esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Previously Developed Pressure Sensor  
The proposed sensor employed an FPI-based pressure sensor previously proposed by one of the authors [29]. Figure 1 presents an overview of the previously developed sensor. The ultra-miniature fiber-optic pressure sensor (UMFOPS) had an FPI formed at the end of an optic fiber. A deformation of the diaphragm in the FPI induced by pressure varies the cavity length \(d\), which can be detected by a spectrometer as a change in spectrum of the reflected light. This change is caused by the white light interferometry of the low-coherence light occurring in the FPI [38]. Therefore, \(d\) can be obtained by the following equation.  
\[d=\frac{\lambda_{1}\lambda_{2}}{2n(\lambda_{2}-\lambda_{1})}, \tag{1}\]  
where \(n\) is the reflective index of the material of the sensor cavity and \(\lambda_{1}\) and \(\lambda_{2}\) are adjacent peaks in the reflection spectrum. UMFOPS is sterilizable, bio-compatible, miniaturized (125 \(\upmu\)m in diameter), immune to electromagnetic noise, and has a low sensitivity to temperature change [29]. We selected the FPI sensor to be a key element of the proposed sensor, because, compared to an FBG, it has a lower temperature sensitivity and a narrower wavelength range that must be monitored. Because the range of measurable wavelength predominantly affects the cost of the spectrometer thus the device, the narrower wavelength range is an advantage for further applications.
###### Abstract  
Accurately forecasting how crowds of people are distributed in urban areas during daily activities is of key importance for the smart city vision and related applications. In this work we forecast the crowd density and distribution in an urban area by analyzing an aggregated mobile phone dataset. By comparing the forecasting performance of statistical and deep learning methods on the aggregated mobile data we show that each class of methods has its advantages and disadvantages depending on the forecasting scenario. However, for our time-series forecasting problem, deep learning methods are preferable when it comes to simplicity and immediacy of use, since they do not require a time-consuming model selection for each different cell. Deep learning approaches are also appropriate when aiming to reduce the maximum forecasting error. Statistical methods instead show their superiority in providing more precise forecasting results, but they require data domain knowledge and computationally expensive techniques in order to select the best parameters.  
forecasting; time series; crowd distribution; aggregated mobile phone data; deep neural networks +
Footnote †: journal: Applied Sciences
# Experimental Analysis of a Novel GFRP Stiffened Pipe
## 1 Introduction
[22] designed and fabricated a new integrated woven corrugated composite sandwich panel, which effectively prevented skin-to-core debonding. [PERSON] et al. [23], [PERSON] et al. [24], and [PERSON] et al. [25] designed and manufactured different integrated light-weight woven lattice panels and reported their failure mechanisms and excellent energy-absorption abilities. [PERSON] et al. [9] investigated a novel GFRP sandwiched pipeline structure with stiffened core, and results showed that the stiffness of the proposed pipe structure increased with considerable weight reduction. In general, many investigations focused on the mechanical behavior of FRP pipes subjected to internal hydrostatic, axial, or bending load [13; 15; 19; 26; 27; 28; 29]. However, lateral compression on buried pipelines should also be considered due to their working environment where the combined action of dead load (from the weight of the soil above the pipe) and live load (by vehicles traversing the pipeline on the surface) can arise [30]. [PERSON] et al. [31], [PERSON] et al. [31], and [PERSON] et al. [32] carried out lateral crushing experiments to explore the energy absorption capacity of tubular structures. More recently, [PERSON] et al. [33] and [PERSON] et al. [34] conducted comparative studies to investigate the lateral crushing behaviors of thin-walled aluminum, GFRP, and CFRP tubes. Despite its importance for facilitating light-weight design and manufacturing techniques for buried composite pipelines [9], the mechanical behavior of the GFRP structures under lateral compressive load has not received sufficient attention [35; 36; 37]. With the aim of further filling this knowledge gap, this study investigated a new GFRP pipeline structure with a stiffened layer to characterize its structural behavior under flatwise compression tests. Firstly, a complete explanation of the proposed pipe fabrication and testing methodology were established. Secondly, experimental and analytical studies were conducted to investigate the PS of the proposed pipe. Accordingly, the analytical and experimental results of PS are compared and discussed. In addition, the ring stiffness values of the commonly used pipes buried underground and the proposed pipes with different stiffened structures were measured in comparative experiments using the same materials. Furthermore, the deflections and buckling behaviors of various FRP pipes for different stiffened structures are described and discussed. The research showed that the stiffened tubes with rough surfaces had stronger interfacial bonding than the regular tubes with smooth surfaces [38]. The proposed pipes with rough surfaces may therefore offer a potential solution to the concrete-GFRP interfacial bonding problem. Most importantly,the envisaged method has improved stiffness and a cost-effective nature, which enables it to be adopted practically in engineering.
## 3 Results
Based on the vibration-assisted Nusselt value, Reynolds values corresponding to each vibrating condition are calculated using Equation (8) [49], and the percentage reduction in Reynolds number is calculated using Equation (9). \[Nu_{without}=0.664\sqrt{\frac{Re.b}{L}}.Pr^{1/3}\sqrt{1+\frac{3.65}{\sqrt{\frac {Re.b}{L}}}} \tag{8}\]  
Figure 5:0  
Here, \(Nu_{without}\) is the Nusselt number in static conditions, \(Re\) represents the Reynolds number, \(Pr\) represents the Prandtl number, \(b\) and \(L\) represent the fin spacing and fin length, respectively. The variation in the percentage reduction of the Reynolds number with respect to vibrational frequency is presented in Figure 11a. Since the enhancement of the Nusselt value is found to be insignificant below the threshold frequency, the increase in percentage enhancement in Reynolds number below 30 Hz is insignificant. As vibration frequency increases above the threshold value, the percentage enhancement in Reynolds increases with an increase in frequency, and a higher enhancement is recorded with a thin plate-fin heat sink. It was found that the percentage reduction in Reynolds number increased up to a maximum of 23% and 27% with conventional and thin plate-fin heat sinks, respectively. Since the increase in amplitude resulted in an enhancement of Nusselt value, as seen in Figure 11b, an increase in vibrational amplitude leads to an increase in percentage reduction in Reynolds number. As expected, the enhancement of \(PR_{Re}\) is found to be prominent with a thin plate-fin heat sink. In comparison with non-vibrating heat sinks, a 28% and 33.3% maximum enhancement in \(PR_{Re}\) is recorded with conventional and thin plate-fin heat sinks, respectively. These results revealed that the application of vibration to heat sinks provides a possible reduction in the size of the cooling system or fin size, while a thin plate-fin heat sink provides higher thermal benefit and a reduction in the size of the cooling system in comparison with the conventional heat sink module. Thus, a vibrating thin plate-fin heat sink that is subjected to sinusoidal vibration will be more suitable for applications in electronic cooling systems such as data centers for heat-transfer enhancement, due to the higher possible reduction in the cooling system size or heat sink size. This research focused on investigating the effect of vibration frequencies, amplitudes and Reynolds number values on heat sinks' thermal performance. Future research could be conducted by investigating more variables such as fin height on heat sink heat-transfer enhancement.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Deposition Techniques
#### 5.1.2 Electrophoretic Deposition  
In contrast to chemical vapour deposition, carbon-based electrode materials are commonly deposited by electrolytic and electrophoretic methods. These methods have been documented for activated carbon [142], carbon nanotubes [143], graphene [144], graphene quantum dots [145], and onion-like carbon [146]. They are cost effective, relatively fast, simple, and can be up-scaled using relatively simple fabrication equipment. Moreover, the deposited film thickness can be well controlled using this method. Electrophoretic deposition involves the diffusion, migration, and deposition of charged nanoparticles, nanotubes, or nanosheets onto pre-patterned current collectors by an electric field.  
Figure 7: Illustrations of thin film electrode fabrication techniques, with (**A**) chemical vapour deposition, (**B**) coating, (**C**) vacuum filtration, and (**D**) in situ deposition. Figure reprinted with permission from [PERSON] and [PERSON] [56]. Copyright 2019 Elsevier.  
As an easy and efficient technique, electrophoretic deposition was used to cover silicon nanowires with a graphene coating, which enhanced the performance of micro-supercapacitors with such electrodes, resulting in a four times higher capacitance in comparison to uncoated silicon nanowires [147].
## 4 Proposed Local ID Mapping Scheme
### Utilization of Standard Information  
The topology-based local ID mapping scheme has a limitation in some cases where two different power system resources have the same topological characteristic. Figure 9 illustrates a case with the same TF for different objects. When graphs are created and sorted by the common rule, TFs for nodes 1 and 2 are identical since the graphs for nodes 1 and 2 are topologically symmetrical. It causes duplicated mappings to local IDs issued by another system, which leads to failed accurate object identification in data exchange. To deal with these cases, the utilization of standard information is introduced in the proposed scheme.  
Figure 8: A case with different TFs for the same topological structure.  
Figure 9: A case with the same TFs for different objects.  
Standard information is defined as common data that all systems considered for data exchange have in common. Since standard information can provide additional distinguishable data, two objects with the same TF can be also separated. For instance, the data of'switch_type' shown in Figure 4 can be used as standard information for local ID mapping between objects in DMS and CIS. If a local ID for an object has its own TF and available standard data, it enables more accurate mapping to another local ID for the same object. Therefore, standard information such as the type of switch should be included in the local ID-TF mapping table. Figure 10 shows an example of a local ID mapping table derived by using the proposed scheme.
# Satellite-Aided Consensus Protocol for Scalable Blockchains
## 5 Throughput Analysis
### Propagation Delay
(**b**) Chain state space and transition diagram. **Remark 3**: _Comparing Equations (6) and (9), we can find the impact of information propagation on blockchain throughput. Only considering at most two-round propagation delay, we can see the maximum throughput degradation by a factor of \(1/\left(1+\left(1-i_{\Delta}\right)h\right)\). In principle, we cannot control the value of \(i_{\Delta}\) for a given network but can select the proper block size \(B\) and the round slot \(\Delta\) based on (9) to maximize the blockchain throughput._  
Usually, propagation delays do not affect the behaviors of colluding adversarial miners so that the growth rate of the adversarial chain will be the same as Equation (2). Therefore, propagation delay not only affects the throughput but also may weaken the double-spending resilience.
# Internet of Things in Marine Environment Monitoring: A Review
## 5 Major Challenges and Opportunities
### Marine Environment Protection  
Currently, the environmental protection issue is one of the most important issues around the world. The ultimate goal of monitoring the ocean is to protect the marine environment. Most of the current marine environment monitoring applications collected and analyzed massive data from the ocean, but there is no action on protection controls yet. With the advancement and sophistication of IoT and Big Data technologies and their wide applications in marine environment monitoring and protection, we are confident that active marine environment protection measures, technologies, and systems will be developed and deployed in the near future. Massive data collected from marine environments will be analyzed using advanced Big Data analytics and the results will be sent to the related marine environment management agencies/control centers for quick decision making and real time manual interventions in order to protect the marine environment from some disasters (e.g., oil spills and bad weather). On the other hand, some autonomous vessels or other devices (actuators) can also automatically and quickly react to disasters and other events in order to protect the marine environment. In the case of a fishing farm application, data collected from the fish farm environment can be analyzed and used to provide the best fish growth condition and the best feeding.
## 2 Fault Signal Characteristics of Test Rig
### Test Rig and Test Conditions
#### 2.1.4 Lubrication System  
The main function of the lubrication system is to lubricate the key contact parts of the bearing and reduce the heat generated during the experiment. Owing to the high spindle speed in the experiment, an iron-based protective cover with an observation port is set to ensure safety.  
In this manuscript, the test sample is the NU210 cylindrical roller bearing, and the lubricating oil is Great Wall 4109 aviation lubricating oil. The test conditions are that the radial load is 120 N, the inner ring speed is 2250 rpm, and the lubricating oil is added at the rate of 10 mL/min.
# Equilibrium Inefficiency and Computation in Cost-Sharing Games in Real-Time Scheduling Systems +
## 2 Preliminaries  
The game is specified by the following parameters: (a) a cost function \(c(x)\) that gives the cost at any given time slot as a function of the number of jobs \(x\) that have to be processed at that time step; (b) a time horizon \(T\) that specifies the available time slots as \(t=1,2,\ldots,T\); and (c) a set of jobs \(J\), with each \(j\in J\) having an integer release time \(r_{j}\) and an integer deadline \(d_{j}\) such that \(0<r_{j}<d_{j}<T\). We denote by \(I(j)=[r_{j},d_{j})\) the feasible interval of job \(j\). Note that the slot \(t\) corresponds to time interval \([t-1,t)\). Job \(j\) needs to be processed in one time slot in its interval.  
Let \(s_{j}\) denote the slot declared by job \(j\) such that \(r_{j}\leq s_{j}<d_{j}\) and let \(s\) denote the _assignment_, i.e., the vector of declared slots. The load on slot \(t\), \(l_{t}(s)=|\{j:s_{j}=t\}|\), is the number of jobs declaring slot \(t\). The cost on slot \(t\) is then \(c(l_{t}(s))\) and the _total cost_ is:  
\[C(s)=\sum_{t=1}^{T}c(l_{t}(s)).\]  
An assignment \(s\) is a _Nash equilibrium_ (NE) when for every job \(j\) we get:  
\[\frac{c(l_{s_{j}}(s))}{l_{s_{j}}(s)}\leq\frac{c(l_{t}(s)+1)}{l_{t}(s)+1},\]  
for every \(t\
eq s_{j}\) in \([r_{j},d_{j})\). This expression suggests that the cost share at the slot declared by \(j\) is at most the cost share \(j\) would get by deviating to any other slot in its interval. The inefficiency of equilibrium solutions is given by the _price of stability_ (PoS), and the _price of anarchy_ (PoA), which are, respectively, the best and worst case ratio of the total cost in a NE over the total cost in the optimal assignment:  
\[PoS=\frac{\min_{s\text{ a NE }}C(s)}{\min_{s^{*}}C(s^{*})},PoA=\frac{\max_{s \text{ a NE }}C(s)}{\min_{s^{*}}C(s^{*})}.\]  
A firmer notion of stability requires that a profile is stable against _coordinated deviations_. A set of players \(\Gamma\subseteq J\) forms a _coalition_ if there exists a joint move where each job \(j\in\Gamma\) strictly reduces its cost. An assignment \(s\) is a _Strong Equilibrium_ (SE) if there is no coalition \(\Gamma\subseteq J\) that has a beneficial joint move from \(s\)[40]. The _strong price of anarchy_ (SPoA) and the _strong price of stability_ (SPoS) introduced in [41], measure the inefficiency of strong equilibria.  
\[SPoS=\frac{\min_{s\text{ a SE }}C(s)}{\min_{s^{*}}C(s^{*})},SPoA=\frac{\max_{s \text{ a SE }}C(s)}{\min_{s^{*}}C(s^{*})}.\]
## 1 Introduction
In addition, the work presents a ready diagnostic system that allows us to accurately classify electrical faults symptoms of IM and determine fault levels. In this paper the results of experimental research on the application of stator current symptoms of the converter-fed induction motor drive to the electrical fault detection and classification using Kohonen neural networks are demonstrated. The experimental tests of a diagnostic setup based on a virtual measurement and data pre-processing system, designed in LabView, are described. It has been shown that the developed neural detectors and classifiers based on self-organizing Kohonen maps, trained with the instantaneous symmetrical components of the stator current spectra (SCA), enable the automatic distinguishing between the stator and rotor winding faults for various supplying voltage frequencies and load torque values. The paper consists of six sections. After the introduction with a brief overview of the technical literature dedicated to fault detection of IM stator and rotor windings, the method of instantaneous symmetrical components analysis (ISCA) is presented in Section 2, as it was used in this research work for the analysis of the electrical winding damage of the induction motor drive. In the next section the main idea and features of the Kohonen self-organizing network are presented. The main part of this article is Sections 4 and 5. In Section 4 the experimental setup, the data acquisition system and the measured signals pre-processing methodology aimed at fault symptoms detection is described. In Section 5 the experimental results obtained for the developed neural detectors and classifiers based on SMSs are given, together with a discussion of the neural detectors' efficiency. The paper is concluded with a short summary.
# Influence of Yeast Strain on Odor-Active Compounds in Fiano Wine
## 3 Results and Discussion
### Statistical Analysis  
PLS regression analysis was chosen as an exploratory technique to investigate the correlation of quantitative level of volatile compounds and aromatic series resulting from GC/O analysis from wines fermented using different yeast strains.  
Figure 2 displays the result of the PLS by excluding seven volatile compounds that were not statistically affected by the different yeasts used for the fermentation. The PLS loading plot did not show a strong separation or clustering, with some exceptions. Diethyl ester of butanedioic acid and 3-hydroxyethyl butanoate were clustered very closely, and they were fairly well separated from all other volatile compounds. These two volatiles were more associated to \"caramel\" and \"fruity\", as shown in the plot. On the other hand, compounds such as hexyl acetate, phenylethyl acetate, acetoin and 5-hydroxymethyl furfural were close to each other and correlated to the sensory attribute \"phenolic\". Regarding the yeast strains used, the control was associated more to this latter group, where attributes such as \"butter/cheese\" and \"floral\" are located.  
On the other hand, yeast strains D47 and QA23 were clustered closely, and more correlated with compounds such as furfural, acetic acid, 3-hydroxy-2-pyranone and others. Wines resulting from fermentation using VL1 or CY3079 were more correlated with \"nutty/roasted\", \"smoked\" and \"winey\" notes. Interestingly, the latter two attributes were clustered very closely. Several acids, alcohols and lactones were associated with these attributes and these yeast strains.  
Figure 1: Effect of different yeast strains on the aromatic series of white Fiano wine fermented in barriques. The aromatic series for each of the yeast strain is shown as compared to the control fermentation. The series used in this work groups the odor compounds with similar odor descriptors and reporting as value the sum of the AEDA values, expressed as log\({}_{10}\), detected by GC/O analysis (Table 2).
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 2 Research Status Worldwide  
At present, more than 10 natural gas hydrate test mining projects have been carried out in several countries, including China, and almost all of them have encountered the sand problem. The most serious ones (AT1-P, 2013, Nankai Sea, Japan) have even led to the early termination of test mining. The sand production problem has become one of the main problems affecting the long-term safe and efficient exploitation of hydrates [10] worldwide.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 4 Constellation Navigation and Orbit Determination Technology
### Multi-Constellation GNSS Lunar Satellite Orbit Determination Method
proposed a method based on the Kalman filter-RBF neural network to predict INS errors and compensate for them, thereby making up for the defect of GNSS lock loss and inability to navigate [66]. When the average position error is 3 m, accurate prediction can last for about 30 s.  
In order to solve the problems of poor fault tolerance and difficulty in real-time calculation of centralized combination methods, some scholars have conducted research on distributed combination methods. The representative distributed combination method is a solution method based on the federated Kalman filter [67]. This algorithm designs federated filters by treating each GNSS system as federation members to improve positioning accuracy. [PERSON] proposed a multi-constellation combination solution method based on the Kalman filter and adaptive joint filter [68]. This method has good tracking performance and improves the positioning accuracy and stability of the system. Joint positioning technology uses the federated Kalman filter algorithm to design a joint positioning system and locate a single point [69]. Compared with the Kalman filter algorithm, the error in straight line distance is reduced by 9.54%, and the error in elevation is reduced by 53.43%.