esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Mission-Specific Growth Conditions and Procedures
#### 2.3.3 Micro-14/FEP, Space X CRS-17  
The FEP bag hardware was utilized for cultivating yeast in this payload and involved serial passage-style growth. The parent culture (Large FEP) was serially propagated over five days and from each of these populations a set of antifungal sensitivity cultures were initiated (Small FEP, see Figure 1B and details in Section 2.4.3. Large FEP bags for the parent cultures were filled with 1\(\times\) YPD (11.0 mL for the initial culture, Bag A, and 11.8 mL for the four downstream cultures, Bags B-E) and gas bubbles were removed. The yeast inoculum of 1 mL (4 \(\times\) 10\({}^{4}\) cells/mL in water) was loaded into a 3 cc syringe and sealed for transport. The needeless syringe was used to inoculate the initial culture (11 mL FEP bag, Bag A) on orbit. Following 24 h of growth at 30 \({}^{\circ}\)C, 0.5 mL of culture was removed from Bag A and diluted 20-fold in water (D\({}_{1}\) FEP bags). An aliquot of this diluted sample (0.25 mL) was used to inoculate the next Large FEP bag (11.8 mL YPD, Bag B). This process was repeated such that the yeast was continually propagated through five cycles of 24 h growth (Bags C, D and E). The Large FEP bag samples were frozen (\(<\)\(-\)80 \({}^{\circ}\)C) either in the presence (Bag A and E) or absence (Bags B-D) of glycerol as a cryopreservation agent. As a complement to this series of growth, Small FEP bags containing 2.8 mL YPD and antifungal agents as indicated (AmB, 0.12-5 mg/L; and CAS, 0.01-0.1 mg/L) were inoculated with a dilution of the corresponding Large FEP culture. Specifically, the 20-fold dilution prepared from the large FEP bag as described above (D\({}_{1}\)) was further diluted 10-fold (D\({}_{2}\) FEP bag) and aliquots (0.2 mL) were used to inoculate each Small FEP bag (A\({}_{1-12}\) from FEP Bag A; B\({}_{1-12}\) from FEP Bag B, etc.). These cultures were grown at 30 \({}^{\circ}\)C for 24 h and then frozen at \(<\)\(-\)80 \({}^{\circ}\)C for the duration of the mission. At the end of the mission, frozen samples were maintained at \(<\)\(-\)25 \({}^{\circ}\)C from undocking to the California-based handover location using phase change bags (Cold Stowage, NASA, Houston, TX, USA) and then to the home laboratory using dry ice.
# Influence of Dry Friction on the Dynamics of Cantilevered Pipes Conveying Fluid
## 2 Theoretical Model
\begin{array}{l}(m+M)\ddot{y}+2M\dot{U}\dot{y}^{\prime}\Big{(}1+{y^{\prime}}^{ 2}\Big{)}+E^{*}\dot{I}\dot{y}^{\prime\prime\prime}+(m+M)g\dot{y}^{\prime}(1+ \frac{1}{2}{y^{\prime}}^{2})\\ +{y^{\prime\prime}}\left[M\dot{L}^{2}\left(1+{y^{\prime}}^{2}\right)\ -(m+M)g(L-s)(1+\frac{3}{2}{y^{\prime}}^{2}) \right]\\ +{y^{\prime}}\int_{0}^{s}\big{(}m+M\big{)}\left(\dot{y}^{\prime}{}^{2}+{y^{ \prime}}\ddot{y}^{\prime}\right)\mathrm{d}s+EI\big{[}{y^{\prime\prime}}^{n} \left(1+{y^{\prime}}^{2}\right)+4{y^{\prime}}{y^{\prime\prime}}^{n}+{y^{ \prime\prime}}^{3}\Big{]}\\ -{y^{\prime\prime}}\left[\int_{s}^{L}\int_{0}^{s}\big{(}m+M\big{)}\left(\dot{ y}^{\prime}{}^{2}+{y^{\prime}}\ddot{y}^{\prime}\right)\mathrm{d}s\mathrm{d}s+ \int_{s}^{L}\!\left(2M\dot{U}{y^{\prime}}\dot{y}^{\prime}+M\dot{U}^{2}{y^{ \prime}}{y^{\prime\prime}}\right)\mathrm{d}s\right]\\ +F(\dot{y})\delta(s-s_{d})=0\end{array}  \tag{7}\]  
where the over-dot and prime denote the partial differentiation with respect to \(t\) and \(s\), respectively. \(\delta(s\)-\(s_{d})\) is the Dirac delta function, and \(s_{d}\) denotes the location of the dry friction restraint. \(F(\dot{y})\) is the dry friction force acting on the pipe, and the direction is related to the lateral velocity at the location \(s_{d}\). When the stick and sliding occur between the contact surfaces, the dry friction force can simulate the real situation well. It is well-known that dry friction force is produced by applying constant pressure to the friction interfaces. When the flow velocity exceeds a certain value, the pipe may generate flutter instability and result in motions. Therefore, the classic coulomb friction model is suitable to approximate the effect of dry friction. This model considers the dynamic friction, ignoring the difference between static and dynamic situations, which is suitable for the situation where adhesion rarely occurs. Thus, the coulomb friction model considers dry friction as a discontinuous nonlinear load. The dry friction is proportional to the normal pressure acting on the friction surfaces, and is independent of the contact area. The direction of the friction force is opposite to the movement direction of the friction surfaces. The expression of the friction force is given by [17]. \[F(\dot{y})=\mu F_{N}\mathrm{sign}[\dot{y}(s_{d},t)] \tag{8}\]  
where \(\mu\) is the friction coefficient; \(F_{N}\) is the normal pressure, and \(\mathrm{sign}[\dot{y}(s_{d},t)]\) denotes the sign function. It is noted that \(\mathrm{sign}[\dot{y}(s_{d},t)]\) is equal to \(1\) when \(\dot{y}(s_{d},t)\) is positive and otherwise it is equal to \(-1\). Now the following non-dimensional parameters are introduced  
\[ \begin{array}{l}\xi=\frac{s}{L},\eta=\frac{\dot{y}}{L},\tau=\sqrt{\frac{EI}{ m+M}\frac{l}{L^{2}}},u=\sqrt{\frac{M}{EI}}UL,\beta=\frac{M}{m+M},\\ \alpha=\sqrt{\frac{l}{E(M+m)}\frac{E^{*}}{L^{2}}},\gamma=\frac{m+M}{EI}L^{3}g,f_{n}=\frac{uF_{N}L^{2}}{EI}\end{array}
# EdgeX over Kubernetes: Enabling Container Orchestration in EdgeX
## 6 Conclusions  
As a representative open-source-based edge computing platform, EdgeX Foundry has been considered a key enabling technology for building IoT edge computing infrastructure.  
Figure 7: Performance of EoK with HPA: (**a**) average CPU usage, (**b**) throughput, and (**c**) latency of each replica set.  
ture. However, due to the absence of container orchestration functions, there are several limitations to its use in an IoT edge computing environment, where edge nodes are geographically dispersed. To overcome this problem, we proposed EoK, which runs EdgeX Foundry over a representative container orchestration platform, Kubernetes. Through performance evaluation, we verified that the throughput and QoS can be significantly improved through resource autoscaling, while the manageability of the system is enhanced with the remote service deployment. In future studies, we will investigate the deployment of connectivity services to support various protocols through heterogeneous IoT devices. Moreover, we will study the load balancing mechanism among edge nodes that can address the imbalance of user demands in IoT edge computing infrastructure. We believe the proposed EoK platform contributes to the acceleration of the realization of industrial and dispersed IoT edge computing environments by offering flexibility pertaining to various protocols and in managing nodes and services.  
Conceptualization, S.L., L.-A.P. and T.K.; experiment, S.L. and L.-A.P.; writing--original draft preparation, S.L.; review & editing, L.-A.P. and T.K.; supervision, D.-H.P. and T.K.; funding acquisition, D.-H.P. and S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00387, Development of ICT based Intelligent Smart Welfare Housing System for the Prevention and Control of Livestock Disease).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Model
### Friction Characteristic Model  
Given the complexity of the underground drilling environment, the control system of the stabilized platform is susceptible to the influence of nonlinear friction. Consequently, it  
Figure 1: Control object model of stabilized platform.  
is necessary to conduct a thorough analysis and research of the friction model. The LuGre model, initially proposed by [PERSON] in 1995, provides a comprehensive framework for describing the dynamic and static aspects of various friction phenomena [20; 21].  
The expression for the LuGre model is as follows:  
\[F_{f}=\sigma_{0}z+\sigma_{1}\frac{dz}{dt}+\sigma_{2}w, \tag{2}\]  
\[\frac{dz}{dt}=w-\frac{|w|}{g(w)}z, \tag{3}\]  
\[g(w)=F_{c}+(F_{s}-F_{c})e^{-(\frac{w}{\sigma_{2}})^{2}}, \tag{4}\]  
where \(\omega_{s}\) is the angular velocity of the tool face; \(Z\) is the deformation of the bristle; \(F_{f}\), \(F_{c}\), and \(F_{s}\) represent the friction torque, Coulomb friction, and static friction, respectively; and \(\sigma_{0}\), \(\sigma_{1}\), and \(\sigma_{2}\) represent the stiffness coefficient, viscous damping coefficient, and viscous friction coefficient, respectively.  
The LuGre friction model effectively describes both static and dynamic frictional processes. Moreover, it possesses the capacity to elucidate intricate phenomena, encompassing viscous friction, Coulomb friction, and Stribeck friction. In the context of operating the control system for a stabilized platform employed in rotary steerable drilling, it is essential to acknowledge the potential manifestation of non-linear frictional effects. These effects encompass phenomena such as low-speed crawling, steady-state error, and limit cycle oscillation. Given the inherent compatibility between the friction characteristics modeled via the LuGre model and the friction encountered during the rotational processes of the stabilized platform, we select the LuGre model for integration into the control system and conduct rigorous research and analysis.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Indoor Environments with Single High Frequency Acoustic Waves
#### 5.2.3. Affected by the Elevation  
In this section, we test the angular errors when the speaker and the smartphone are not at the same height. We set \(L=8\) m, \(\phi=45^{\circ}\) and \(\theta=0^{\circ};10^{\circ};20^{\circ}\) and \(\beta=0^{\circ}\). To vary different elevation angles, we fix the speakers on different heights of the pillars in the parking lot. We can find the different performances of the VULA system and the VUCA system in different scenarios in Figure 10a. We detect that the VULA is affected when the elevation angle \(\theta\) grows. On the other hand, the VUCA's accuracy remains almost the same as different elevation angles. We believe the main reason for these results is that the signal model of the VULA system is two-dimensional, while the signal model of the VUCA system is three-dimensional. In other words, only the VUCA system takes the elevation angle of the target signals \(\theta\) into consideration. In addition, the VUCA system has another advantage that it can also calculate the specific value of elevation angle \(\theta\) by the UCA-RB-MUSIC algorithm. For example, it helps us to determine whether the anchor points are on the same floor with us or not in a big shopping plaza. This characteristic can bring more productive experiences to the user.
## 4 Security Analysis of the MAPP Protocol
### Security Requirement Analysis
#### 4.1.6 System Updates  
Our protocol provides a dynamic key generation in each zone that allows vehicles to use different secret and public keys for a short time. System updates prevent the key compromising attacks and protect the security material from sniffing attacks. Using a single key for a long time can be hacked within a defined time, after many trials to break it. Our system supports a list of secret keys, public keys, and pseudo-identities that help the vehicles to use short time keys and pseudo-identities.  
From the previous analysis of different security requirements, we can prove that the proposed authentication methods support the standard security requirements, with a low-cost overhead.
## 5 Results
### Flows over an Asymmetric RAE2822 Airfoil  
The capabilities of the developed code equipped with shock capturing, described in Section 2.3, are further verified to deal with both transonic compressible and nearly incompressible flows over an asymmetric RAE2822 airfoil. The computational domain is discretized by a fully unstructured mesh having 4386 triangle elements, as shown in Figure 7. A set of simulations with different flow conditions was carried out with different accurate orders. Here, numerical results of third-order (\(p=2\)) accurate simulations are presented in Figure 8 for the nearly incompressible flow ( \(Ma=0.01\) and \(\alpha=1.89^{\circ}\)), and in Figure 9 for the transonic shocked flow (\(Ma=0.729\) and \(\alpha=2.31^{\circ}\)). It can be noted from Figure 8 that the obtained surface pressure coefficients (see Figure 8a) are in good agreement with other researchers' results [35; 36] and the Mach contours are distributed smoothly (see Figure 8b). Additionally included in Figure 9a are the experimental data [37] and other researchers' results [38]. Quite a good agreement can also be noted in view of the strength or location of the captured shock. Frankly, such shocked solution is based on the strength and the affected region of the artificial viscosity. As mentioned in the References [8; 28], the region functioned with artificial viscosity is as narrow as possible for the given mesh. The results can be further improved when using local refinement of the mesh near the shock, but this is not considered here.  
Figure 6: Contours of the Mach number for flows over NACA0012 airfoil. (**a**) Without preconditioning; (**b**) with preconditioning.  
Figure 7: Computational mesh around RAE2822 airfoil.  
It should be mentioned that based on the preconditioned Euler equation, the shock capturing is implemented and only functions when shock appears, and hence the performance of the developed code in convergence is still kept for the nearly incompressible flow. Actually, both explicit and implicit time marching schemes described in Section 4 are used for the code developed. The implicit speed up of convergence histories is remarkable for the above nearly incompressible flow, as illustrated in Figure 10, in view of being consuming in both iteration and time.  
Figure 8: Computational results for the nearly incompressible flow around a RAE2822 airfoil. (**a**) Surface pressure coefficients (Cao [35], Puoti [36]); (**b**) Contours of Mach number.  
Figure 9: Computational results for the transonic flow around a RAE2822 airfoil. (**a**) Surface pressure coefficients (Experiment [37], Yasue [38]); (**b**) Contours of Mach number.
## 3 Results and Discussion
### N-Glycan Data from the 2D-LC-MS Method  
The 2D-LC-MS method has been developed for the analysis of complex samples with high efficiency in recent decades [28]. The FabRICATOR-HPLC column is a new immobilized IdeS-HPLC column from Genovis, and can completely digest mAbs in only 10 min, which makes it possible to construct a 2D-LC-MS system with on-line IdeS digestion for rapid N-glycan analysis. In this study, we constructed a 2D-LC-MS system to quickly analyze N-glycan of mAbs, using the FabRICATOR-HPLC column as the first dimension for on-line IdeS digestion and the RP mAb Polyphenyl column as the second dimension for the separation of subunits. Figure 3C shows representative deconvoluted MS spectra of the Fc/2 fragment of mAb A from the 2D-LC-MS method with on-line immobilized IdeS digestion. G0F-GN, G1F-GN, G0F, G1F, G2F, and Man5 were identified as the six major glycans, which were the same as those from the reduction method and the off-line IdeS digestion method. Moreover, C-terminal lysine truncation was the main PTM, which was the same as the results from the off-line IdeS digestion method. Representative raw MS spectra of the Fc/2 fragment of mAb A from the 2D-LC-MS method is shown in Supplementary Materials, Figure S1C. The method was run in triplicate, and the details are shown in Supplementary Materials, Table S3. The average glycan ratios (n = 3) of G0F-GN, G1F-GN, G0F, G1F, G2F, and Man5 were 4.74%, 2.97%, 49.46%, 34.75%, 7.77%, and 0.31%, respectively. The RSD (n = 3) of G0F-GN, G1F-GN, G0F, G1F, G2F, and Man5 were 3.31%, 3.86%, 0.76%, 1.41%, 2.12%, and 6.73%, respectively.
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 4 Simulation Results and Analysis
### BER Performance  
Figure 11 illustrates the BER of the noise-tolerant method with RS coding. Considering the burst errors caused by phase cycle slips in the Diff-4 th methods, differential quadrature phase shift keying (DQPSK) modulation is also incorporated for further analysis. The findings indicate the following: (i) the BER of the noise-tolerant method exceeds that of the Diff-4 th methods; (ii) at \(E_{b}/N_{0}\) values below 5.2 dB, the performance of the noise-tolerant method exhibits stable performance, free from the burst errors observed in the Diff-4 th methods when QPSK modulation is employed; (iii) while DQPSK modulation effectively mitigates the impact of phase cycle slips, it incurs an approximate 0.5 dB degradation in performance at a BER of \(10^{-5}\) relative to QPSK modulation.  
Combining the results from Sections 4.2 and 4.3, the proposed method demonstrated superior phase estimation continuity and accuracy compared to traditional methods under low-SNR conditions (\(E_{b}/N_{0}\leq 5\) dB). Specifically, it achieved lower LRMSE and BER values, demonstrating enhanced noise tolerance capability.
## 3 Classification of MPPT  
### P&O Method  
In perturb and observe method (P&O) a voltage perturbation is provided with respect to the change in the power of the panel. The conditions are, if \(dP/dV\) = 0, MPP is reached. Otherwise, \(dP/dV\) < 0, reduce the duty cycle by a value and \(dP/dV\) > 0, increase the duty cycle by a value [4; 5; 6]. Figure 4 represents the P&O based MPPT and step by step process of the P&O algorithm is depicted in the flowchart shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Specification** & **Values** \\ \hline Peak power, \(P_{mpp}\) & 60 W \\ Peak Power voltage, \(V_{mpp}\) & 17.1 V \\ Peak Power Current, \(I_{mpp}\) & 3.5 A \\ Short-circuit current, \(I_{sc}\) & 3.8 A \\ Open circuit voltage, \(V_{oc}\) & 21.1 V \\ Temperature co-efficient of current, \(K_{i}\) & 0.003 (mA/\({}^{\circ}\)C) \\ Temperature co-efficient of voltage, \(K_{V}\) & \(-\)0.08 (mV/\({}^{\circ}\)C) \\ Solar irradiation at STC & 1000 W/m\({}^{2}\) \\ Temperature at STC & 25 \({}^{\circ}\)C \\ Electron charge, \(q\) & 1.6 \(\times\) 10\({}^{-19}\) C \\ Number of cells connected in series, \(N_{s}\) & 36 \\ Number of cells connected in parallel, & 1 \\ \(N_{p}\) & 1 \\ Ideality factor of the diode, \(n\) & 1.3 \\ Boltzmann's constant, \(k\) & 1.3805 \(\times\) 10\({}^{-23}\) J/K \\ Band gap energy, \(E_{g\circ}\) & 1.12 eV \\ Series resistance, \(R_{s}\) & 0.0018 \(\Omega\) \\ Shunt resistance, \(R_{slt}\) & 400 \(\Omega\) \\ \hline \end{tabular}
\end{table}
Table 1: MSX-60W PV Module Specifications.  
Figure 3: I-V characteristics of MSX 60W PV panel for (**a**) varying irradiance (**b**) varying temperature.  
Figure 4: Block diagram of Perturb and Observe method.  
Figure 5: Flowchart of Perturb and Observe method.
# From Patient to Musician: A Multi-Sensory Virtual Reality Rehabilitation Tool for Spatial Neglect
## 4 Design of the VR Therapy for USN
### VR Therapy Module
#### 4.1.2 Scales Task  
The _Scales_ task is the first training task. In this task, the patient has to play scales on the xylophone, i.e., the patient has to sequentially hit each key in order, starting from their non-neglected side towards their neglected side. For left-sided neglect, this corresponds to playing the keys from right to left. This task familiarises the user with the off-centre layout of the instrument (cf. Figure 7) and explores whether the patient has a certain threshold for which they believe it is the last key on the instrument.
## 2 Background  
### Type of Communication Methods  
Various network technologies can be used for IoT data communication. Table 1 compares the advantages and disadvantages of different communication methods: Sigfox, LoRa, NB-IoT, LTE-M, and 4G/3G.  
In the existing 3G/LTE cellular-based system, LPWAN communication technologies such as Sigfox, LoRa, and NB-IoT (LTE Cat NB1) compete for large-scale deployment, which can be used as complementary technologies as shown in Figure 1, which is a graph showing the comparison in date rate vs. coverage of various communication methods. Comparably, Figure 2 illustrates the features of current communication technologies in a diagram to help gain better understanding of the differences between Sigfox, LoRa, NB-IoT, and LTE.  
Figure 1: Types of Communication Methods [2].  
Figure 2: Features of Current Communication Technologies [3].
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.9 Network Consensus Protocol Goals  
The network consensus protocol can be designed around the following key properties: (i) permission-free: any hotspot that operates reliably can participate in the network architecture; (ii) constraints can be imposed so that there is no additional benefit to complex hardware equipment in the hotspot; (iii) the protocol is Byzantine fault-tolerant; (iv) the consensus system should be based on useful, reliable, and reusable actions; (v) the transaction confirmation rate should be imposed; (vi) the hotspots should behave in a censorship-independent manner (they should not select or deselect any IoT device). In addition, the formation of sidechains can be allowed for conducting micropayments, research, and development, and for publishing betas. A sidechain is a blockchain that is connected to a parent blockchain via a two-way connection. Such sidechains have their own consensus protocols so that privacy and security can be improved. In this way, trust in the main blockchain is minimized. The two-way connection facilitates the transfer of digital assets between blockchains. In addition, such blockchains can enable the exchange of crypto tokens.
## 3 Methods
### Detection of Fertilizer Application Responses at the Field Scale  
In order to quantify the fertilization effect with regard to the total NDVI variability within one field, the variance components are estimated using an ANOVA variance decomposition method. The variance is decomposed for each field at each date (from crop emergence to harvest) using as inputs all individual pixels falling within the limit of the monitored fertilization treatment plots B-E (B-F for millet and sorghum: Figure 2). For this method the determination coefficients (R\({}^{2}\)) of a linear model estimates the part of variance explained by the fertilization treatment with respect to the total variance, and thus measure the ability to discriminate fertilization treatments using NDVI. The R\({}^{2}\) distribution among fields of a given crop type informs about the possibilities to assess fertility levels in relation to a reference plot within the same field. A higher median R\({}^{2}\) indicates a better discrimination of fertilization levels using NDVI for a given crop type at a given date. The R\({}^{2}\) distribution spread measures the consistency of the fertilizer application response between the different fields of a given crop type. R\({}^{2}\) values were marked as outliers when more than 1.5 times the interquartile range below quartile 1 or above quartile 3.  
The normality of the residuals distribution has been verified. To validate the independency hypothesis, the spatial correlation is assessed using variograms of a UAV orthoimage acquired on 10 September 2014. Variograms were developed using the \"gstat\" add-on package available in the R software.  
Additionally, given the non-linear plant response to fertilizer application, we identify the dose increment leading to the strongest reaction through a 2-by-2 comparison of NDVI means for each available dose increment. This was done for the (crop-specific) date of maximum fertilizer response. The date with the highest proportion of fields with a significant (\(t\)-test at \(p=0.05\)) response to fertilization increments was defined as the date with the strongest effect of fertilizer application on crop NDVI response.