esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ4--Edge Intelligence Application Domains
#### 4.4.5 Health  
Nowadays, even though we live in a so-called globalized world, insufficient medical resources are still a global problem according to the World Health Organization (WHO) [160]. Patients in the counryside or developing countries have difficulty obtaining high-quality and timely healthcare because of geographical barriers.  
The recent effort in integrating IoT into the healthcare scenario has been a great catalyst to the development of novel smart care applications or the enhancement of past ones, which may include telemedicine, tele-consultancy, pervasive health monitoring applications, human activity [101] and/or emotion recognition [117], and others.  
Recently, the topic of data privacy has gained prominence in the industry, news and courts. When referring to health data, it is no different. On the contrary, it requires even more concern, as we are talking about extremely sensitive data. In response to the demand for privacy, trust and control over the data, executing machine learning tasks at the edge of the system has the potential to make the healthcare services more human-centric [48].  
Besides the easy-to-notice benefits to latency-sensitive and aforementioned data-sensitive tasks, edge AI can also allow healthcare to be less costly, more efficient and incredibly beneficial in the field of cognitive assistants for the elderly, people living alone, or the people geographically segregated. EI is an interesting tool for the democratization of healthcare services.
## 4 Results and Analysis
### Physical Analog Model's Results and Analysis
#### 4.1.3 Surface Subsidence Characteristics  
Surface subsidence and horizontal deformation are the main factors that characterize strata and ground subsidence. The results reveal that the backfill body can successfully bear the overlaying strata's load. The surface's subsidence and horizontal deformation are significantly improved compared to those created with the caving mining method. Consequently, the maximum surface subsidence in the middle of the model's surface was 0.46 mm, and the maximum horizontal deformation was \(-\)0.009 to 0.0085 mm/mm. This demonstrates that solid filling can effectively regulate the strata and ground subsidence of overlaying strata by reducing surface sinking. Figure 10(a),b successively show the surface subsidence and horizontal deformation results.
## 2 Materials and Methods
### HS-SPME-GC-MS Procedure  
Next, 1.0 \(\pm\) 0.1 g of the sample was weighed into a glass vial (20 mL). Then, 5 uL of internal standard 2-methyl-1-pentanol (560 mg Kg\({}^{-1}\)) was added into the vial, followed by the addition of 2 mL of distilled water (with or without NaCl addition). A polytetrafluoroethylene (PTFE) stirring rod was added to ensure homogeneous stirring of the sample. The fiber used for the extraction of the volatiles was a DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) 50/30 mm, StableFlex, 1 cm long, mounted to an SPME manual holder assembly (Supelco, Bellefonte, PA, USA) and the procedure was carried out by using the auto-sampler of the equipment. This fiber has been previously  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Independent Variables**} & \multicolumn{3}{c}{**Coded Levels**} \\ \cline{2-4}  & \(-\mathbf{1}\) & \(\mathbf{0}\) & \(\mathbf{+1}\) \\ \hline Temperature (\({}^{\circ}\)C) & 30 & 50 & 70 \\ Time (min) & 10 & 35 & 60 \\ NaCl (M) & 0 & 2 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coded factors and levels for the BBD.  
used to extract volatile compounds from pineapple samples such as wine [31], juices [32], and pulps from tropical fruits [33].  
After the extraction process of volatile compounds, the fiber was immediately desorbed in the spitless mode for 10 min into the injection port of the GC-MS equipment. The equipment used in the analysis was a gas chromatograph (Agilent 7890B) coupled to a quadrupole mass spectrometer (Agilent 5977B). A Gerstel Multipurpose Sampler (MPS) robot was used as the sample introduction system to the chromatograph. The chromatographic column used was an Agilent DB624. The sample was subjected to the following temperature program using helium as carrier gas with a flow rate of 1 mL min\({}^{-1}\): initial temperature of 40 \({}^{\circ}\)C, maintained for 2 min and then an increase in temperature at a rate of 5 \({}^{\circ}\)C min\({}^{-1}\) up to 250 \({}^{\circ}\)C, where it was maintained for 10 min. The energy of the ionization source used was 70 eV. The temperature values of the ion source and the transfer line were 230 and 150 \({}^{\circ}\)C, respectively. The mass detector was operated in scan mode at a mass-to-charge ratio range (\(m/z\)) of 50-230. In order to verify the absence of contaminants, blank runs were carried out between samples  
To identify unknown compounds, the comparison of their mass spectrum data (full scan mode (\(m/z\) 30-550)) with the ones found in the NIST library was carried out. A % of similarity equal or higher than 80% was fixed. In this study, two main pineapple volatile markers were selected: ethyl acetate and isopentyl acetate. Both were quantified in all the studied samples using calibration curves at six concentration levels prepared in deionized water. All determinations were carried out in triplicate.
## 3 Test Results  
### Mode-I Fracture Toughness  
Equation (2) was applied to calculate the mode-I fracture toughness from the data recorded in the SCB test. The average values of mode-I fracture toughness by processing temperature are plotted in Figure 5.  
Figure 5 shows the average mode-I fracture toughness of the sandstone samples. In the range 20-500 \({}^{\circ}\)C, the mode-I fracture toughness was between 0.64 MPa\(\cdot\)m\({}^{1/2}\) and 1.13 MPa\(\cdot\)m\({}^{1/2}\). These are relatively high values, and the range in variation is not large. Mode-I fracture toughness reached its highest value in the sample processed to 100 \({}^{\circ}\)C, slightly higher than that kept at 20 \({}^{\circ}\)C, indicating that heating to 100 \({}^{\circ}\)C had a toughening effect and made the rock more able to resist fracture. The lowest mode-I fracture toughness in the temperature range below 500 \({}^{\circ}\)C was at 500 \({}^{\circ}\)C, when it was 0.81 MPa\(\cdot\)m\({}^{1/2}\); this is about 27.03% lower than at room temperature, indicating that heating to 500 \({}^{\circ}\)C had a weakening effect and made the rock less resistant to fracture.  
When heat treatment was to higher than 500 \({}^{\circ}\)C, the mode-I fracture toughness suddenly decreased from 0.81 MPa\(\cdot\)m\({}^{1/2}\) for 500 \({}^{\circ}\)C to 0.64 MPa\(\cdot\)m\({}^{1/2}\) for 600 \({}^{\circ}\)C, a reduction by 20.99%. The mode-I fracture toughness of samples heated to 600 \({}^{\circ}\)C was 42.34% less than of those heated to 20 \({}^{\circ}\)C, indicating that exposure to such a high temperature had a very significant effect on fracture toughness, greatly weakening the ability of the rock to resist fracture. This can most probably be attributed to the fact that 573 \({}^{\circ}\)C, the transition temperature of \(\alpha\)-quartz to \(\beta\)-quartz, lies within the temperature range 500-600 \({}^{\circ}\)C. This phase transition causes severe cracking in the rock structure, significantly impacting its mechanical properties.
# Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets
## Materials and Methods
### Calculation of Bolt Loosening Angle
#### 2.4.3 Analysis of Perspective Errors
The same method as the bolt loosening angle calculation formula can be used to calculate the error angle caused by perspective. \[\left\{ \begin{array}{c}\varepsilon_{i}=\alpha_{i}^{\#}-\alpha_{i}\\ \varepsilon=\frac{\sum_{i}\varepsilon_{i}}{6}\end{array} \right. \tag{8}\]  
where \(\varepsilon_{i}\) is the error angle of each corner point and \(\varepsilon\) is the average of the error angle for all the corner points and is used as the perspective error angle value. Combining Equations (6)-(8) demonstrates that the error is only related to \(\alpha_{i}\) and \(\beta\). According to Figure 15b, the variation range of \(\alpha_{i}\) is [60\({}^{\circ}\), 120\({}^{\circ}\)). Based on engineering experience, the camera's shooting angle \(\beta\) falls between [0, 60). In the above range, we calculated the error angle caused by the perspective, as shown in Figure 16.  
The following conclusions can be obtained from the figure:  
1. The perspective error increases as \(\beta\) increases and is almost zero at \(\beta\in\) [0, 20];
2. With constant \(\beta\), the error shows a sinusoidal variation as \(\alpha_{i}\) varies. When \(\alpha_{i}\) is equal to 75\({}^{\circ}\) or 105\({}^{\circ}\), the error is taken to the maximum value;
3. As \(\beta\) moves closer to 90\({}^{\circ}\), the error becomes larger more rapidly;
4. When \(\beta\) is less than 60\({}^{\circ}\), the error angle is less than 0.58\({}^{\circ}\); when \(\beta\) is less than 45\({}^{\circ}\), the error angle is less than 0.09\({}^{\circ}\). In summary, using the bolt loosening angle calculation method proposed in this paper, in general, engineering scenarios, the perspective error caused by the camera angle is small enough to avoid additional processing.
## 2 Receiver Architecture  
It is known that a GNSS signal in space (SIS) is transmitted by using a Code Division Multiple Access (CDMA) format and the received signal of the GPS L1 C/A code can be written as a combination of the SISs of all the satellites in view, each one expressed by  
\[y_{m}(nT_{s})=\sqrt{2P_{m}}C_{m}(nT_{s}-\tau_{m})d_{m}(nT_{s}-\tau_{m})\cos(2 \pi(f_{IF}+f_{d,m})nT_{S}+\varphi_{m})+w(nT_{S}) \tag{1}\]  
where \(m\) is the index of a specific satellite, \(P_{m}\) is the received power, \(C_{m}(\cdot)\) is the Pseudo Random Noise (PRN), \(d_{m}(\cdot)\) is the navigation data, \(f_{IF}\) is the intermediate frequency of the front-end, \(f_{d}\) is Doppler frequency, \(\varphi_{m}\) is a phase term, \(\tau_{m}\) is the code delay and \(T_{s}\) is the sampling interval. The second term of the sum is a noise component \(w(nT_{S})\), that is a discrete-time random process obtained by sampling the noise at the front-end output. Since the input noise may be assumed to be White Gaussian Noise (WGN) with power spectral density \(S(f)=N_{0}/2\) at the front-end output, \(w(nT_{s})\) becomes a Gaussian discrete-time random process with zero mean and variance \(\sigma^{2}=N_{0}B_{IF}\), where \(B_{IF}\) is the front-end bandwidth. Therefore Equation (1) represents a so called Additive White Gaussian noise (AWGN) channel. Thanks to the code orthogonality, the receiver can discriminate different signals coming from different satellites. Therefore the analysis of the receiver can be done considering only a single signal \(y_{m}(nT_{s})\).  
In our experiments we used the receiver architecture described in [17], and in particular we implemented a distortion-detector working at the tracking stage. The detector structure is strictly related to the working principle of the DLL and to the mechanism of PR measurements which are described in Sections 2 and 3.1.
# Vibration of Rotating and Revolving Planet Rings with Discrete and Partially Distributed Stiffness
## 3 Vibration Analysis
### Free Planet Ring  
In practical applications, such as planetary gear systems, planet rings behave like free rings in idle status. Figure 3a shows the natural frequencies of a free planet ring, with parameters in Table 1 calculated by the Galerkin method. The natural frequencies have degeneracy two, which is typical in axisymmetric spinning systems, like spinning disks [31] and spinning disk-spindle systems [32]. Two natural frequencies with the same nodal diameter separate at non-zero rotation speed and become different as shown in Figure 3a. One represents vibration waves propagating in the positive \(\theta\) direction, and the other represents vibration waves propagating in the negative \(\theta\) direction, which corresponds to backward waves and forward waves, respectively. Moreover, the corresponding vibration mode for all the frequencies is dominated by only one nodal diameter. The representative vibration mode and its components at \(\omega\) = 1 are shown in Figure 3b. The eigenvalues for the system are purely imaginary over the range of analyzed speeds. It can be found that 1 ND backward frequencies intersect with abscissa at point C in Figure 3a. This indicates that possible unstable phenomena exist and the value at point C is the critical speed.  
Frequency veering occurs in multiple regions with the increase of rotation speed, such as the areas V1, V2, V3, and V4 in Figure 3a, and V2 for 1 ND and 2 ND at \(\omega\) = 3.5, etc. In these regions, the lower ND frequencies become larger than the higher ND frequencies, that is, the modes exchange here. Figure 4 shows the modes exchange with the frequency veering at V2 around \(\omega\) = 3.5. All the other veering regions have similar behavior. This  
Figure 3: (a) Natural frequencies of a free planet ring versus varying speed; (b) representative vibration mode and its components at speed \(\omega\) = 1 (dash line = original position; solid line = vibration mode).  
Figure 2: The dimensionless natural frequencies with respect to dimensionless rotation speed with the parameters in Reference [22].  
means that, with the increase of rotation speed, the high-order frequencies may be dominated by lower ND components in the vibration mode, and, conversely, the low-order frequencies may be dominated by high ND components.
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 4 Discussion
### Implications  
Through quantitative statistical research, the current meta-analysis study first revealed tactile warnings to potentially be the best unimodal warnings, and auditory-tactile bimodal warnings can better optimize drivers' performance than unimodal warnings. These findings can be implemented not only in the driving field, but also industries such as aviation and construction to increase operation safety. Moreover, based on the research findings that trimodal warnings had no significant facilitatory effect as compared to bimodal warnings on drivers' response time, the current study proves that there might be a ceiling effect on MIE due to cognitive overload, which provides theoretical research insights for neuroscientists.
## 3 Results and Discussion
### Characterization of Optimized ETB-NE
#### 3.4.3 Refractive Index and Percentage Transmittance of ETB-NE  
The refractive index of value provides a measure of the easiness with which light can pass through it. Thus, any refractive index value near to water (the dispersion medium), indicates the transparent nature of the NE [36]. In the present study, the refractive index of the ETB-NE was found to be 1.39 \(\pm\) 0.02 at 25 \({}^{\circ}\)C. This value is very close to the refractive index of water at 25 \({}^{\circ}\)C, i.e., 1.3329 [37]. Considering the observed transparency of the prepared ETB-NE, this value was in line with expectations. Meanwhile, the refractive index value near that of water indicated the possibility of a high percentage transmittance for the ETB-NE. As expected, the percentage transmittance observed of ETB-NE was 98.16 \(\pm\) 0.22%. Further, this high percentage transmittance value was in agreement with reported values for NEs [38].  
Figure 3: Numerical optimization data for ETB-NE by Box–Behnken design, showing the optimized values for independent factors and predicted values for the PS and PDI.
## 2 Materials and Methods  
### Materials and Instrumentations  
2-Cyclohexen-1-one, (E)-but-2-enal, dry CH\({}_{2}\)Cl\({}_{2}\) (\(\geq\)99%), n-hexane (\(\geq\)98%), acetonitrile (\(\geq\)99%), chloroform (\(\geq\)99%), deuterochloroform (CDCl\({}_{3}\), 100%, 99.9 D) toluene (\(\geq\)99%), hexadeuterodimethylsulfoxide (DMSO-\(d_{6}\), 100%, 99.9 D), THF (HPLC grade, \(\geq\)99.8%), and PtCl\({}_{2}\) were purchased from Aldrich. \(\upiota\)-lactide was purchased from Purac. 4-Methyl-4'-(hydroxymethyl)-2,2'-bipyridine (Bipy) was purchased from Imperial Chemical Industries(I.C.I.) (\(\geq\)99.8%).  
GC analyses were carried out on a Shimadzu 2010 (VFWAX column 30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upiota\)m) and GC-MS analyses were run using a Shimadzu GC-MS QP5050A (Petrocol\({}^{\mathrm{TM}}\) column 100 m \(\times\) 0.25 mm \(\times\) 0.5 \(\upiota\)m).  
NMR analyses were performed with a Varian model GEMINI 400 MHz using CDCl\({}_{3}\) or DMSO-\(d_{6}\) as the solvent. Integral errors were \(\pm\)5%.  
GPC analyses were carried out with a [PERSON] model binary HPLC 1525 system equipped with three columns (Shodex KF-802.5, KF-803, KF-804) and a refractive index detector Wyatt T-REX Optilab. Analyses were performed with THF as the eluant.  
UV-Vis analysis was carried out using a Varian model Cary 4000 UV-Vis spectrophotometer.  
Fourier transform infrared spectroscopy (FTIR) was performed using a Shimadzu model IR Affinity-1, equipped with a Golden Gate single reflection diamond ATR accessory supplied by Specac. Elemental analysis was carried out using a Perkin-Elmer 240 CHN model.  
Differential calorimetric analyses were carried out using a Perkin Elmer Pyris-1-dsc. Samples were tested from 40 \({}^{\circ}\)C to 180 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min.  
Catalytic reactions: Homogeneous catalytic hydrogens of \(\upalpha\),\(\upbeta\)-unsaturated substrates were carried out in a 150 mL stainless steel homemade autoclave thermostated in an oil bath (\(\pm\)1 \({}^{\circ}\)C) containing a glass vessel. The substrate, solvent, and catalyst were introduced in the glass vessel, under a nitrogen atmosphere, then the autoclave was closed. The autoclave was pressurized at room temperature with hydrogen, then stirred and heated at the prefixed temperature for the established time. At the end of the reaction, the autoclave was rapidly cooled to room temperature, the gas vented, and the solution collected and transferred into a 50 mL Schlenk tube. A sample of the solution was taken for the GC and GC-MS analyses. Each test was repeated three times, and the mean value was reported.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 4 MKIDs in Particle Physics
### MKIDs for Neutrino Physics Experiments
#### 4.1.3 Holdmes  
HOLMES is an experiment aimed at the direct measurement of the electron neutrino mass produced by electron capture decay of radioactive holmium nuclei \({}^{163}\)Ho. The experiment is set up to measure the energy freed by the decay of \({}^{163}\)Ho, detecting all the energy except for the mass of the neutrino. This simple calorimetric measurement is extremely powerful as it is completely independent on the model used to describe the physics of the neutrino particle. HOLMES is currently deploying Transition Edge Sensors provided by NIST, which are frequency domain multiplexed by coupling each TES to a SQUID (Superconducting QUantum Interference Device) and a readout MKID [132] to create an array of resonant circuits with unique resonant frequencies. Unfortunately, TES are not ideal detectors for future upgrades of the experiment; even with frequency domain multiplexing the scalability of the array size beyond 1 kilo-pixels is difficult and the fabrication of such arrays could be a major challenge. In order to prepare for future upgrades to HOLMES MKID arrays are therefore under development. Their scalability to Mega-pixel numbers is significantly easier and their fabrication is also simpler as MKIDs are built on few superconducting layers. The cryogenic setup and read-out electronics of MKIDs being significantly simpler than that of TESs is another argument in favor of the technology switch. A Mega-pixel MKID array can yield sub-eV sensitivity for this experiment in the energy range of interest [133].  
The MKIDs being developed for HOLMES are meant to be X-Ray detectors; typically, these detectors consist of an absorber coupled to a micro-resonator. The absorber, tantalum (Ta) in this case, having a high stopping power, absorbs X-Rays and produces quasi-particles which then propagate to the micro-resonator where they are efficiently trapped, and a detectable signal is produced. MKIDs can also be operated as thermometers (TKIDs), an increase in temperature could produce quasi-particle excitations the same way a photon would. This second approach exploits the thermal absorption of X-Rays in the absorber rather that the ionization of the same. Both detecting mechanisms are currently under investigation for the sake of the HOLMES experiment [133]. For this purpose, MKIDs made of Ti/TiN multilayers are being fabricated. Through the stacking of multiple layers of Ti and TiN the critical temperature of the superconductor can be tuned through the proximity effect with great accuracy from 70 mK to 4.6 K [133]. In the process of the optimization of TKID detectors, Ti/TiN multilayers micro-resonators have been coupled to a gold (Au) absorber deposited on a suspended silicon nitride (SiN\({}_{\mathrm{x}}\)) membrane. The performance of these detectors is still under investigation [134].
# A Hierarchical Resource Allocation Scheme Based on Nash Bargaining Game in VANET
###### Abstract  
Due to the selfishness of vehicles and the scarcity of spectrum resources, how to realize fair and effective spectrum resources allocation has become one of the primary tasks in VANET. In this paper, we propose a hierarchical resource allocation scheme based on Nash bargaining game. Firstly, we analyze the spectrum resource allocation problem between different Road Side Units (RSUs), which obtain resources from the central cloud. Thereafter, considering the difference of vehicular users (VUEs), we construct the matching degree index between VUEs and RSUs. Then, we deal with the spectrum resource allocation problem between VUEs and RSUs. To reduce computational overhead, we transform the original problem into two sub-problems: power allocation and slot allocation, according to the time division multiplexing mechanism. The simulation results show that the proposed scheme can fairly and effectively allocate resources in VANET according to VUEs' demand.  
VANET; resource allocation; edge cloud computing; Nash bargaining game +
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