esa-sceva/satcom-chunk-collection huggingface dataset:
# Detailed Design and Aerodynamic Performance Analysis of a Radial-Inflow Turbine
## 4 Detailed Design of Rotor  
The general gaspath design system is employed in the rotor blades detailed aerodynamic design (Figure 6). The Bezier polynomial curve [26] is used to evaluate the hub and shroud curve imported by the preliminary design system. This curve is well suited to the gaspath design process to generate a smooth curve for the blade surfaces. The suction and pressure sides of the rotor blade at the hub and shroud can be computed by Equations (26)-(28). As is shown in the Figure 6, the cross product of the tangent vector to blade camberline, \(\overset{\rightarrow}{S}\), and the vector, \(\overset{\rightarrow}{B}\), directed along the quasi-normal line determines the vector \(\overset{\rightarrow}{T}\), which defines the local normal direction to camberline surface. By determining the hub and shroud contours at the suction and pressure sides, the intermediate surface coordinates are defined by connecting the hub and shroud surface points along the quasi-normal line.  
\[\left[\begin{array}{c}T_{x}\\ T_{y}\\ T_{z}\end{array}\right]=\overset{\rightarrow}{S}\times\overset{\rightarrow}{B} =\text{det}(\left[\begin{array}{ccc}i&j&k\\ \sin\theta_{c}\sin\varphi\sin\beta_{c}&\cos\theta_{c}\sin\varphi\sin\beta_{c}& \cos\varphi\sin\beta_{c}\\ +\cos\theta_{c}\cos\beta_{c}&-\sin\theta_{c}\cos\beta_{c}&\\ (x_{cs}-x_{ch})/L&(y_{cs}-y_{ch})/L&(z_{cs}-z_{ch})/L\end{array}\right]) \tag{26}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Azimuth Angle [deg.]** & **Radius of Center [mm]** & **Radius of Section [mm]** \\ \hline
1 & 0 & 57.90 & 1.00 \\
2 & 30 & 60.93 & 3.03 \\
3 & 60 & 62.23 & 4.33 \\
4 & 90 & 63.24 & 5.34 \\
5 & 120 & 64.11 & 6.21 \\
6 & 150 & 64.88 & 6.98  
\[\left\{\begin{array}{l}x=r_{c}\sin\theta_{c}\pm\frac{1}{2}t_{r}T_{x}\\ y=r_{c}\cos\theta_{c}\pm\frac{1}{2}t_{r}T_{y}\\ z=z_{c}\pm\frac{1}{2}t_{r}T_{z}\\ \cot\beta_{c}=r_{c}\frac{d\theta_{c}}{dm}\end{array}\right. \tag{27}\]  
\(\theta_{c}\) and \(\beta_{c}\) are blade and polar angle with respect to the tangent at the hub and shroud camberlines, respectively. The angle \(\varphi\) (Figure 6) is the meridional surface slope angle with respect to \(z\). It should be suitable to maintain a constant rotor tip radius for all meridional surfaces, according to Equation (29) and polar to Cartesian coordinate transformation.  
\[\left\{\begin{array}{l}\theta_{4}=\theta_{c,4}\pm\tan^{-1}\left[\frac{t_{r, 4}}{2r_{c,4}\left(T_{x}\cos\theta_{c,4}-T_{y}\sin\theta_{c}\right)}\right]\\ x_{4}=r_{4}\sin\theta_{4}\\ y_{4}=r_{4}\cos\theta_{4}\end{array}\right. \tag{29}\]  
The final geometry of the designed radial-inflow turbine for the air cycle machine is shown in Figures 7-9, which show the rotor blade angle and the polar angle in the hub and shroud section.
## 3 Test Results and Discussion
### Load-Deflection Characteristics  
The curves of applied load-deflection of the specimens are shown in Figure 7. The deflections were measured at the center of the slab. The characteristics of load-deflection behavior can be used to distinguish the failure types. As shown in Figure 7a,b, the load-deflection curves of the specimens CSR1-C1-F0 and CSR1-C2-F0 indicated that they experienced flexural failure. Both specimens exhibited an initial stiffness of the uncracked slab at early loading stage, and followed by a decreasing stiffness when the tensile flexural crack developed. These two curves were characterized by the relatively large deflection with a horizontal plateau before the slabs developed their ultimate strengths. The deflections at ultimate strength of the specimens CSR1-C1-F0 and CSR1-C2-F0 were 15.99 and 20.93 mm (in Table 3), respectively, which were much greater than those of the specimens failed in punching shear. As shown in Table 3, the deflections at ultimate strength of the specimens failed in punching shear were certainly less than 10 mm.  
Figure 7: Load-deflection curves: (**a**) CSR1-C1 series of specimens; (**b**) CSR1-C2 series of specimens; (**c**) CSR2-C1 series of specimens; (**d**) CSR2-C2 series of specimens.  
The remaining specimens failed in punching shear, as revealed by the load-deflection curves. These specimens had similar behavior and their load-deflection curves were generally similar. Linear load-deflection relations were observed at the early loading stage, after which non-linear behavior with reduced stiffness was noticed. Moreover, these curves exhibited a very steep descending branch right after the ultimate load.
# Novel 3U Stand-Alone CubeSat Architecture for Autonomous Near Earth Asteroid Fly-By
## 4 Subsystems Analysis
### Propulsion System
#### 4.2.1 Chemical Micro-Propulsion
Figure 4 reports the relation between required specific impulse and propellant mass to deliver 385 m/s to a CubeSat with a varying Beginning-Of-Life (BOL) mass, reporting in the chart the micro-propulsion systems with their characteristics; the comparison is between: VACCO MiPS [38], VACCO ArgoMoon MiPS [39], Aerojet Rocketdyne MPS-130 [40], Aerojet Rocketdyne MPS-120 [41], Busek BGT-X5 [42], NanoAvionics EPSS C1 [43], Hyperion Technologies PM200 and PM400 [44], and Tethers Unlimited HYDROS-C [45]. Figure 4 shows the performances of three ECAPS HPGP thrusters [46], taking into account the different thrusters mass. Both plots have been obtained from the rocket equation. From the evaluation chart, the most promising solution is the Aerojet Rocketdyne MPS-130 1U [40], a mono-propellant micro propulsion system able to provide 385 m/s of \(\Delta V\) to a CubeSat of 3.9 kg. ECAPS HPGP 0.1 N thruster may offer either higher \(\Delta V\) or the same \(\Delta V\) to a heavier CubeSat; however, it would require an additional design of the rest of propulsion system (tank, feeding systems, etc. ), which would make the architecture not off-the-shelf. The selection of the Aerojet Rocketdyne MPS-130 defines then the mass and volume requirements for the architecture and the other subsystems, which need to fit the remaining 2U. Moreover, as this engine is able to deliver the required \(\Delta V\) to a CubeSat of maximum 3.9 kg, and it is characterized by a BOL mass of 1.7 kg, the remaining components of the architecture should not exceed 2.2 kg of mass. This, again, shows that mass and volume for each component are driving parameters for the selection. Figure 3: Required \(\Delta V\) at the SSGTO perigee in function of the hyperbolic excess velocity (solid line), and 600 m/s threshold escape velocity (dashed line).
###### Abstract
Digital Twins can effectively be used in buildings to enable the more efficient and intelligent management of energy, security, and comfort [24; 25; 26] or to improve the healthcare of older people through monitoring and prediction [27; 28]. Monitoring and prediction can be applied to the creation of routines in smart homes so that the Digital Twin can notify users when a conflict between routines occurs. For example, the user might have defined a routine to switch on the light of the bedroom lampshade every day at 10:30 p.m., and then another routine to switch off all the lights at 10:30 p.m. without realizing that the second routine will also turn off the lampshade. Obviously, more complex situations may occur when several routines are defined to work synergistically in the same smart home. Some initial work in the Human-Computer Interaction field has been developed to study how to specify automation rules and permit their debugging, including conflict detection [29; 30]. However, in the literature on Digital Twins, home inhabitants usually play a passive role: they contribute to generating data but do not intervene in actively modifying smart home behaviors by creating routines. Moreover, there is a lack of scientific work addressing the issues related to the interaction between users and Digital Twins [31]. This paper explores the design and implementation of a Digital Twin for a green smart home to forecast and optimize the energy consumption of various appliances. Particularly, the Digital Twin can evaluate in advance the routines that home inhabitants would like to create and suggest possible modifications that enhance energy consumption or avoid energy supply interruption. The novel contribution of this research is applying a sociotechnical approach [5] to designing Digital Twins for smart homes, focusing both on humans through End-User Development techniques and on technical issues related to appliance automationwith Machine Learning algorithms. The final goal is to better support users in controlling their smart home and preventing undesired situations. More precisely, this paper's contributions are the following:  
* creation of the Digital Twin of a hypothetical smart home based on energy consumption open data;
* a method for mining appliances' operation modes with unsupervised machine learning;
* implementation of a Digital Twin web service for energy consumption prediction;
* design of the Digital Twin interface for energy consumption simulation of routines created through End-User Development. The paper is organized as follows: Section 2 presents the hypothetical smart home and the Machine Learning approach adopted in the Digital Twin to recognize the operation modes of appliances; Section 3 describes the developed features to evaluate user-created routines and the Digital Twin operation through a proof-of-concept web-based application; Section 4 presents the most important findings, design implications, and open issues of the research.
## 5 Dataset Description  
The CDRs dataset used in this paper is obtained from an Egyptian mobile operator. This dataset provides CDRs for one million originating calls occurring in 24 h. Each CDR is described by 256 fields. Some examples of the dataset CDRs fields are shown in Table 1.  
1. **Device model:** The type of the user mobile handset.
2. **Audio codec:** Indicates the type of codec used for voice calls. Codec is the abbreviation for coder-decoder or compression-decompression. The two most used codec Narrow-Band Adaptive Multi-Rate (NB-AMR) and Wide-Band AMR (WB-AMR) [46]. The numbers 101 and 102 are defined for WB-AMR and AMR, respectively [46].
3. **Video codec:** Indicates the type of codec used for video calls. In the shown example there is no video calls, so all fields of the video codec equal zero. The types of video codec and the way they are expressed in the CDRs are shown in [46].
4. **Alerting time:** The time for the user handset in milliseconds between receiving a 180 ringing SIP response and a 200 ok SIP response.  
Figure 5: SIP requests categorization.  
Figure 6: SIP responses categorization.  
* **Answer time:** The time of the call in milliseconds.
* 6. **SRVCC flag:** SRVCC is the abbreviation for Single Radio Voice Call Continuity. It is a binary value. It equals one if the user makes a Inter RAT handover from packet data to circuit switched data voice calls. Meanwhile, it equals zero otherwise.
# A Framework for the Detection of Search and Rescue Patterns Using Shapelet Classification
## 4 Experimental Evaluation & Results
### Binary Classification on Synthetic Data  
After setting up the parameters for the GENDIS algorithm and the synthetic data generator in the first experiment (i.e., Steps vs. Random classification), we perform additional experiments for better  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Accuracy Level** & **Number of Experiments** & **Percentage of Experiments** \\ \hline Bad & 132 & 16.5 \\ Acceptable & 276 & 34.5 \\ Excellent & 379 & 47.375 \\ Perfect & 13 & 1.625 \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of the classifier accuracy performance on the “Steps vs. Random” scenario.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Time-Series Created** & **GENDIS Iterations** & **Time Passed** & **Average Accuracy** \\ \hline
1 & 100 & 5 & 1.20 & 83.33 \(\pm\) 2.01 \\
2 & 100 & 10 & 2 & 85.53 \(\pm\) 1.88 \\
3 & 100 & 15 & 2.40 & 84.73 \(\pm\) 2.06 \\
4 & 100 & 20 & 3.10 & 86.77 \(\pm\) 1.61 \\
5 & 150 & 5 & 1.5 & 85.10 \(\pm\) 1.76 \\
6 & 150 & 10 & 2.25 & 88.77 \(\pm\) 1.3 \\
7 & 150 & 15 & 3.5 & 89.48 \(\pm\) 1.35 \\
8 & 150 & 20 & 6.5 & 90.40 \(\pm\) 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results for the “Steps vs. Random” scenarios. The average accuracy scores are reported in the 95% confidence interval.  
understanding the nature of every pattern. We evaluated (100 repetitions each) two types of scenarios: (i) binary classification scenarios (scenarios 9-12), where we have to distinguish between a certain SAR maneuver and a Random movement and (ii) binary classification scenarios (scenarios 13-16), where the aim is to distinguish between the Step maneuver and other types of SAR maneuvers. The results in all experiments are very promising as depicted in Table 3, with more than 87% of the runs to have an accuracy score above 90%.  
The detailed results per experiment, as depicted in Table 4, show an impressive classification performance (above 97%) in distinguishing the spiral and expanding square patterns from the random movement and an average accuracy above 90% in distinguishing the Creeping line and Sector patterns from random movement. The results are even better in the binary classification tasks that refer to the Steps pattern and distinguish it from other SAR patterns, which are more complicated and thus easier to detect. This performance can be explained by the major differences that the various SAR patterns have in the bearing and bearing change which result to totally different shapelets being extracted. However, the results in the real trajectories dataset show that there is still place for improvement as explained in the following paragraph.
## 3 Test Results and Analysis  
### Changes in Physical Properties  
#### 3.1.1 Surface Morphologic Change  
Figure 3 shows the surface morphology after natural cooling and water cooling. From Figure 3, we found that under the effect of two cooling methods, the morphology of the sample changes little at 200 \({}^{\circ}\)C, 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C. The sample particles are closely connected and the morphology is normal. With the increase in heating temperature, fine cracks appear on the surface of the 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C samples. This is mainly due to the fact that, with the increase in temperature, the mineral particles in the sample deteriorated to varying degrees, as macro cracks were showing. Furthermore, it can be seen from Figure 3 that more micro cracks appear on the surface of the granite sample, which was cooled by water under the high temperature conditions and the surface of the rock is even rougher. It is considered that the granite, after the high temperature treatment, encounters water when the surface cools rapidly, while the temperature inside the sample does not decrease. So, there is a temperature difference producing both inside and outside stresses, which leads to the temperature stress action and it further causes more microcracks on the surface of the samples.  
Figure 3: Surface morphology of granite with different cooling methods. (**a**) is the surface morphology of granite under natural cooling; (**b**) is the surface morphology of granite under cooling in water.  
Figure 2: WAW-1000 universal testing machine.
## 5 Experiments and Results  
### Repeatability Test  
For precise operation of the robot, repeatability experiments are required to verify the same positioning of the same commands in the robot's workspace. In order to measure the repeatability of the robot, an experimental setup was constructed with reference to ISO 9283. We set the four vertices of the diagonal plane across the test cube with dimensions of 200 mm \(\times\) 100 mm \(\times\) 100 mm as the target position, as shown in Figure 8.  
In order to avoid the effect of vibration generated when the target point is reached, it started to move to the next target position after a certain time. At each of the four points, 15 measurements were obtained. In the experiment, the position of the end-effector of the robot was measured using a laser tracking system (Radian, API Metrology, USA) with extremely high accuracy. The laser tracking device could accurately estimate the position of API's spherically mounted retroreflectors (SMRs). In the experimental environment, as shown in Figure 8, the SMR was attached to the end of the robot, and the tracking equipment was constructed about 2 m away from the robot. The system resolution was set to 0.1 um. We used this instrument only for this measurement test because the robot had very small errors. The error between the reference position and the robot's real position was calculated using Equation (4). The maximum and minimum errors are shown in Table 2. The repeatability of the robot was calculated as 0.0504 mm. This result is similar to the level of repeatability of industrial robots [16; 17]. The OMS introduced in the previous section is to be used as a sensor for tracking objects in other brain stimulation experiments. The OMS has a resolution of a few sub-millimeters which is low resolution compared to the laser tracking system, but that resolution is sufficient for brain stimulation. In this sense, the OMS is an optimal and cost-effective tracking sensor in real clinical situations.  
Figure 8: Setup of repeatability test of robot and test geometry.
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 3 Related Work
### Text to NoSQL  
In contrast to traditional SQL-based systems, the variety of database schemas and less strict query structures in NoSQL query languages pose a particular challenge for the development of NLI, as they require flexible and dynamic processing and interpretation of the natural language. Therefore, various approaches have been developed in the literature that use classical and modern techniques to deal with this challenge. [PERSON] et al. [41] developed a query-response model that can handle various queries, like assertive, interrogative, imperative, compound, and complex forms. Their system is essentially basedon the extraction of entities and attributes from natural language using NLP techniques, from which the query can then be formed after semantic analysis. [PERSON] et al. [42] and [PERSON] et al. [43] both propose deep learning-based approaches for generating queries. In the former, after the identification of the question type by a question detection module, the natural question is converted into the query by an encoder-decoder architecture. For each different question type, such a deep learning module was created. In the latter, the question is first pre-processed using NLP techniques. The information obtained from this is used in the next step to extract collections and attributes using the Levenshtein distance algorithm. A BERT-based module is then used to extract the operation from the natural question. Finally, all extracted information is used to map it with a syntax tree, which is then used to generate the query. All these approaches have been developed and tested to generate queries for the database MongoDB. In contrast, [PERSON] et al. [44] developed an NLI for querying elasticsearch databases. In their system, the user's question is first converted into a template question using a pre-trained transformer-based sequence-to-sequence model into a template question. This is then used by an encoder to generate the condition fields and values, which can then be inserted for the elasticsearch query templates.
###### Abstract  
We report comprehensive elemental composition studies on the average urban airborne particulate matters (PMs) collected in the Greater Seoul area, Korea, in 2019 to identify regional and chronological characteristics of the sample as a candidate for certified reference material (CRM), using \(k_{0}\)-based single comparator instrumental neutron activation analysis (\(k_{0}\)-INAA). The method was successfully validated by comparing the analysis result of a similar matrix CRM (SRM 1648a urban particulate matter) of National Institute of Standards and Technology, USA, with corresponding certified values. The same methodology was applied to determine various elements in candidate environmental materials for future CRM development, including the urban PMs and incineration ashes, to investigate the possibility of using \(k_{0}\)-INAA for certification of relevant reference materials. In total, 46 elements in the urban PM sample were analyzed and their concentration levels were compared with the urban PMs collected in the 1970s in St. Louis, USA. Urban PMs of Korea Research Institute of Standards and Science in 2019 contain significantly lower levels of hazardous elements, such as As, Cd, Cr, Hg, and Pb, as compared to those of the 1970s, which can be attributed to the reduced air pollution by environmental regulation and technological innovation. The potential major source of urban airborne PMs was also discussed.  
urban airborne particulates; \(k_{0}\)-INAA validation; certified reference material; hazardous elements; source identification +
Footnote †: journal: Applied Sciences
# Theoretical Modelling of Ion Exchange Processes in Glass: Advances and Challenges
## 3 The Mixed Ion Effect  
Constant diffusion coefficients were assumed in Section 2. However, they depend on both the temperature \(T\) and the molar fraction \(c_{A}\). In monoalkaline glasses, the temperature dependence follows the Arrhenius law:  
\[D=D_{0}\exp\biggl{(}-\frac{Q}{kT}\biggr{)}. \tag{25}\]  
This is due to each cation needing to surmount a potential barrier of height equal to \(Q\) to move to another potential well. The diffusion coefficient is proportional to the number of cations that overcome this energy in a given instant. This number follows a Maxwell-Boltzmann statistic, which explains the above exponential law. According to the Stuart-Anderson model [49], the potential barrier has both an electrostatic component, corresponding to the energy necessary to separate the cation from its anion, and a mechanical component that describes the glass network distortion necessary for the cation to break through another potential well. Therefore, when two species of cations are present in the glass, it is natural that each one has its own diffusion coefficient. However, surprisingly, the \(D_{0}\) and \(Q\) values of each species (\(D_{0A},Q_{A}\) and \(D_{0B},Q_{B}\)) largely depend on the cation mole fraction, in such a way that each diffusion coefficient is reduced by up to several orders of magnitude as its respective mole fraction is approaching zero [27; 50; 51]. This reduction is mainly due to an increase of the activation energy of minority cations; furthermore, it is stronger than the difference between the diffusion coefficients of both species in their respective monoalkaline glasses (\(D_{AA}\!\equiv\!D_{A}|_{c_{A}=1}\) and \(D_{BB}\!\equiv\!D_{B}|_{c_{A}=0}\)). Consequently, \(D_{A}\) and \(D_{B}\) are equal for some intermediate mole fractions. We will show that this leads to a maximum value of the interdiffusion coefficient and a minimum value of the direct current (DC) conductivity for this intermediate mole fraction or its neighbourhood. Therefore, the DC conductivity shows an excess of activation energy with respect to the monoalkali glasses (this excess disappears with frequency in AC conductivity). This phenomenon, among others, is included in the so-called double-alkali effect, mixed alkali effect or, later, mixed mobile ion effect or mixed ion effect (MIE), since silver, thallium or copper cations behave similarly to alkali ones in glass [37; 52; 53; 54].