esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Laser Measurement Technology
Both LISA and GRACE Follow-on [11] use dual-frequency laser interferometers to complete inter-satellite ranging; the performance comparisons are shown in Table 8. The dual-frequency laser interferometer detects the change in the optical path difference between the measurement arm and the reference arm, which is, essentially, a differential measurement. The main components of the laser detection system of LISA include a laser interferometer measurement system, a laser source, a photodetector, and an optical telescope. The formation-satellite members are equipped with two independent measurement systems. The laser beam generated by the laser source is emitted from the optical telescope, received by the optical telescope on the formation satellite, and further input into the photodetector  
Figure 17: The spaceborne LiDAR system. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Project** & **Mode** & **Frequency Band** & **Accuracy** & **Dynamic Range** & **Effective Range** \\ \hline LISA & Outlier type & 0.1–100 mHz & 10 pm & \(\pm\)12/4 MHz & 5 million km \\ GRACE Follow-on & Outlier type & 0.2–100 mHz & 80 nm & \(\pm\)10 MHz & 220–270 km \\ \hline \hline \end{tabular}
\end{table} 
Table 8: LISA and GRACE Follow-on laser-ranging system performance. and the laser interference ranging system to obtain the distance information. The LISA laser interferometric ranging system can establish the distance between two adjacent spacecraft and the pointing angle between the received laser beam and the emitted laser beam. The ranging principle is shown in Figure 18.  
The laser-ranging interferometry system of GRACE Follow-on [1] is mainly composed of optical components and phase-reading components. The optical assembly consists of a laser source, a photodetector, an optical telescope, and a fast-control mirror. The master spacecraft sends the laser beam from the optical telescope to the slave spacecraft. The laser beam is received from the slave spacecraft optical telescope, and the laser beam is forwarded in phase with the difference frequency through a heterodyne optical phase-locked loop. The main spacecraft interferes with the received retransmitted laser beam with the local reference laser beam, to form a beat frequency signal. It is further converted into an electrical signal by a photodetector. The core of the phase-reading component of the master spacecraft is a phase meter that can read the phase of the beat frequency signal and further obtain the relative pose information. The slave spacecraft is equipped with a temperature sensor and a voltage sensor for feedback control of the phase difference frequency of the laser beam. Figure 19 shows the schematic diagram of GRACE Follow-on, in which the dotted line represents the laser beam of the master star and the solid line is the laser beam forwarded from the satellite.
## 2 Phase Synchronization Scheme  
In this section, an advanced non-interrupted phase synchronization scheme designed for the LT-1 mission is introduced. The phase model of the synchronization scheme is also described in detail along with the system design of the LT-1 synchronization module.
# Influences of Brass Surface Morphology on Leidenfrost Effect during Liquid Nitrogen Cooling
###### Abstract  
Cooling in liquid nitrogen is a typical service condition of high-temperature superconducting wire, and the variation of boiling stages on the wire protective layers such as the brass layers could be crucial for the quench behavior of superconducting devices. In this study, the influence of brass surface morphology (parameters of surface roughness and fractal dimension) on the Leidenfrost effect (including the wall superheat at critical heat flux and the wall superheat at leidenfrost point, which are respectively characterized by the temperatures of \(\Delta T_{\text{CHF}}\) and \(\Delta T_{\text{LP}}\)) was studied. The surfaces of brass samples were polished by sandpaper to obtain different morphologies, which were characterized by using white light interferometer images, and the boiling curves were recorded and analyzed by Matlab with lumped parameter method. The experimental results demonstrated that the surface morphology of brass samples could influence the \(\Delta T_{\text{LP}}\) significantly, but had no clear relationship with the \(\Delta T_{\text{CHF}}\). Moreover, the multi-scaled analysis was carried out to explore the influencing mechanism of surface microstructure, the relationship between \(\Delta T_{\text{LP}}\) and scale was more clear when the scale was small, and the fractal dimension was calculated and discussed together with surface roughness. The findings of this study could be instructive for surface treatment of superconducting wires to suppress quench propagation.  
Leidenfrost effect; liquid nitrogen; surface morphology; roughness; fractal dimension Article
###### Contents
THz-time-domain spectroscopy (THz-TDS) has been used for characterizing or analyzing petroleum products and contaminants, including: several grades of lubricating oils [20; 21], several lubricating grease types [22], solidifying point for diesel fuel [23], diesel and gasoline fuel [24], different grades of gasoline engine oil [25], three different levels of water contaminations in diesel engine oil [26], four different levels of fuel contaminations in gasoline engine oil [27], four different periods of oxidation for gasoline engine oil [28], six different periods of oxidation for diesel engine oil [29]. Previous studies have not assessed the ability of the THz-TDS to detect engine oil contamination with glycol. This research aimed to determine the ability of THz-TDS to identify four different levels of glycol contamination in parts per million (ppm) units with (0 ppm, 150 ppm, 300 ppm and 500 ppm) in engine oil (5W-30) intended for diesel and gasoline engines. While THz-TDS is not envisioned as an in-situ system, it was used to evaluate the appropriate/optimum frequency ranges for glycol concentration determination. A final in-situ system could be a miniaturized, cost-effective electronic system, operating in a narrow frequency range.
## 3 Verification of Proposed Model  
### Centrifuge Tests and Input Properties  
Verification of the proposed numerical model was carried out using the results from the centrifuge test conducted by [PERSON] (1998) [PERSON] (1998) [16]. The centrifuge test was conducted under centrifugal acceleration of 30 g, which corresponds to the length scaling factor of 30 using the National Geotechnical Centrifuge tester from UC DAVIS, and the model soil was constructed with two layers using uniform Nevada  
Figure 6: Typical mesh and boundary extent of the numerical model.  
sand. The dense bottom layer (D\({}_{\rm r}\) = 80%) was 11.4 m thick and the loose top layer (D\({}_{\rm r}\) = 35% or 55%) was 9.1 m thick. All the soil layers were completely saturated (Figure 7).  
The model pile was fabricated with aluminum. It measured 0.67 m in diameter and was 0.072 m thick. The pile head was located 3.8 m from the ground surface, and a superstructure load of 480 kN was installed. The penetration depth of the pile was 16.8 m, and the pile end simulated the friction pile located approximately 3.7 m from the bottom of the soil container. The soil container was filled with a fluid of a viscosity approximately ten times greater than water, which was fabricated by mixing a viscous fluid (Hydroxyl-propylmethyl-cellulose) with water to generate saturated soil conditions and to satisfy similitude law for the permeability ([PERSON] et al., 1998 [49]). For the input seismic wave, the Kobe (1995) seismic wave (Port Island 83-m depth, NS direction) was used, for which the maximum acceleration value was scaled to 0.22 g. Figure 8 shows the input acceleration-time history used in experiments and numerical analysis.  
For the numerical simulation of the experimental model, the modeling method described in Section 2 was applied. The soil properties applied in the numerical model are summarized in Table 1 ([PERSON], 1993 [50]). Variation of internal friction angle and Poisson's ratio according to the relative density was determined by [PERSON] et al. (1979) [39] and [PERSON] & [PERSON] (2010) [51],  
Figure 8: Base input motion (Acceleration record of Kobe (1995) earthquake scaled to 0.22 g).  
Figure 7: Layout of the experimental model ([PERSON], 1998 [16]).  
respectively. The pile was modeled as a cylinder and had an equivalent flexible stiffness with the model pile of the centrifuge test. The pile properties are summarized in Table 2.
## 3 Materials and Methods
### Coseismic InSAR Data Preprocessing and Labeling  
The InSAR coseismic interferograms obtained in the previous steps are typically large-scale image files that require preprocessing before being used for deep learning model training. To improve model generalization and stability, a systematic preprocessing pipeline is applied, as illustrated in Figure 3. The preprocessing consists of the following steps:  
1. Coherence Filtering: Interferograms with more than 20% missing or invalid pixels are discarded to eliminate low-quality data affected by severe decorrelation or data gaps, thereby retaining only high-quality images with reliable deformation information.
2. Cropping: To improve training efficiency and reduce memory consumption, large-size interferograms are cropped into smaller patches of uniform size and resolution (e.g., 1024 \(\times\) 1024 or 512 \(\times\) 512 pixels). During this process, priority is given to preserving regions containing seismic deformation while minimizing the inclusion of irrelevant areas to maintain the integrity of valid deformation signals.
3. Resampling: Cropped image patches are resampled to a fixed size of 224 \(\times\) 224 pixels using cubic convolution interpolation to match the input requirements of deep learning models. This standardization improves dataset consistency and facilitates efficient model training.
4. Normalization: Pixel values are normalized to the standard range of [0, 255] to reducing grayscale variability across samples and ensure a uniform data distribution. This process improves model stability, reduces gradient fluctuations during training, accelerates convergence, and enhances overall model performance.  
To classify each image patch as either \"Deformation\" or \"Noise\", we employed a semi-automated labeling tool developed in Python, utilizing libraries such as Matplotlib, OpenCV, and NumPy. This tool incorporates a graphical user interface (GUI) that allows annotators to visually inspect interferograms and identify the presence or absence of discernible coseismic deformation signals. Although the classification process remains primarily manual, the interactive interface and streamlined workflow significantly reduce the time and effort required to construct large-scale datasets compared to conventional labeling approaches.  
Figure 3: Flowchart of interferogram preprocessing and data annotation.
## 5 Nanoparticles with Microorganisms
### Algal Biomass and Nanoparticles  
In order to fulfill the future energy requirements of the world, algal biomass is potentially the next energy resource for bioenergy production. It also has a potential to serve as a resource of high value for basic chemicals and extracts. Being a member of aquatic systems, they serve as food sources and hold a key role in photosynthesis. In fresh water, the impact of nanoparticles on microalgae can be found in the form of silver ions, silver chloride (AgCl) and silver sulfide (Ag\({}_{2}\)S). Silver ion is the most toxic form of silver NPs [93]. Due to an increase in concentration in an aquatic environment, these nanoparticles can damage and affect the biota [88; 94]. In drinking water, ground water and surface water, the concentration of silver nanoparticles has already been found to be above 5 g/L [95]. Among many of the possible reasons for toxicity of nanoparticles, three are very obvious: (i) a high dissolution rate due to a high surface area to volume ratio [94], (ii) aggregation behavior due to bioavailability, and (iii) coating with the stabilized organic material [96]. [PERSON] [97] carried out an investigation on the toxicity of Ag NPs stabilized with PVA (polyvinyl alcohol) for aquatic microalgae, such as _Daphne similis_, _Pseudokirchureiella_ subcapitata algae and _Artemia Salina_. With a size range of 218 nm, Ag NPs in solution were well dispersed under dynamic light scattering measurements. Lower toxicity was shown by Ag NP against _P. Subcapitata_ and _A. Salina_ while high toxicity exhibited to _D. Similis_. A major component of global aquatic ecosystems is formed by _Chara vulgaris_ and _Pithophora edogonia_, which are predominantly members of photosynthetic eukaryotic algae. According to [PERSON] et al., prominent adverse effects of nanosilver on the morphology and growth of filamentous algae were illustrated on a concentration basis [98]. When _Algal Thalli_ was exposed to higher concentrations of Ag NPs there was continued algal chlorophyll content depletion and disturbance in mitoses chromosomal instability. Dramatic alterations in the algal cell wall, characterized by pithophoral degradation and ruptured cell walls, was revealed in SEM micrographs when treated with nanoparticles.
# Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot
## 2 Related Work  
### Touch in Human-Robot Interaction  
The main application of human-robot touch interaction is mental therapy for users. Several studies have investigated the merits of touch interaction with robots [7; 8; 9; 10; 34]. One famous therapy robot is a touchable seal robot (Paro) whose mental support effectiveness is well known worldwide [7]. Examples of commercially available, touchable pet-type robots include LOVOT ([[https://lovot.life/](https://lovot.life/)]([https://lovot.life/](https://lovot.life/)), accessed on 1 December 2020) and Qoobo ([[https://oobo.info/index/](https://oobo.info/index/)]([https://oobo.info/index/](https://oobo.info/index/)), accessed on 1 December 2020).  
To achieve natural touch interaction between people and robots, robotics researchers have focused on communication cues, which have been broadly investigated in conversational settings [35; 36; 37]. However, such knowledge cannot be applied to touch interaction settings due to the differences in interaction settings or difficulties due to shorter distances. Therefore, robotics research on touch interaction is investigating appropriate communication cues under such settings [14; 38].  
Robot appearances also influence touch interaction. For example, in human-human interaction, an acceptable body part for touching differs based on the relationship [18; 39]. Which body part is touched also communicates a different meaning in the context of conveying emotions [40]. Researchers also investigated how a robot's body parts are essential to convey emotions by people/robots [41; 42]. Other studies have focused on human touch interaction cues such as handshaking, describing its phases and analyzing both before-touch and after-touch actions [43], and defining methods for evaluating the human likeness of a robot handshake using a Turing-like test [44].  
Figure 1: Android behavior to participant’s approach: (**a**) initial position for starting touch interaction; (**b**) android looking at participant at a certain distance before being touched.  
These studies described the importance of touch behavior in human-robot interaction from various perspectives. However, even though they provided rich knowledge about touch interaction, they focused on after-touch interaction situations, downplaying before-touch situations. Unlike these studies, our study investigates pre-touch situations to understand people's perception of being touched. Therefore, we observed pre-touch proxemics data, modeled them, and implemented our model.
## 2 Array Signal Model
\begin{array}{cccc}\gamma_{11}&\gamma_{12}&\cdots&\gamma_{ 1D}\\ \gamma_{21}&\gamma_{22}&\vdots&\gamma_{2D}\\ \vdots&\vdots&\ddots&\vdots\\ \gamma_{M1}&\gamma_{M2}&\ldots&\gamma_{MD}\end{array} \right] \tag{9}\]  
In order to compute the phase difference, the time difference of arrival of the \(k\)th signal at each reference element and each sample element needs to be calculated. The wavefront time delay is calculated using the difference in distance (\(q_{ik}\)) that is given by:  
\[q_{ik}=r\cos\gamma_{ik}=\ r\cos\Bigl{(}\cos^{-1}(\sin\theta_{k}\cos(\varphi_{k }-\varphi_{i}))\Bigr{)}=r\sin\theta_{k}\cos(\varphi_{k}-\varphi_{i}) \tag{10}\]  
where \(r\) can be computed as follows:  
\[r=\frac{d}{2\sin\bigl{(}\frac{2\pi}{M}\bigr{)}} \tag{11}\]  
The phase difference (\(\psi_{ik}\)) can be expressed in the following formula:  
\[\psi_{ik}=\beta.q_{ik}=\frac{2\pi}{\lambda}r\sin\theta_{k}\cos(\varphi_{k}- \varphi_{i}) \tag{12}\]  
Then, the circular array steering vector can be defined as follows:  
\[\mathbf{a}(\theta_{k},\ \ \varphi_{k})=\left[ \begin{array}{c}e^{-i\varphi_{1k}} \\ e^{-i\varphi_{2k}}\\ \vdots\\ e^{-i\varphi_{Mk}}\end{array} \right] \tag{13}\]  
As arriving signals are time-varying, the calculations are based on time samples of the arriving signal. Clearly, if the signal sources are moving from one location to another, the corresponding received angles and the matrix of steering vectors change with time. The array covariance matrix (\(\mathbf{R}_{\mathbf{xx}}\)) can be expressed in the following form:  
\[\mathbf{R}_{\mathbf{xx}}\ =E\Bigl{[}\mathbf{X}(t)\ \mathbf{X}^{H}(t)\ \Bigr{]} =E\Bigl{[}\mathbf{A}\mathbf{S}(t)\mathbf{S}^{H}(t)\mathbf{A}^{H}\Bigr{]}+E\Bigl{[} \mathbf{N}(t)\ \mathbf{N}^{H}(t)\Bigr{]}\] \[\mathbf{R}_{\mathbf{xx}} =\mathbf{A}\mathbf{R}_{\mathbf{ss}}\ \mathbf{A}^{H}+\sigma_{n}{}^{2}\mathbf{I}_{\mathbf{M}} \tag{14}\]  
where \(\mathbf{R}_{\mathbf{ss}}\) is the (\(D\times D\)) source signal correlation matrix \(\mathbf{R}_{\mathbf{ss}}=E[\mathbf{S}(t)\mathbf{S}(t)]\), \(\sigma_{n}{}^{2}\) is the noise variance and \(\mathbf{I}_{\mathbf{M}}\) is the \(M\times M\) identity matrix. In this case, the received signals are taken as being uncorrelated, so that \(\mathbf{R}_{\mathbf{ss}}\) is a diagonal matrix. However, if the received signals are correlated, the signal correlation matrix will be non-diagonal, as appears in realistic environments due to multipath propagation or unfriendly jamming signals. Complete knowledge of \(\mathbf{R}_{\mathbf{xx}}\) cannot be assumed; instead, we may use as necessary the sample-average estimated array input autocovariance matrix given by:  
\[\hat{\mathbf{R}}_{\mathbf{xx}}\approx\frac{1}{N}\sum_{k=1}^{N}\mathbf{X}(t)\mathbf{X}^{H}(t) \tag{15}\]After the CM is obtained, AOA estimation is performed using a suitable AOA algorithm.
## 1 Introduction
### Closed-Loop Insulin Pumps  
Manual injection with an insulin pen or pump causes patients to appear twice in a discrete control loop: as the glucose metabolic system which must be controlled and as the controller itself (cognitive system). Since it is difficult to take all external disturbances and internal systems into consideration, the discrete control method is generally followed by hypoglycemic or hyperglycemic events. Thus, to avoid patients having to determine each insulin dose manually, and to limit the variation in blood glucose concentration, an artificial pancreas needs to be developed as an essential scientific research aim. The required insulin dose is calculated using a control algorithm based on continuous glucose measurements obtained via a sensor without human input, and the precise insulin dose is automatically administered via a pump that continuously delivers insulin [5]. Improvement in glycemic control, in terms of glycated HbA1c (hemoglobin A1c) concentration and a reduction in hypoglycemia, and automatic changes in the basal insulin infusion rate at the time of exercise without any extra high-carbohydrate intake are essential advantages of APSs [6].  
A T1 DM patient needs to calculate his/her meal intake and required exercise interval in order to inject insulin, and glucose level also needs to be measured several times during a loop [4]. In this manual loop, the whole responsibility is on the patient, and if the patient cannot balance the glucose level correctly, it may cause many different diseases. Consequently, using a closed-loop system controlled by an artificial pancreas is quite beneficial for T1 DM patients [6]. Artificial pancreases should be convenient to use, accessible, long-lasting, secure, safe, and affordable. Furthermore, they should also consume low energy. In this study, being low-cost and disposable, and consuming low energy are the primary focused features of an artificial pancreas.  
Most of the APSs in the market includes a syringe pump such as Medtronic's MiniMed 670G [7], Beta Bionics' iLet [8], and Tandem's tsim X2 [9]. However, the clinical use of syringe pumps is associated with several risk factors, including overdose or underdose due to lack of control of the infusion speed, bleeding from backflow, and introduction of air. Overdose is the most problematic and significant one among all. It can lead to severe complications such as hypersecretion, respiratory depression, and hypoxia [10]. Additionally, the use of syringe pumps may not be ideal due to their large size and high-power demands for APS applications, where portability and mobility are important requirements. The novel artificial pancreas, which we propose in this study, functions with a piezoelectric actuated pump and can eliminate the accuracy, portability, and power efficiency issues of the old methods and serve as a solution.
# Study of the Relationship between Mode I Fracture Toughness and Rock Brittleness Indices
###### Abstract  
Mode I fracture toughness (\(\mathrm{K_{IC}}\)) and rock brittleness are important properties that influence many rock engineering applications. Due to the difficulties in determining \(\mathrm{K_{IC}}\) experimentally, previous studies have investigated the relationship between \(\mathrm{K_{IC}}\) and rock brittleness indices. However, only rock brittleness indices (based on strength parameters) and \(\mathrm{K_{IC}}\) obtained from Chevron Bend and Short Rod test methods were considered. In this paper, regression analysis was carried out to investigate the relationship between \(\mathrm{K_{IC}}\) and rock brittleness using literature data collected from different rock types and core \(\mathrm{K_{IC}}\) test methods under level I and static test conditions. Rock brittleness was assessed using ten indices based on strength and pre-peak elastic parameters. The results showed that elastic-based indices were not good predictors of \(\mathrm{K_{IC}}\), while strength-based indices correlated well with \(\mathrm{K_{IC}}\). A comparison with previous studies revealed that the correlations between \(\mathrm{K_{IC}}\) and strength-based indices were significantly sensitive to the rock type, i.e., soft or hard, and the \(\mathrm{K_{IC}}\) test method. However, a brittleness index, based on both strength and pre-peak elastic parameters, was found to be the best index to predict \(\mathrm{K_{IC}}\) because of its lower sensitivity to the test method and rock type.  
mode I fracture toughness; rock brittleness; strength-based brittleness indices; elastic-based brittleness indices; prediction; LEFM; FPZ