esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Modeling
### Hovering Within the Triple NEA System (153591) 2001 SN263
#### 4.2.1 System Environment  
The primary (largest central body) of the system 2001 SN263, named Alpha, is a spheroidal body measuring \(\sim\)\(2.8\times 2.7\times 2.9\) km (\((x,y,z)\) axis) and equivalent diameter of \(\sim\)2.5 km, with density \(\sim\)1.13 g/cm\({}^{3}\), mass \(\sim\)917.47 \(\times\) 10\({}^{10}\) kg and sidereal rotation period \(\sim\)3.4 h. The larger moonlet, Beta, is a slightly elongated body (\(\sim\)0.7 \(\times\) 1 \(\times\) 0.6 km) and equivalent diameter of \(\sim\)0.8 km, with density \(\sim\)1.0 g/cm\({}^{3}\), mass \(\sim\)24.04 \(\times\) 10\({}^{10}\) kg and sidereal rotation period \(\sim\)13.4 h. The smaller moonlet, Gamma, is almost spherical with an equivalent diameter of \(\sim\)0.4 km, density \(\sim\)2.3 g/cm\({}^{3}\), mass \(\sim\)9.77 \(\times\) 10\({}^{10}\) kg and sidereal rotation period \(\sim\)16.4 h. Beta's orbit around Alpha has a semi-major axis equal \(\sim\)16.6 km, orbital period of \(\sim\)149 h, eccentricity 0.015 and is almost aligned with Alpha's equatorial plane. The orbit of Gamma has a semi-major axis of \(\sim\)3.8 km, eccentricity 0.016, an inclination of \(\sim\)15 deg relative to Alpha's equatorial plane and an orbital period of \(\sim\)16.5 h, which makes the body's rotation almost tidally synchronized with its orbital motion [74,147].  
As the system is composed of bodies with similar physical characteristics, such as size and density, and are close to each other, a spacecraft traveling in their neighborhood will experience perturbations arising from all the bodies. This will be decisive in determining the regions of stability and instability inside and outside the system, a feature whose understanding and characterization are essential for the designing of rendezvous mission aiming to explore this NEA system [59,63,86,148].  
Ref. [43] mapped long-lasting orbits around the main body of the triplet. The map was built from a grid of initial conditions concerning semi-major axis and eccentricity. The orbits were numerically integrated for a period of 500 days, double the time planned for the ASTER mission. The model includes perturbations from the oblateness of Alpha, the gravity field of the two secondary bodies, the Sun, the Moon, the asteroids Vesta, Pallas, and Ceres, and all the planets of the Solar System. These perturbations were considered individually to identify their relative strength. The results showed that Gamma is the main perturbing body, followed by Beta, 10 times smaller, and the group Sun-Mars-Alpha's oblateness, with perturbations 1000 times smaller than that of Gamma. The other bodies have perturbations 10\({}^{7}\) times smaller. The results also showed that circular and polar orbits are less perturbed when compared to elliptical and equatorial ones. Regarding the semi-major axis, an internal orbit is the best choice, followed by a larger external orbit. The inclination of the orbit plays an important role in their model, so it is necessary to make adequate orbit selection to attain minimum disturbance.
# Description of Thermochromic Offset Prints Morphologies Depending on Printing Substrate
## 3 Results
### Structural Characterization of Thermodynamic Printing Ink by FTIR Analysis  
The main components in offset ink are the colorant, the binder or vehicle, solvent (ink oils) and additives (catalytic dryers, antioxidants, wax particles, and others). In classic offset inks, the colorant is usually a hydrophobic organic pigment, while the binder consists of alkyl and hard resins. In the used thermochromic ink, the pigment is microcapsule containing leuco-dye-developer system. Ink oils can be classified into mineral (mixture of hydrocarbons from petroleum distillates) and vegetable oils (esters). Drying of used offset ink occurs by penetration of the ink vehicle into the paper structure. Ink oil usually penetrates deeper than pigments and resins. The amount of pigments, resins, and oil that remain on the printed surface or penetrates into the paper, depends mainly on the amount of ink transferred, the rheological properties of the ink, and the structural properties of paper, such as roughness and porosity [32].  
FTIR spectrum of thermochromic printing ink (Figure 6) shows vibrational bands characteristic for vegetable and mineral oils [32; 53; 54; 55; 56]. Oils have vibrational bands from 2925 to 2850 which are assigned to -CH, -CH\({}_{2}\), and -CH\({}_{3}\) stretching bonding vibration of aliphatic chains. In addition, vegetable oils show bands at 3010 (alkenyl CH stretching), 1463 (CH, CH\({}_{2}\) stretching), 1166 (C-O stretching), 1101 (C-O stretching), and 721 (-CH\({}_{2}\)- rocking) cm\({}^{-1}\) and an intense band at 1740 cm\({}^{-1}\), assigned to the carbonyl group of esters [32; 57; 58; 59; 60]. The bands at 1463 cm\({}^{-1}\) (aromatic C-H bending) and 1376 cm\({}^{-1}\) indicate the presence of mineral oil [32; 61]. The binder in used thermochromic ink therefore contains mineral and vegetable oils that cover microcapsules in a way that they cannot be detected by FTIR analysis. This behavior was also confirmed in research by [PERSON] et al. [24] and [PERSON] et al. [23]. The proportion of vegetable oils used to manufacture cold-set ink is quite low (less than 5%) [32]. The ink may contain resins in a binder, as well, but the resin bands may overlap with vibrational bands of oils [32; 62].
## 3 Engineering Case Analysis
### Case History 3  
The plane size of the excavation area of a certain foundation pit [27] adjacent to Shanghai Metro Line 1 was \(L=70\) m, \(B=42\) m, and the excavation depth was \(d=10\) m. Metro Line 1 was located under the road beside the foundation pit. The distance between the retaining structure and the shield tunnel axis was \(s=7.2\) m. The outer diameter of the shield tunnel segment ring was \(D=6.2\) m, the ring width \(D_{\text{t}}=1\) m, the lining thickness \(t=0.35\) m, and the calculation parameters \(k_{\text{sl}}=2.23\times 10^{6}\) kN/m and \(k_{\text{t}}=9.39\times 10^{5}\) kN/m. The depth of shield tunnel was 10.1 m and was located in a layer of sandy silt and silty clay. The excavation range of the foundation pit was mainly filled with soil, silty clay, and sandy silt. The Poisson's ratio of the soil was \(\mu=0.35\), \(E_{\text{s}}=7.35\) MPa.  
Figure 12 displays the comparison curve between the value calculated and data measured of the horizontal displacement of the shield tunnel. As the figure shows, the calculated results are in good agreement with the measured values. The rotation effect ratio coefficient of the shield tunnel beside the foundation pit is \(j_{\text{x}}=0.2\). In the calculation example, the control parameter is \(v_{\text{max}}/d=0.25\%\) of the deformation of enclosure structure. The maximum measured horizontal displacement is 3.66 mm, and the maximum horizontal displacement calculated is 3.68 mm. The curve of the horizontal displacement presents a normal distribution, and the horizontal displacement is relatively large near the center of the excavation. Furthermore, it is decreasing on both sides. Similar to case history 2, the length of the central uplift affected area on the shield tunnel is about twice the length of the sidewall of the foundation pit along the tunnel axis.  
Figure 11: Longitudinal distribution of shear force between the shield tunnel rings in case 2.  
The length of the foundation pit is closed to that of case 2, while the excavation depth in case 3 is only about 60% of that in case 2, so the sidewall of the foundation pit is less deformed. The resulting soil deformation is relatively more minor, so the horizontal displacement of the adjacent shield tunnel in case history 3 is more diminutive compared with case history 2, but the horizontal displacement distribution law is similar to that of case history 2.
## 3 Results and Discussion
### Microstructure  
The microstructure of the resistance spot weld could be divided into three zones, i.e., BM, HAZ, and FZ. Figure 4 shows the microstructure of the aluminum 6061-T6 matrix, as observed with the OM. The base material mainly consisted of grains elongated in the rolling direction, as shown in the figure. Figures 5 and 6 show the microstructures of the HAZ and FZ of the welds produced with the as-received and abraded electrodes, respectively. As shown in Figure 5a,b, the microstructure did not exhibit any partially melted zone at the electrode force of 400 kgf; however, a partially melted zone was observed near the columnar grain zone in the microstructure formed under 600 kgf. In addition, at the center of the FZ, an equiaxed grain zone was observed, as shown in Figure 5c,d; however, the grain size did not considerably change with the electrode force. The microstructures of the welds produced using the abraded electrode were not significantly different from those produced with the as-received electrode. As shown in Figure 6a,b, a columnar grain zone and an HAZ were observed on either sides  
Figure 3: Measured hardness distribution of welds produced at the electrode force of 600 kgf.  
Figure 2: Measured hardness distribution of welds produced at the electrode force of 400 kgf.  
of the fusion line; in addition, a partially melted zone was observed. Moreover, in the center of the FZ, an equiaxed grain zone was observed (Figure 6c,d). As shown, the weld microstructure was not significantly affected by the electrode surface roughness. The grain size decreased from the HAZ, to the base material and finally the fusion zone.  
Figure 4: Microstructure of the base material used in this study.  
Figure 5: Microstructures of heat-affected zone (HAZ) and fusion zone obtained with the as-received electrode at electrode forces of 400 kgf and 600 kgf.
# Trusted Academic Transcripts on the Blockchain: A Systematic Literature Review
## 5 Contribution Overview
### Hyperledger Prototypes  
Despite being open-source and adaptable for both public and private blockchains, Hyperledger is usually preferred for the latter [91]. Almost all the proposals and prototypes found in this study using Hyperledger share the characteristic of being permissioned. One main difference between Bitcoin and Hyperledger prototypes is the possibility to access or alter data according to the user's rights. Aiming to reduce the asymmetry between employees and academic institutions, [PERSON] et al. [79] developed their proposal considering academic as well as employment data. Access on the Hyperledger blockchain, as also in [PERSON] et al. [11], is restricted according to the user type. Their model, however, is more focused on transaction speed and ease of record transfer whilst also guaranteeing data integrity [80]. [PERSON] and [PERSON] [81] also followed a similar approach, using simulated data to demonstrate the reliability of their prototype. The system also adopts a Byzantine Fault Tolerance consensus mechanism and enables a reduced number of nodes. In their research, of over 100 instances, the mean time for creating and verifying certificates was around three minutes.  
[PERSON] and [PERSON] [22] offered a more comprehensive approach, which discussed the platform's content and reliability. Following [PERSON] et al. [79] and [PERSON] et al. [8], the proposed system records the transcript and a student's performance on specific courses with skills acquired. Accordingly, the expected outcome would be the reduction of asymmetry between employers and academic institutions. Discussing the advantage of the implemented model, the authors confirmed the necessity of trusting humans behind the system, doubting then complete automation of the process. Finally, [PERSON] et al. [77] proposed a system that appears to be more similar to ones that made use of Bitcoin (for encryption and output types), and is the only Hyperledger based system that makes use of a public ledger. Whilst it should be noted that their proposal is at a very early stage when compared to the others in the sample.
# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 3 Discussion
### Impact on Biomaterials Research  
Machine learning techniques have significantly advanced the study of polymers, metals, composites, and inorganic materials. For instance, the use of databases like Citrination and The Materials Project has enabled researchers to explore extensive chemical descriptors, leading to a better understanding of the properties and behaviors of different metal compositions [100]. In the realm of biomedical applications, ML algorithms like random forest classifiers have achieved remarkable accuracy in predicting the behavior of nanoparticles, facilitating their use in medical contexts [101].  
Similarly, ML has transformed the design and discovery of polymer-based biomaterials. Techniques such as LAD regression and QSAR models have optimized gene delivery systems and predicted protein adsorption on biodegradable polymers [102; 103]. High-throughput methods and projects like the Polymer Genome have further accelerated the discovery and optimization of novel polymers with desired properties [104]. In composite materials, ML approaches, including statistical models and deep learning, have enabled the prediction of elastic localization, structural properties, and mechanical behaviors [105; 106]. These advancements have facilitated the design of bioinspired hierarchical composites and nanocomposites optimized for specific applications. For inorganic materials, ML frameworks have predicted various properties, including band gap energy and glass-forming ability [83]. The importance of chemical diversity in training data for generalizable predictions has been highlighted, emphasizing the need for diverse datasets to improve ML model accuracy.
# An Audio-Based Method for Assessing Proper Usage of Dry Powder Inhalers
## 3 Results  
### Classification  
The last step of this algorithm (as shown in Figure 4), after extracting features, is the process of classifying the audio signals into one of the final classes. The database of 1600 audio files is used to train the classifiers and to find the one that achieves the highest classification performance among the data. These data are split into training and test data, 75% for algorithms' training and 25% for the testing.  
Figure 4: Flow diagram of the proposed algorithm, where first silence removal is performed in the audio signal and then the classification of each extracted segment into four targeted classes, i.e., _Actuation_, _Inhale_, _Button press_, _Exhale_.  
Various classifiers were used for this purpose, such as k-Nearest Neighbors (k-NN) [38], Extra Trees [39], SVM [40], Gradient Boosting (GB) [41], and Random Forest [36]. The following metrics were selected to evaluate the results of each classifier: Accuracy, F1-score, precision, and recall. Gradient Boosting achieved the highest accuracy of 90.92% and an F1-score of 89.87% (Table 2). For the performance evaluation of the classifiers, 10-fold cross validation was used and the results were calculated on the testing set. The silence removal procedure is not used in this step since the database consisted of fixed-sized audio files of 2 s.  
Gradient Boosting is a technique for classification tasks in the field of machine learning. It produces a prediction model in the form of an ensemble of weak prediction models (decision trees). The model is built in a stage-wise fashion, and the generalization is being performed by an arbitrary differentiable loss function.  
Gradient Boosting algorithm can easily overfit [41] a training data set, and that is why different regularization methods could be applied to improve the algorithm's performance and encounter the problem of possible overfitting. A large number of boosting stages to perform usually results in better performance, and for that reason 500 estimators are used. Furthermore, the maximum number of features are taken into account when looking for the best split and the minimum number of samples required to split an internal node are 10. In addition, \"learning rate\" is set to 0.1 since the parameter's tuning resulted in a better classification score after performing a grid-search between 0.001 to 0.1 with steps of 0.05.
# Research on Solidification Methods and Stabilization Mechanisms of Sulfate Saline Soils
## 3 Experimental Design and Methods  
### Sample Preparation  
The saline soil was washed several times with repeated water and dried to obtain the soil, which was crushed and passed through a 2.00 mm sieve. Different proportions of binding materials (time, FA, PAM) were added to the soil and sprayed with sodium sulfate solution calculated proportionally. After mixing well, the soil was placed in a sealed bag and simmered for 12 h to evenly distribute the moisture and sulfate. Experiments used the molding method with reference to the standard [35], the standard for the inorganic binding material using two-way static compression method, and then stabilized pressure for 2-3 min. Stabilized soil is divided into 2-3 layers and put into the mold for compaction. Then, 100 \(\times\) 100 mm cylindrical specimens were prepared via the hydrostatic press method, wrapped in cling film, and labeled. The specimen was placed in a closed, constant-temperature environment for curing. The prepared sample had a moisture content of 12.5% and a dry density of 1.894 g.cm\({}^{-3}\).  
Stabilized soil consists of four components: 1% sodium sulfate, soil, binding material, and water. Stabilized soils with Wt of 6.0%, 8.0%, and 10.0% lime are denoted as L1, L2, and L3, respectively; those with Wt of 9.0%, 11.0%, and 13.0% FA are denoted as F1, F2, and F3, respectively; and those with Wt of 0.5%, 1.0%, and 1.5% PAM are denoted as P1, P2, and P3, respectively.
## 4 Performance
### Initial Stage  
The sensor nodes tried to send data packets to the sink node when there was a path to it. Figure 9A shows the packet reception ratio at the sink node in the initial stage. The successfully received packets sent from 72 sensor nodes in the failure affected area were counted every second and divided by the number of sent packets. Figure 9B shows the number of broadcast packets sent by the 72 sensor nodes. The broadcast packets were used for routing control messages.  
In Figure 9A, the 100-s period from 0 s to 100 s shows the initial stage of network configuration. In this period, ARF showed the fastest performance in the construction of a routing tree. Proc and MintRoute with 5,000 ms intervals showed similar performances. As explained in Section 3.3, the initial stage could be regarded as the failure of the entire network, and thus, routing protocols with short transmission intervals could be used to construct the routing tree. ARF set the routing interval at 5,000 ms for failure and routing recovery and thus showed a fast routing construction speed. In the initial stage, all of the sensor nodes in ARF broadcast routing control messages with orphan flags in failure detection for a while, and the sensor nodes near the sink node started to broadcast routing control messages with the proper routing tree. As the routing tree was being constructed from the sink node to the leaf nodes, the sensor nodes with a parent node increased their routing interval from 5,000 ms to 20,000 ms. As a consequence, the number of broadcasts in ARF increased drastically between 0 s to 10 s and then decreased slowly between 20 s and 35 s, as shown in Figure 9B. The number of broadcast messages in Proc and MintRoute shows almost constant since Proc and MintRoute use fixed routing interval. The Proc protocol with 5,000 ms also constructed the routing tree quickly, similar to ARF, but the number of broadcast packets remained at a high rate. MintRoute with the 5,000 ms interval also showed a high rate of message routing, while the packet reception ratio was very low compared to ARF and Proc with 5,000 ms. Both Proc and MintRoute with the 10,000 msinterval transferred fewer routing control messages but the routing tree was constructed very slowly. As a result, ARF could construct fast the routing tree with fewer routing control messages at the initial stage.  
Figure 9: Packet reception ratio and broadcast packets in initial stage.
## 3 Inverse and Forward Kinematics Problems
### Coordinate System Transformation  
Besides the translation vector \(\mathbf{T}\) of the platform's frame in reference to the global frame, it is necessary also to define a matrix \(\mathbf{R}\) that represents the rotation matrix, whose elements are functions of the three Euler angles that determine the orientation of the moving platform's reference frame in reference to the global frame. To ease writing, a condensed notation of the trigonometric functions sine and the cosine, \"\(\mathbf{s}\)\" and \"\(\mathbf{c}\)\", respectively, are used to define the rotation matrix \(\mathbf{R}\) as follows:  
\[\mathbf{R}=\mathbf{R}_{z}(\psi)\mathbf{R}_{\mathbf{y}}(\theta)\mathbf{R}_{\mathbf{x}}(\varphi)=\begin{bmatrix} \zeta_{\varphi}\,\zeta_{\varphi}&-\varsigma_{\varphi}\,\zeta_{\varphi}+\zeta_ {\varphi}\,s_{\theta}\,s_{\varphi}&s_{\varphi}\,s_{\varphi}+\zeta_{\varphi}\, s_{\theta}\,c_{\varphi}\\ \varsigma_{\varphi}\,c_{\theta}&\,c_{\varphi}\,c_{\varphi}+s_{\varphi}\,s_{ \theta}\,s_{\varphi}&-c_{\varphi}\,s_{\varphi}+s_{\varphi}\,s_{\theta}\,c_{ \varphi}\\ -s_{\theta}&\,c_{\theta}\,s_{\varphi}&c_{\theta}\,c_{\varphi}\end{bmatrix} \tag{13}\]