esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions
The presented analyses not only intend to better understand the physical processes behind the preparation phase of the medium-large earthquakes in the world but also to demonstrate the usefulness of a large satellite constellation to monitor the ionospheric geomagnetic activities and to investigate how long a seismo-induced ionospheric disturbance could be detected in the active seismic region before the event, which can be even several months before the mainshock. Finally, this type of analysis, together with lithospheric, geochemical and atmospheric data investigation, could possibly bring, in the near future, the capability to make reliable earthquake forecasting. Conceptualization, D.M. and A.D.S. ; Data curation, D.M. ; Formal analysis, D.M. ; Funding acquisition, A.D.S. ; Investigation, D.M. and A.D.S. ; Methodology, D.M., A.D.S., S.A.C. and D.S. ; Project administration, A.D.S. ; Software, D.M., S.A.C. and G.C. ; Supervision, A.D.S. and L.P.; Validation, S.A.C., M.S., A.P., D.S., G.C., L.P. and M.O. ; Visualization, D.M., S.A.C., M.S., D.S. and M.O. ; Writing--original draft, D.M. and A.D.S. ; Writing--review & editing, D.M., A.D.S., S.A.C., M.S., A.P., D.S., G.C. and M.O. All authors have read and agreed to the published version of the manuscript. This research was funded by European Space Agency in the framework of the ESA-funded project SAFE (\"_Swarm_ for Earthquake study\", Contract No. 4000116832/15/NL/MP), its extension e-SAFE (Contract No. 4000113862/15/NL/MP) and also by Italian Space Agency (ASI) with the project \"LIMADOU-Scienza\" (Contract No. 2016-16-H0) and ISSI-BJ project \"The electromagnetic data validation and scientific application research based on CSES satellite\". The APC was provided by FURTHER (\"The role of fluids in the preparatory phase of earthquakes in Southern Apennines\") project funded by INGV Earthquake Department. The authors are very grateful for the useful comments received by two anonymous reviewers that permitted us to improve the quality of our work and research investigation. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Haptic Glove TV Device for People with Visual Impairment
## 1 Introduction
Others even go beyond this and present wearable technologies where actuators can be embedded in the fabric to add haptic sensations into clothing for customizable user experiences, making them more real and interactive [28; 29]  
From the evaluation done on these products, we decided not to opt for one of these solutions but to build our glove with haptic feedback vibratory discs, delivering haptic-tactile vibratory stimuli wholly adapted to our objective. The system must be easy to learn and interpret and must not be invasive so that blind people like to use the device and give them an enjoyable and immersive user experience. We also want the system to be moderately priced and affordable for blind users. The major motivation for this work's development is undoubted to provide a technological solution that makes them feel that TV can be accessible and inclusive regardless of their visual impairment. In one of the interviews we had with blind people, one of them gave us a very simple but shivery challenge: when you go to a sporting event, blindfold yourself for a few minutes, and you will feel exactly what we feel. It is easy to infer that if they do not have a sighted person to narrate the scene taking place in a sporting event or any activity, the blind person is totally lost off the space. That also happens on TV when the content does not have sound with the right narrative. This simple exercise gave us the necessary push to work on this solution with great pleasure. The related works with haptic in immersive telecommunication and other interesting applications are detailed in Section 2. The proposed methodology for designing the haptic glove, the diagrams, and a general explanation of its operation are detailed in Section 3 of this work. Section 4 shows the development and implementation of the haptic glove. Section 5 shows how to achieve the integration between the glove and the TV. Section 6 shows the analysis of the results according to the evaluation with visually impaired people who used the glove. Finally, the discussion and the proposal for future work are included in Sections 7 and 8 respectively.
## 4 Experiments and Results
### Experiment Setup
#### 4.1.4 Training  
The proposed hybrid neural network includes a CNN to extract multiple time scale features, three LSTMs to learn time dependencies, and fully connected layers for higher-level feature abstraction. The CNN has two layers containing 1-d convolution, activation, and pooling operations. Convolution operations of two layers have 10 and 20 filters, respectively. Considering that the data we studied is a one-dimensional price series, 1-d convolution is sufficient here. The activation function is LeakyReLU. The pooling operation is max pooling with size 2 and stride 2. The number of LSTM units is 10, and the number of units in the subsequent fully connected layer is 10 and 1.  
Besides, the loss function is binary cross-entropy. An Adam optimizer is used to train the neural network. The learning rate is initialized to 0.001, and decays [30] with the iteration according to Equation (11).  
\[lr=lr*\frac{1}{1+I*d} \tag{11}\]  
where \(lr\) represents the learning rate, \(I\) describes the current iteration, and \(d\) represents the attenuation coefficient which takes a value of \(10^{-6}\). The value of the batch size is 80. We use early stopping to prevent the network from overfitting. That is, training will stop if the accuracy on the validation set has not been improved for 70 epochs. In addition, we use dropout [31] to control the capacity of neural networks to prevent overfitting, and use batchnormalization [32] after convolution operation to reduce internal covariate shift for the better performance of the network.
# Recent Advances in Evolving Computing Paradigms: Cloud, Edge, and Fog Technologies
## 4 Challenges and Opportunities
### Future Prospects of Fog/Edge Computing
#### 4.2.5 Software-Defined Networking  
Software-Defined Networking is an upcoming paradigm that overcomes the vertical integration issue by separating the control logic of the network from the underlying switches and routers, enabling a logical network control centralization [151]. It makes it simpler to manage a flexible and reliable network, introduces new networking abstractions, and leads to network evolution. SDN overcomes conventional network issues by enhancing the virtualization, security, energy efficiency, and network reliability, optimizing the network topology, managing complexity, service orchestration-benefitting fog, and edge computing [152; 153].
## 4 Results
### Cost Analysis
#### 4.2.7 Scenario 7  
The S7 or OFC-RECT scenario considered off-site construction of rectangular house modules--also known as prefabrication. This scenario was important since the efficiency of this method for conventional house designs is well known. However, the numbers relating to the costs for this scenario were collected from the end customer and therefore included a profit margin. The cost data were obtained from several Finnish concrete element factories. External walls cost EUR 140 per square meter, and internal walls cost EUR 90 per square meter. Thus, these numbers were only used for the purpose of rough comparison with the other scenarios, which excluded the profit margins. If we conductedan estimate by excluding a 30% profit margin from S7, the cost was around EUR 9000, making it comparable to conventional scenarios S3 and S6.  
The most significant cost component was the concrete element cost. The labor cost for S7 was naturally lower than for S3 and S6, since prefabrication is less labor-intensive than conventional construction methods. The crane used to lift and move the components to be assembled to the assembly location comprised 6.3% of total costs as a machinery cost. The daily cost of renting a truck and its driver was taken as EUR 500 for all the off-site scenarios. The transportation cost was about 4% of the total cost in this scenario. For more detail refer to Table 10.
###### Abstract  
Million cubic meters from abandoned mines worldwide could be used as subsurface reservoirs for large scale energy storage systems, such as adiabatic compressed air energy storage (A-CAES). In this paper, analytical and three-dimensional CFD numerical models have been conducted to analyze the thermodynamic performance of the A-CAES reservoirs in abandoned mines during air charging and discharging processes. Unlike other research works, in which the heat transfer coefficient is considered constant during the operation time, in the present investigation a correlation based on both unsteady Reynolds and Rayleigh numbers is employed for the heat transfer coefficient in this type of application. A tunnel with a 35 cm thick concrete lining, 200 m\({}^{3}\) of useful volume and typical operating pressures from 5 to 8 MPa were considered. Fiber-reinforced plastic (FRP) and steel were employed as sealing layers in the simulations around the fluid. Finally, the model also considers a 2.5 m thick sandstone rock mass around the concrete lining. The results obtained show significant heat flux between the pressurized air and the sealing layer and between the sealing layer and concrete lining. However, no temperature fluctuation was observed in the rock mass. The air temperature fluctuations are reduced when steel sealing layer is employed. The thermal energy balance through the sealing layer for 30 cycles, considering air mass flow rates of 0.22 kg s\({}^{-1}\) (charge) and \(-\)0.45 kg s\({}^{-1}\) (discharge), reached 1056 and 907 kWh for FRP and steel, respectively. In general, good agreements between analytical and numerical simulations were obtained.  
abandoned mines; underground reservoirs; energy storage; renewable energy; CAES; analytical modelling; numerical modelling; sealing layer +
Footnote †: journal: Applied Sciences
# Computational Test for Conditional Independence
## 4 Results
### Testing with One p-Value  
In the heat and overlay plots in Figure 6a, we see the type I error rate for 100 tests for each sample size using the seven proposed simulations. The computational testing procedure exhibits fairly good type I error across different data-generating functions, especially if one were to compare against alternative methods, shown in the heat plots in Figure 6b,c. Data simulated from the TrigData seems like the simulation scenario where it is most difficult to control the error rate. Overall, Kappa scores seem more stable in type I error control.  
Figure 6: Type I error rates for different testing methods. A bluish color means the error rate is low and should ideally be around 0.05; purple, orange, yellow, and red indicate a high type I error rate. The CCI test exhibits the best type I error control across simulation scenarios. (**a**) CCI testing using log loss (left) and Kappa scores (right). (**b**) Multinominal regression and GCM. (**c**) KCI.  
In Figure 7a, we see the estimated power of the computational testing procedure. The power is calculated as the proportion of \(p\)-values less than 0.05 when testing \(Y\perp\!\!\!\perp X|Z_{2}\). The power increases with the sample size, and using log loss seems to give more power than Kappa scores. The possibility for low power with small samples emphasizes that computational testing is a \"large\" sample method due to its reliance on ML classification methodology.  
Figure 7: Heatmaps of power (null: \(Y\perp X|Z_{2}\)). More red means higher power, and a power of 1.00, means a false null hypothesis is always rejected. The CCI test demands a relatively large sample size to reject a false null consistently, using only one \(p\)-value. Ideally, a test needs to achieve at least 80% power. (**a**) Power over simulation scenarios for CCI test using log loss (left) and Kappa scores (right). (**b**) Multinominal regression and GCM. (**c**) KCI.  
The other methods, multinominal regression, GCM test (Figure 7b), and KCI test (Figure 7c), have high power, but testing CI with categorical variables with these methods can be very misleading as the type I error rate can be as high as 100%.  
In Figure 8a,b, we have averaged the type I error and power over all simulations, which leads us to conclude that the proposed computational testing is a viable alternative to existing methods.
# Efficient Health Data Transmission Method in a Wireless Body Area Network for Rural Elderly
###### Abstract  
The wireless body area network (WBAN) can monitor human physiological information for a long time and alleviate problems such as the imbalance of medical resources distribution and population aging. The complex human media may make data transmission unstable in wireless body area networks. A delay-tolerant WBAN opportunity transmission method based on human data priority was studied. In this method, the historical states between links are recorded by a sliding window. Then, the link stability is measured by a prediction function. At the same time, considering the heterogeneity of the data in the body area network, transmission priority is assigned to different health data. Combined with residual energy, link stability, and distance, the transmission cost of the candidate set is calculated, and the node with the lowest expected transmission cost is selected as the next hop to improve the reliability of data transmission. The experimental results showed that this method includes the characteristics of opportunity routing and WBAN to transmit health data with lower delay and higher efficiency than previous methods.  
wireless body area networks; delay-tolerant; sliding time window; opportunistic routing Article
## 2 Data
### Satellite Data
#### 2.1.2 MODIS Products  
The MOD09Q1, MOD11A2 collection 6 (V006) products generated from the Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery are freely available from the Land Processes Distributed Active Archive Center (LP DAAC) of the U.S. Geological Survey ([[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)) accessed on 20 November 2021). The acquired products covered Poland and the period 2000-2019. The MOD09Q1 product contains 8-day composites of land surface spectral reflectance acquired in 620-670-nm (RED) and 841-876-nm (NIR) channels at a 250-m resolution, which were used to calculate NDVI. Information on the presence of clouds, cloud shadows, and snow was derived from the associated quality flags. The MOD11A2 product provides 8-day composites of daytime and night-time LST and emissivity calculated from the 11.03 \(\upmu\)m and 12.02 \(\upmu\)m channels at a spatial resolution of 1 km. The NDVI and LST MODIS products were mosaicked and reprojected to the Poland CS92 coordinate system (EPSG: 2180).  
These MODIS products were chosen for a backward prolongation of Sentinel-3 products due to a similar processing stage and spatial resolution. They serve as a long term average for calculation of anomaly-derived indices from Sentinel-3. With this in mind, the data can be at a certain level of generality, as only the average course of the product along the vegetation season is needed. For this reason, it was decided to use 8-day composites, because in the aggregation process the observations with the highest quality are selected taking into account cloud contamination and the sun zenith angle [28].
# Coupling Secret Sharing with Decentralized Server-Aided Encryption in Encrypted Deduplication
## 5 Evaluation
### Setup  
**Testbed:** We conduct all experiments on Alibaba Cloud with a ecs.g7.xlarge instance as the client and four ecs.g7.xlarge instances as the servers. Each instance is equipped with four vCPUs and 16 GB RAM. All instances are connected via a 10 Gbps network.  
**Datasets:** We use four well-known deduplication datasets for evaluation, as shown in Table 1. These datasets represent various typical workloads, including website snapshots, tarred source-code files, and database snapshots.  
**Configurations:** In our evaluation, we compare four encryption schemes: (i) centralized server-aided encryption based on MetaDedup [3], which has only a centralized key server to handle the key generation requests from clients; (ii) traditional decentralized server-aided encryption based on GDH-Dedup [8], which has multiple key servers, and each key server handles a portion of the key generation requests from clients; and (iii) our ECDedup with two different matrix generation schemes in Section 3. Since our ECDedup only changes the encryption scheme on the client side, we set the same number of 4 servers and 4 key servers in the experiments. For the parameter of erasure coding, we set \((n,k)=(4,3)\), as in the state-of-the-art centralized server-aided encryption schemes [2; 3; 4; 14].
# SIS Epidemic Propagation on Scale-Free Hypernetwork
## 2 Theoretical Backgrounds  
### The concept of Hypergraph and Hypernetwork  
In graph theory, a graph consists of numbers of given nodes and an edge connecting the two nodes, which is usually used to describe a specific relationship between things, where a node represents a thing, and an edge connecting two nodes represent corresponding specific relationships. A hypergraph is a generalization of a graph. Each edge in an ordinary graph can only connect two nodes, but a hyperedge in a hypergraph can connect two or more nodes.  
The concept of hypergraph was first proposed by [PERSON] in 1970 and defined as follows: Let \(V=\{v_{1},v_{2},\cdots,v_{n}\}\), then \(E=\{e_{1},e_{2},\cdots,e_{m}\}\) are all finite sets, if \(e_{i}\
eq\phi(i=1,2,\cdots,m)\) and \(\mathrm{U}_{i=1}^{m}\ e_{i}=V\), this is called binary relation \(H=(V,E)\), which is a hypergraph [25], where \(V\) is the set of vertices of the hypergraph \(H\), and \(E\) is the set of all hyperedges in the hypergraph \(H\). The elements \(v_{1},v_{2},\cdots,v_{n}\) in \(V\) are called nodes of the hypergraph, and the elements \(e_{i}=\Big{\{}v_{i_{1}},v_{i_{2}},\cdots,v_{i_{i}}\Big{\}}(i=1,2,\cdots,m)(1 \leq j\leq n)\) in \(E\) are called the hyperedges of the hypergraph. While \(|e_{i}|\) represents the cardinality of the hyperedge \(e_{i}\), namely, the number of nodes contained in the hyperedge \(e_{i}\). If the cardinalities of all hyperedges are equal, the hypergraph \(H=(V,E)\) is called a uniform hypergraph, otherwise it is called a non-uniform hypergraph. If \(|e_{i}|=2(i=1,2,\cdots,m)\), the hypergraph \(H=(V,E)\) degenerates into an ordinary graph. The hyperdegree of a node \(v_{i}\) is defined as the number of hyperedges that contain the node, denoted as \(d_{H}(v_{i})\), sometimes referred to as degree. The hyperdegree distribution \(P(d_{H})\) of nodes in a hypergraph \(H\) is the proportion of nodes with hyperdegree \(d_{H}\) to the total nodes, namely, \(P(d_{H})=N_{d_{H}}/N\), where \(N_{d_{H}}\) represents the number of nodes whose degree is \(d_{H}(v_{i})\). The average hyperdegreerefers to the average hyperdegree of all nodes in the hypergraph, denoted as \(\langle d_{H}\rangle\), namely, \(\langle d_{H}\rangle=\frac{1}{N}\sum_{i=1}^{N}\ d_{H}(v_{i})\). Figure 1 below is an example of a hypergraph, the nodes with hyperdegree 1 are \(v_{1},v_{2},v_{4},v_{5},v_{7},v_{8}\), and the nodes with hyperdegree 2 are \(v_{3},v_{6}\).  
[PERSON] et al. [14] believed that a network that can be represented by a hypergraph is a hypernetwork. The hypernetwork in this paper is a hypernetwork with a hypergraph as the underlying topology. Its mathematical definition is as follows: Let \(\Omega=(V,E)\) be a finite hypergraph, \(G\) is the mapping from \([0,\infty)\) to \(\Omega\), then for a given \(t\geqslant 0\), \(G(t)=(V(t),E(t))\) is a finite hypergraph. Namely, a hypernetwork \(G(t)\) is a collection of hypergraphs that evolve over time \(t\).
# In Situ Experimental Study of the Friction of Sea Ice and Steel on Sea Ice
## 2 Materials and Methods
### Test Parameters
#### 2.4.2 Test Parameters  
The influences of normal stress, sliding velocity, contact materials, and sliding direction with respect to ice columnar grain orientation on the kinetic friction coefficient \(\mu_{\mathrm{k}}\) were studied.  
The normal stress ranged from 3.10 to 6.56 kPa for steel-ice contact and from 5.34 to 8.11 kPa for ice-ice contact. Assuming that the velocity of an icebreaker would not be small during navigation, the minimum sliding velocity was set to 0.02 m\(\cdot\)s\({}^{-1}\), and the velocity range was extended to 0.02-2.97 m\(\cdot\)s\({}^{-1}\) to cover the whole range during the icebreaker ramming event shown in Figure 2. In each test, the sliding velocity was constant. The contact materials against sea ice were SS, CS, and sea ice to model the possible objects encountered during ice navigation. Because granular grains are isotropic and columnar grains are anisotropic, the effect of ice columnar grain orientation was investigated by measuring \(\mu_{\mathrm{k}}\) in sliding directions vertical and parallel to ice columnar grain orientation. The sliding configuration in which the top surface of the ice slider slid on the Lateral// track is denoted  
Figure 2: Distributions of (**a**) along-track velocity and (**b**) vertical-track velocity of the icebreaker during the ramming process compared to normal distributions (solid lines). Data are from the shipboard recorded velocity during the Antarctic expedition of the research vessel _Polarstern_ in 2006.  
as Top-Lateral//. Especially for ice-ice cases, friction could occur between any two surfaces. Therefore, there were in total ten types of sliding configurations for ice-ice friction, as shown in Figure 3. The tests were repeated three times under each test condition. During the day, tests were usually conducted in the morning when the air temperature did not rise largely and terminated when air temperature rose above melting point. Therefore, the temperatures of ice slider and ice track surface were believed to be same as air throughout the test duration.