esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The surface of the analyzed object (e.g., a pharmaceutical formulation) can reflect, scatter, and/or absorb light radiation from the reflectometer. Knowing the amount of light radiation that is scattered or reflected from the tablet, it is possible to determine how much radiation is absorbed by it. Thus, the strength of the influence of the given spectral ranges on the pharmaceutical product can be determined. Except for our recent study [16], the measurement of hemispherical reflectance has not yet been used to evaluate the stability of pharmaceutical products. Earlier, this method was used to analyze counterfeit drugs [17]. Other studies have demonstrated that the analysis of reflectance and transmittance can be used to predict drug content [18], as well as to measure hardness and porosity using the near-infrared diffuse reflectance spectroscopy method [19]. In turn, computer microtomography uses X-rays to analyze the inner structure of the analyzed object. Different objects absorb X-rays differently, which is affected by both the density and thickness of the material they are made of. Thicker and denser materials absorb more radiation. With this method, individual elements can be distinguished in the tested object with an accuracy of 1 \(\upmu\)m. In pharmacy, microtomography is able to visualize any structural defects, such as micro-cracks, air bubbles, or delamination [20; 21]. Several studies have demonstrated the usefulness of X-ray microtomography in evaluating pharmaceutical products, including modified-release tablets containing pellets with theophylline [22], multiple-unit pellet system (MUPS) tablets [23], as well as mini-tablets with moxidectin [24]. Both hemispherical reflectance and X-ray microtomography have several important advantages, including ease of sample preparation, speed, and saving of test samples. The present study aimed to determine whether directional hemispherical reflectance allows for the shelf life of effervescent tablets containing magnesium and vitamin B6 to be assessed during storage. In addition, we analyzed selected pharmacopeial parameters that are used to describe tablets as a standard, and we used X-ray microtomography to evaluate the internal structure of the tablets.
## 3 Results
### Impaction Depth  
In the [PERSON] and Gregory classification, class A (53.4%) was the most common, followed by class B (34%), and class C (12.6%). For Class I, II, and III classification, Class II (55.3%) was the most common, followed by Class III (33.0%) and Class I (11.7%). When the two categories were combined and confirmed, Class IIA (30%) occupied the largest proportion, followed by Class IIB (19%), Class IIIA (16%), and Class IIIB (12%) (Table 2).  
Figure 5: Relationship between the inferior alveolar canal and roots in cone beam computer tomography views: (**A**) root darkening in panoramic view, (**B**) root deflection in panoramic view, (**C**) root narrowing in panoramic view, (**D**) dark line in apex in panoramic view, (**E**) loss of white line in panoramic view, (**F**) inferior alveolar nerve (IAN) narrowing in panoramic view, and (**G**) IAN diversion in panoramic view.  
Age and Pell and Gregory classification (P.G.) showed a statistically significant correlation as a result of [PERSON]'s chi-square test, with a significance less than 0.05 (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **No.** & **\%** \\ \hline Total & & 6182 & 100.0 \\ \hline \multirow{2}{*}{Gender} & Male & 3220 & 52.1 \\  & Female & 2962 & 47.9 \\ \hline \multirow{6}{*}{Age} & 10–19 & 1026 & 16.6 \\  & 20–29 & 3518 & 56.9 \\  & 30–39 & 921 & 14.9 \\ \cline{1-1}  & 40–49 & 436 & 7.1 \\ \cline{1-1}  & 50–59 & 178 & 2.9 \\ \cline{1-1}  & 60–69 & 75 & 1.2 \\ \cline{1-1}  & 70– & 28 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distributions of gender and age.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **No.** & **\%** \\ \hline Total & & 10,310 & 100.0 \\ \hline \multirow{2}{*}{P.G. A, B, C} & A & 5509 & 53.4 \\  & B & 3503 & 34.0 \\  & C & 1298 & 12.6 \\ \hline \multirow{2}{*}{P.G. I, II, III} & I & 1204 & 11.7 \\  & II & 5704 & 55.3 \\ \cline{1-1}  & III & 3402 & 33.0 \\ \hline \multirow{6}{*}{P.G. A, B, C and I, II, III} & IA & 756 & 7.3 \\ \cline{1-1}  & IB & 320 & 3.1 \\ \cline{1-1}  & IC & 128 & 1.2 \\ \cline{1-1}  & IIA & 3088 & 30.0 \\ \cline{1-1}  & IIB & 1990 & 19.3 \\ \cline{1-1}  & IC & 626 & 6.1 \\ \cline{1-1}  & IIIA & 1665 & 16.1 \\ \cline{1-1}  & IIIB & 1193 & 11.6 \\ \cline{1-1}  & IIIC & 544 & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of the Pell and Gregory classification.  
Figure 6: Distribution of the Winter classification: horizontal impaction (42.1%), mesioangular impaction (29.8%), vertical impaction (22.9%), inverted impaction (2.7%), distoangular impaction (1.5%), and transverse impaction (0.9%).
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 3 Pseudo-Identity Based Encryption Framework
### Key Replacement and Key Revocation  
It is quite natural that hosts and network devices, such as routers and switches, are replaced in a regular interval. The new, replaced device may have the same-old name. One of the biggest challenges in the industry is how to ensure that decommissioned devices are not reconnected to the network in order to launch a replay attack using old profiles. Identity-based encryption cannot handle this case, as there is no change in the public identity of the replaced host. We now explain how the key-replacement problem can be solved using pseudo-identity based encryption, without changing the node's identity.  
Our key-replacement procedure also handles cases where a particular key is compromised.  
Without loss of generality, let \(X_{1}\) be the host whose key \(<\)\(PK_{1}^{1}\), \(SK_{1}^{1}\)\(>\) needs to be replaced. However, we wish to keep its identity \(X_{1}\). Previously, we have used \(r_{1}^{1}\) as the smallest integer such that \(SK_{1}^{1}=f(X_{1}+r_{1}^{1})=d+R(X_{1}+r_{1}^{1})(mod\ N)\) has a public-key component \(PK_{1}^{1}\). Now, choose the next higher integer \(r_{1}^{2}\) such that \(SK_{1}^{2}=f(X_{1}+r_{1}^{2})=d+R(X_{1}+r_{1}^{2})(mod\ N)\) has a public-key component \(PK_{1}^{2}\).  
Once new keys are found for \(X_{1}\), the hash-polynomial is recomputed. The polynomial is securely communicated to all existing and active nodes, through the multicast tree architecture discussed in the following section. Whenever a decommissioned node uses its old keys to launch a replay attack, it may not able to communicate with the existing nodes, as existing nodes use its new keys.  
Whenever a key is compromised, we follow a procedure similar to the \"key revocation\" algorithm to generate a new pair of keys.  
Since our protocol involves strict admission control using multicast tree, any node with identity \(X_{i}\) being suspended in connecting to the multicast service network can be enforced easily.
## 3 TanSat Simulations and Retrieval Assumptions
### The A Priori and Measurement Error Assumptions
[54], [PERSON] and [PERSON] [33] and [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [20], we believe that the _a priori_ errors for non-CO\({}_{2}\) retrieved parameters are independent from one another, so \(\mathbf{S}_{a_{CO2}}\) is defined as a diagonal matrix. Combined with \(\mathbf{S}_{a_{CO2}}\), which is defined in Table 4 and Figure 4, the total _a priori_ error covariance matrix \(\mathbf{S}_{a}\) may be expressed as follows:  
\[\mathbf{S}_{a}=\Bigg{[} \begin{array}{cc}\mathbf{S}_{a_{CO2}}&0\\ 0&\mathbf{S}_{a_{other}}\end{array} \Bigg{]}. \tag{14}\]  
Because the calibration and systematic errors could be removed from the retrieval algorithm to some extent, only measurement noise was considered in this study, which is calculated by the spectra signal and corresponding SNR in each band as follows:  
\[\text{Noise}=\frac{\text{Spectra}_{\text{ACGS}}}{\text{SNR}_{\text{ACGS}}}. \tag{15}\]  
The corresponding SNR could be interpolated from the SNR curves provided by the experiments conducted in the laboratory (SNR curves for all bands are shown in the Supplement). Similarly, measurement noise was also assumed to be noncorrelated, so the error covariance matrix \(\mathbf{S}_{\varepsilon}\) is a diagonal matrix with the square of noise as the diagonal elements. Figure 4: The a priori CO\({}_{2}\) correlation matrix. The colors represent the error correlation coefficients between CO\({}_{2}\) concentrations at different levels, and the pressures are presented on the left.
# StereoVO: Learning Stereo Visual Odometry Approach Based on Optical Flow and Depth Information
## 2 Related Work
### Methods Based on Learning  
In recent years, learning-based VO has gained substantial attention for its promising prospects of learning capability and robustness to challenging environments.  
DeepVO [27] introduced an end-to-end framework for monocular VO that utilizes deep Recurrent Convolutional Neural Networks and requires five sequential frames for estimation, which results in greater memory consumption and poorer real-time performance. [PERSON] et al. [16] proposed a parallel CNNs framework where joint training for single view depth and VO improved depth prediction by imposing additional constraints on depth, achieving competitive results for VO. UnDeepVO [7] estimates the 6-DoF pose of a monocular camera using deep neural networks and leverages stereo image pairs to recover absolute scale. However, its inability to generalize to other datasets due to changes in absolute scale limit its application. Similarly, D3 VO [28] also employs stereo image pairs to train a monocular VO network and models photometric uncertainties to enhance VO accuracy, which results in a fixed absolute scale and cannot be generalized to other datasets. MLF-VO [29] estimates camera ego-motion by using paired monocular frames in an unsupervised manner, focusing on different depth and image feature fusion strategies, which utilizes Multi-Layer Fusion to leverage RGB and inferred depth information to predict ego-motion after predicting depth images for each source frame; however, dynamic objects are not considered. P-CNN [9] estimates frame-to-frame ego-motion from dense optical flow generated by BROX [30], which can be time-consuming. Flowdometry [10], trained by optical flow generated by FlowNet [31], achieved better results than P-CNN but did not consider absolute scale. TartanVO [32] utilizes monocular image sequences, optical flow labeling, and camera matrix from the TartanAir dataset [33], which offers a wide range of synthetic data, for training and can be applied to other real-world datasets such as KITTI [34] and EuRoC [35] by adjusting the intrinsic matrix. In addition to pure VO methods, there are learning visual-inertial odometry methods [36; 37] that combine visual and inertial together, which are not in the scope of this work.  
Although recent monocular VO methods [19; 27; 28] employ stereo frames for training to obtain absolute scale, they still cannot fully solve the issue of absolute scale that persists in all monocular methods. Additionally, most VO methods based on learning do not consider the effect of dynamic objects. In this work, we leverage stereo image pairs to recover scale and employ optical flow and depth information to consider the effects of dynamic objects.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 4 Results
### Ndvi  
Figure 8a shows the comparison of the NDVI values calculated from Sentinel-2A and Landsat-8. The ODR slope statistics is 1.08, and the RMSE is 0.036. In Figure 8b, the same comparison is presented, but after adjusting the reflectances using Equation (1). The ODR slope is reduced to 1.01, and the RMSE is reduced to 0.021.  
The Landsat-7 case is omitted here for the sake of brevity, but exhibits nearly identical behaviour.
## 2 Building Domain Dictionaries and Dictionary Matching Methods
### Dictionary Tree  
A prefix tree [38; 39] is an efficient data structure. It can store vocabulary in the domain of mine hoist equipment, thereby constructing a domain dictionary tree. For instance, we can store the three strings \"(Mine hoist)\", \"(Mine hoist)\" (main mine hoist)\", and \"(Auxiliary mine hoist)\" in the prefix tree, as shown in Figure 1.  
In this prefix tree, each node represents a character and the path from a root node to a leaf node represents a string. For example, the path from the root node \"(  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Entity Type** & **Examples** \\ \hline Failure phenomenon & The contact tip on the rectifier bar is melted off by heat, \\  & Severe speed reducer oscillation... \\ Fault Location & Hoist spindle bearing, Hydraulic system reducer... \\ Cause of fault & Spindle bearing breakage, Improper gear meshing... \\ Fault Impact & Hoist emergency brake, Hydraulic system light up alarm... \\ Repair measures & Optimization of the lubrication station cooling system, \\  & Smooth out the rectifier... \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of words in the field of mine hoisting fault.  
Figure 1: Example of a domain dictionary tree.  
leaf node \"hoist\" represents the string \"#\" +#\" +#\" +#\" +#\" (mine hoist)\". Storing strings in a prefix tree effectively reduces storage space because it saves space through sharing common prefixes. Moreover, string retrieval using a prefix tree is very efficient because we only need to search along the link that matches the first letter of the target string. This minimizes the number of string comparisons, thereby enhancing retrieval efficiency. The specific steps of the dictionary tree construction Algorithm 1 are as follows:  
**Input**\(s_{c}=\{c_{1},c_{2},\ldots,c_{n}\}\), a sentence with n characters \(s_{c}\)  
**Output**\(s_{cw}=\{(c_{1},ws_{1})\), \((c_{2},ws_{2}),\ldots,(c_{n},ws_{n})\}\)  
1. Initialize the word list \(WS_{i}=\{ws_{1},ws_{2},\ldots,ws_{n}\}\), which is derived from a pre-compiled list of domain-specific terms, where each \(ws\) represents a unique term.
2. Initialize the dictionary tree root node: _root_
3. for \(i\gets 1\) to n do
4. Initialize tree node: _node_
5. repeat
6. Iterate through the word list \(WS\) to get the words matching the matching words \(ws_{i}\)
7. Take \((c_{i},ws_{i})\) Constructing dictionary tree sub-nodes: \(sub\)
8. \(node\to next=sub,node=sub\)
9. until the traversal is complete \(WS\) stop
10. \(root\to next=node\)
11. end for ```  
**Algorithm 1**Dictionary tree construction algorithm
## 3 Results and Discussion
### Formation and Characterization of the Micro-Structured Surfaces
The obtained histograms show the depth values on the vertical left-side; each histogram contains a number of 20 bins, with the bin width of 0.03284 \(\upmu\)m (a-PDMS–LDSU), 0.06371 \(\upmu\)m (b-PDMS–MDSU) and 0.09467 \(\upmu\)m (c-PDMS–HDSU), respectively. The bin widths were assigned automatically by the software (APEX 3D BASIC software analyses V7, Digital Surf, Besancon, France, 2016, approved by ISO 25178). (**d**) Histogram representation showing the adhesion force values of the different PDMS replicates. units on the individual PDMS replicas induced an increase in the contact angle value. Our results are in good agreement with the data obtained using the sessile drop technique and reported in literature--i.e., 95.2 \(\pm\) 0.9 [38]. Surface free energy, as determined by the OWRK method, showed that the energy profiles mimic the hydrophobicity ones. Analysis also showed that, as the polar component increased, the density of the structural units increased, with the exception of HDSU interface that maintained this component close to that of the PDMS used as a control (1.34 mN\(\cdot\)m\({}^{-1}\)) [42; 43]. An increase in the polar component of surface energy (1.55 to 4.59 mN\(\cdot\)m\({}^{-1}\)) was observed and correlated to the increase in density of structural units for LDSU and MDSU, respectively.
## 3 Results and Discussion
### Evaluation of Ultimate Tensile Strength  
The strength values of six tensile test samples related to nozzle temperature are given in Figure 4. Average ultimate tensile strength (UTS) values were calculated at each temperature value. A significant decrease in UTS values was detected with increasing nozzle temperature. The largest mass decrease appeared when the nozzle temperature increased from 240 to 250 \({}^{\circ}\)C. Although the mechanical strength values of the samples tend to decrease with increasing temperature, the rate of decrease gradually lessens after 250 \({}^{\circ}\)C. There was a 41.52% decrease in the UTS value between the lowest and highest nozzle temperature values.  
Figure 4: The effect of nozzle temperature on ultimate tensile strength data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **SS** & **DF** & **MS** & **F** & **Critical F** & \(\mathbf{p}\) \\ \hline Nozzle Temperature & 147.864 & 5 & 29.572 & 3049.628 & 2.53 & \textless{}0.00001 \\ Error & 0.29091 & 30 & 0.0096 & & & \\ Total & 148.155 & 35 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ANOVA for the mass data.  
The results of the ANOVA test in which ultimate tensile strength data were examined are given in Table 6. It is seen that the change in nozzle temperature is a factor affecting the tensile strength data in the F and \(p\) values. The porous structure of ABS samples increases with increasing temperature. This situation causes a decrease in tensile strength values. The decrease in density at high temperatures indicates the decrease in tensile strength at these temperatures.
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 2 Data Review
### Training Dataset Construction
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Name** & **Abbreviation** & **Minimum Value** & **Maximum Value** & **Average Value** & **Unit** \\ \hline Advancing speed & AS & 0.031 & 234.245 & 36.577 & mm/min \\ Penetration & PE & 0.003 & 39.022 & 6.575 & mm/rot \\ Total thrust force & TF & 1063.310 & 17.741.300 & 12.474.052 & kN \\ Cutterhead rotation speed & CRS & 0.285 & 8.788 & 5.888 & r/min \\ Cutterhead torque & CT & 150.676 & 3827.090 & 1501.380 & kN\(\cdot\)m \\ Cutterhead average current & CAC & 48.901 & 390.743 & 188.012 & A \\ Pressure of chamber with rod of roof support & PWRS & \(-\)1.000 & 227.419 & 71.209 & bar \\ Pressure of chamber without & PORS & 22.031 & 263.534 & 115.070 & bar \\ rod of roof support & PWRS & \(-\)69.000 & 195.439 & 44.932 & bar \\ Pressure of chamber without & POLS & \(-\)1.000 & 236.899 & 96.525 & bar \\ rod of left support & Pressure of chamber with rod of right support & PWRS & \(-\)50.000 & 192.189 & 65.765 & bar \\
20 excavation parameters & Pressure of chamber without & PORS & \(-\)18.000 & 291.743 & 130.415 & bar \\ Pressure of chamber with rod of propulsion cylinder & PWRPC & \(-\)0.270 & 4.067 & 1.032 & bar \\ Pressure of chamber without & PWRPC & 30.104 & 229.142 & 159.032 & bar \\ Pressure of chamber with rod of left support & PWLS & 0.000 & 107.162 & 37.583 & bar \\ Pressure of chamber with rod of right support & PWRS & \(-\)1.000 & 96.993 & 41.707 & bar \\ Pressure of chamber without rod of left torque cylinders & POLT & 45.391 & 202.264 & 123.194 & bar \\ Pressure of chamber with rod of left torque cylinders & PWLT & \(-\)1.000 & 172.912 & 64.262 & bar \\ Pressure of chamber without rod of right torque cylinders & PORT & 44.074 & 147.439 & 91.565 & bar \\ Pressure of chamber with rod of right torque cylinders & PWRT & 22.114 & 166.529 & 100.531 & bar \\ \hline \end{tabular}
\end{table} 
Table 1: Statistical analysis of the excavation parameters. Figure 1: TBM tunneling cycle. The training dataset for TBMAP predictive model should be built from the preprocessed data. The model predicts the next moment TBMAP based on the current TBMAP and attitude control parameters. Four measuring parameters were selected to represent TBMAP, and two control parameters were selected to be the attitude control parameters, which were combined as the input of this model, and the next moment TBMAP representation data were taken as the output. Their names and abbreviations are shown in Table 1. The recorded TBMAP representation and control parameters data were all indexed by time so that they could be matched by time. The prediction time interval of TBMAP was set to 2 min. The next moment TBMAP representation data could be obtained by time index, which formed the training dataset of the TBMAP predictive model.
## 1 Introduction
The model (referred as ECFM-TKI in [24]) has been applied to predict the ignition process of Diesel and homogeneous charge compression ignition (HCCI) engines and is currently implemented in various commercial engine CFD software. With respect to modeling of spray flames and Diesel engines, numerous models, such as the Partially Stirred Reactor (PaSR) concept [25], have been formulated and implemented in OpenFOAM [26; 27]. When it comes 0-D/1-D frameworks, the number of studies featuring tabulated chemistry-based methods is rather limited. Its implementation, however, is potentially very useful as it allows to include detailed chemistry effects, as opposed to the commonly used empirically derived correlations for emission predictions. [PERSON] et al. [28] proposed a table look-up approach based on mixture fraction and reaction entropy as progress variable. Their methodology was implemented in an SRM and tested under constant pressure reactor conditions. [PERSON] and co-authors [10] simulated heat release and pollutant formation by means of a Flame Prolongation of Intrinsic Low Dimensional Manifold (FPI/ILDM initially proposed in [29]) as well as a timescale-based sub-model for NO formation. The tabulation method used constant volume reactors and a CO-CO\({}_{2}\)-based progress variable definition. Upon table generation, CO, CO\({}_{2}\), H, H\({}_{2}\) and the fuel molecule were stored as key species for the reconstruction of the thermodynamic state during engine simulation. The method was validated for a wide range of Diesel engine conditions. Within the SI engine simulation framework, [PERSON] et al. [30] proposed a two-zone 0-D model (referenced as One-Dimensional Coherent Flame Model-Tabulated Chemistry (CFM1D-TC)) where the chemical part of the combustion process was tabulated using laminar 1-D premixed flame solutions. In addition, a time-scale model was formulated to better represent the relaxation towards equilibrium of CO and NO species with the help of homogeneous -reactor calculations. [PERSON] et al. [31] implemented the previously mentioned ECFM-TKI [24] approach within a 0-D/1-D phenomenological combustion model for better knock prediction in spark ignited engines compared to the traditionally used Livengood-Wu [32] approach. Validation under both 0-D reactors and knocking SI engine simulations under stoichiometric conditions showed promising results when compared to the online chemistry solutions.