf{Const}\), that, in a sound way, approximates the length of all the possible strings of an automaton. The abstract semantics of length is defined by the function \(\mathsf{LE}^{\sharp}:\textsc{Dfa}/_{=}\to\mathsf{Const}\), computed by Algorithm 1, where \(\mathsf{Paths}:\textsc{Dfa}/_{=}\to\wp(\wp(Q))\) returns the set of the paths from the initial state to any final state of \(\mathsf{A}\)[35]. Given a path \(\mathsf{p}\in\mathsf{Paths}(\mathsf{A})\), we denote by \(|\mathsf{p}|\) the length of \(\mathsf{p}\).  
```
Input:\(\mathsf{A}=(Q,\Sigma,\delta,q_{0},F)\) Output:\(\mathsf{LE}^{\sharp}(\mathsf{A})\)
1 if\(\textsc{hasCycle}(\mathsf{A})\)then
2 return\(\top_{\mathsf{Const}}\);
3 else
4 lenights \(\leftarrow-1\);
5 foreach\(\mathsf{p}\in\mathsf{Paths}(\mathsf{A})\)do
6 if\(|\mathsf{p}|==\mathsf{lenights}\lor\mathsf{lenights}==-1\)then
7\(\mathsf{lenights}\leftarrow|\mathsf{p}|\);
8 else
9 return\(\top_{\mathsf{Const}}\);
10
11 end if
12
13 end if
14 return\(\mathsf{lenights}\);
15
16 end if
```  
**Algorithm 1**\(\mathsf{LE}^{\sharp}:\textsc{Dfa}/_{=}\to\mathsf{Const}\) algorithm  
If the input automaton has cycles, \(\mathsf{LE}^{\sharp}\) returns \(\top_{\mathsf{Const}}\) otherwise it checks that any path of the automaton \(\mathsf{A}\) has the same length (lines 5-8). Whenever the algorithm finds that there exists two paths in the automaton that have different lengths, \(\top_{\mathsf{Const}}\) is returned (lines 8-10). Due to the constant integers domain, the abstract semantics of length can give a precise answer only when any string of the automaton has precisely the same length. More accurate results can be obtained by using more precise integer abstract domains, e.g., intervals, as we will discuss in Section 6. For example, consider the automata \(\mathsf{A}\) and \(\mathsf{A}^{\prime}\) in Figure 8,\(\mathsf{b}\), respectively. \(\mathsf{LE}^{\sharp}(\mathsf{A})\) precisely returns 5, since all the strings recognized by \(\mathsf{A}\) have the same length, while \(\mathsf{LE}^{\sharp}(\mathsf{A}^{\prime})\) returns \(\top_{\mathsf{Const}}\).  
**Theorem 4**.: \(\mathsf{LE}^{\sharp}\) _is sound and complete. Formally,_  
\[\forall\mathtt{A}\in\textsc{Dfa}_{/=}.\textsc{Le}(\mathcal{L}(\mathtt{A}))= \gamma(\mathsf{LE}^{\sharp}(\mathtt{A}))\]
## 2 Materials and Methods
### Experimental Methodology
#### 2.2.6 Scanning Electron Microscope (SEM)  
The JSM-6510 scanning electron microscope (JEOL, Tokyo, Japan) was employed in this study to analyze the porosity of specimens for each formula. During the sample preparation process, the samples with a curing age of 28 d need to undergo a drying process and then be made into flakes. Moreover, to make the SEM test proceed normally, the sample is glued to the round table by a conductive band and sprayed with 20 nm gold powder to make the sample conduct electricity.
# Privately Generated Key Pairs for Post Quantum Cryptography in a Distributed Network
## 2 Background Information
#### Relevant Prior Work
During the verification, users regenerate the public key and shared secret value to generate the authentication signature value and match it with the received one for user verification [23].
# Physical and Respiratory Rehabilitation in Spinal Muscular Atrophy: A Critical Narrative Review
## 3 Results
### Respiratory Interventions
#### 3.2.2 Niv to Improve Respiratory and Sleep Parameters  
[PERSON] et al. investigated in a cohort of 13 children with SMA I the role of polysomnography, the provision of non-invasive positive pressure ventilation (NIPPV) for ventilatory support and physiotherapy, and the use of MI-E [46]. Pressures were set low (positive inspiratory airway pressure 12 cm H2O, positive expiratory airway pressure 4 cm H2O with a backup rate between 18 and 35 breaths per minute depending on the child's spontaneous respiratory rate and inspiratory time); settings were then rapidly increased to ensure good chest wall movement and eliminate paradoxical breathing. NIPPV and MI-E were used for protocol-guided successful extubations (n = 9) but not for those not guided by the protocol (n = 3). NIPPV was essential for home discharge in ventilator-dependent patients (n = 7) and was used for palliation of respiratory symptoms (n = 4). The chest wall shape improved with NIPPV. Parents of deceased children (n = 5) were positive about using these techniques.  
[PERSON] et al. treated 10 boys with SMA II (aged 8-12 years) with nightly BiPAP for more than 12 months, and all had normal blood gases [53]. All children used BiPAP ventilators set in spontaneous/timed mode. Positive inspiratory/expiratory airway pressures were set at 12-20 cmH2O and 4-7 cmH2O, respectively. The respiratory rate was set slightly below the rate of spontaneous breathing.  
[PERSON] et al., in a cohort of twelve children with SMA I or II, investigated the use of non-invasive ventilation during sleep in six patients with SMA I and one with SMA II, both with sleep disorder, adopting polysomnography and a symptom questionnaire [54]. Five less severely affected patients had no sleep disorders and served as the reference group. In the treatment group, non-invasive ventilation with BiPAP was used for 7-12 h of sleep during the night with the following ventilator settings: positive inspiratory airway pressure range of 8-14 cmH2O, positive end-expiratory pressure range of 3-4 cmH2O, and backup respiratory rate range of 16-24 ppm. Patients in the reference group and their caregivers, on the other hand, were trained in assisted coughing techniques, which they applied daily.  
[PERSON] et al. investigated the effectiveness of non-invasive ventilation in treating thoracabdominal asynchrony during sleep in children with SMA I and II [58]. After baseline polysomnography, the examination was repeated following nasal BiPAP. BiPAP was introduced by using it for 20 min, two or three times a day, before the sleep study. All patients used high-flow BiPAP (range 14-20 cm H2O) to achieve good chest and alveolar expansion.
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 2 Method
### Predication Approaches
#### 2.1.2 Machine Learning Algorithms in Grade Prediction  
During the scope of this research, a unique hybrid approach known as RFKNN-MPA was evaluated alongside six different machine learning algorithms. These algorithms included RF, K-NN, GPR, DT, and FCN. These algorithms were selected because of their ability to represent non-linear correlations with little to no user input, lack of assumption requirements, long track records of success, and proven robustness.  
Random Forest (RF)  
The foundation of RF is an ensemble of regression trees, a family of algorithms that divide the training data into classes of probability based on a set of if-then rules. The CART algorithm ([PERSON], 1984) serves as the foundation for RF. This algorithm fits a single tree, utilizing the whole training data. Unlike other tree approaches that only create a small number of trees, RF generates hundreds or thousands of decision trees ([PERSON], 1987). The fundamental concept of RF is to repeatedly choose bootstrap samples from the given data, construct a decision tree (DT) for each bootstrap sample, and combine the estimates from several DTs into the prediction model. A single DT can overfit the data, resulting in a lower bias but greater prediction variance. Averaging the predictions from multiple DTs can achieve a trade-off between bias and variance ([PERSON], 1984).  
The strategy relies on identifying the most significant data error as the primary error and correlating other data inaccuracies with it. Selecting a general error based on the most data gives us a more accurate and reliable forecast. This prediction becomes even more accurate when the data fed into the model does not change ([PERSON], 1987). It is widely acknowledged that this algorithm is among the most effective learning algorithms due to its ability to accurately classify various inputs ([PERSON] et al., 2017). One of the strengths of this classifier is that it performs quite well even when used in massive datasets ([PERSON] et al., 2017). The most crucial quality of random forests is their superior performance in assessing the significance of variables, which establishes the contribution of each variable to the prediction of outcomes ([PERSON] et al., 2017).
# Bricks Using Clay Mixed with Powder and Ashes from Lignocellulosic Biomass: A Review
## 5 The Effect of Lignocellulosic Biomass Ash By-Products on Fired Bricks
### Effect on Thermal Properties  
The improvement of insulation properties through the reduction of thermal conductivity of building materials is crucial for the development of energy-efficient buildings. Therefore, adding lignocellulosic biomass ash to clay bricks, which produce porosity during firing, may be a promising way to create thermally insulating bricks. Ref. [60] showed that the thermal conductivity decreases steadily as olive bottom ash (OPBA) residue is added up to 20% by weight. When 20 wt.% OPBA was added to the bricks, the values decreased to 0.82 W m\({}^{-1}\) K\({}^{-1}\) from 0.99 W m\({}^{-1}\) K\({}^{-1}\) for bricks without additives. Therefore, the addition of 10 wt.% of olive bottom ash (OPBA) resulted in a 14.1% improvement in the thermal conductivity of the bricks with a thermal conductivity value of 0.85 W m\({}^{-1}\) K\({}^{-1}\) (Figure 6). In addition, a similar trend was observed by [68]. They noticed that as the amount of sugar bagasse ash (SBA) and rice husk ash (RHA) in fired clay bricks increases, the thermal conductivity values decrease. They obtained a thermal conductivity at the vicinity of 0.52 W m\({}^{-1}\) K\({}^{-1}\). Moreover, a low thermal conductivity was observed with the incorporation of 6.5% of oil mill wastewater (OMW) [63], and an improvement of almost 65% was observed. In all the studies performed, the thermal properties of the bricks were limited to the calculation of thermal conductivity, except [PERSON] et al. [31] for which the measurement of acoustic insulation was performed.  
Many factors such as mineralogical composition, type of porosity (open or closed porosity), pore size, measurement method, and density are important factors that govern thermal conductivity. The thermal conductivity of bricks made with lignocellulosic biomass ash is significantly influenced by the brick density. More thermally insulating bricks are produced when the density is lower. When comparing Figures 4 and 5, it can be seen that adding 6.5% of olive mill waste (OMW) produced the lowest density (1110 kg/m\({}^{3}\)) and lowest thermal conductivity (0.143 W m\({}^{-1}\) k\({}^{-1}\)), whereas adding 10% of wood stone ash (WA) produced the highest density (1800 kg/m\({}^{3}\)) and highest thermal conductivity value (1 W m\({}^{-1}\) k\({}^{-1}\)). This might be accounted for by the fact that a low density permits a higher percentage of air volume to fill the voids. As a result, by serving as effective insulator, the greater volume of air in the pores lowers the solid matrix's overall thermal conductivity.
## 4 Results and Discussion
### Surface Current Distribution (A/m)  
To better understand the working mechanism of the proposed mobile MIMO antenna, the surface current distribution at frequencies 3.5 GHz and 5.1 GHz is presented in Figure 7. As observed from Figure 7a, the maximum current flows in the lower part of the radiator, i.e., the E-shaped structure, which results in the generation of resonance at 3.5 GHz. Similarly, from Figure 7b, it is visualized that the current flows through the octagonal ring as well as from the inverted L-shaped strip, which helps in the generation of resonance at 5.1 GHz. So, these two modes are overlapped with the fundamental mode of 3.5 GHz lying in the frequency range of 2.37-5.85 GHz band.  
Figure 6: Simulated and measured transmission coefficient of 4-element mobile MIMO antenna.  
Figure 7: Current distribution of 4-element mobile MIMO antenna at (**a**) 3.5 GHz and (**b**) 5.1 GHz.
## 5 Simulation Results
### Data Delivery Probability
In addition, the proposed distributed RA scheme does not need the procedure of resource allocation and controls the \(PLR\) strictly, which reduces the communication delays caused by resource assignment and packet retransmission. Therefore, the proposed distributed RA scheme is more suitable for the future low-cost interactive satellite communication scenario with a large network size and high EH rate.