esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparation and Electron-Beam Surface Modification of Novel TiN Material for Medical Applications
## 4 Conclusions  
For the first time, new materials were obtained by depositing a porous TiNi powder onto monolithic TiNi plates followed by sintering at 1200 \({}^{\circ}\)C and further modification with high-current-pulsed electron beam (HCPEB) treatment. Analysis of the structure and composition of newly developed materials showed that the treatment caused structural changes and phase redistribution in the HCPEB-processed surface layer. Depending on the energy of applied electron beam, the modified top layer of the coatings was found to be of 5-20 \(\upmu\)m and enriched in Ti, while their overall roughness decreased up to 25%.  
As potential materials for medical applications (as implants), the prepared samples were then tested electrochemically during their storage in saline and after cell cultivation on one of them. Simulation of obtained impedance data resulted in a model with two layers (external and internal) which was valid for all the samples. The external layer consisted of the topmost surface of coating and its shallow pores, whereas the internal layer was assigned to deeper pores and the TiNi monolith sublayer. The tested cells, MCF-7, were found to grow first on the external layer, after which their growth was detected on the internal layer. The electrochemical studies also revealed increased stability of HCPEB-treated TiNi coatings against corrosion. The obtained results imply that the newly developed materials, consisting of TiNi porous coating on monolith TiNi plate and modified by HCPEB, can be promising for implant development.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11104372/s1](https://www.mdpi.com/article/10.3390/app11104372/s1)]([https://www.mdpi.com/article/10.3390/app11104372/s1](https://www.mdpi.com/article/10.3390/app11104372/s1)), Table S1: Values of CPE-T and R\({}_{\text{p}}\) parameters calculated from EIS data for materials before and after immersion in saline (inorganic fraction of DMEM) from 1 to 10 days; Table S2: Time components values from EIS data for all samples.  
Conceptualization, S.G.A., A.V.S. and V.N.H.; methodology, V.N.H., O.V.K. and S.A.K.; investigation, N.V.A., O.V.K., E.V.Y., V.A.V. and D.R.K.; resources, E.V.Y., V.A.V. and V.N.H.; writing--original draft preparation, A.V.S., S.A.K., N.V.A., D.R.K. and S.G.A.; writing--review and editing, A.V.S. and S.A.K.; project administration, S.G.A. and V.N.H.; funding acquisition, S.G.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, grant number 19-79-10045.  
Not applicable.  
Characterization was carried out using the equipment of the Tomsk Materials Science Shared Center of Tomsk State University.  
The authors declare no conflict of interest.
## 1 Introduction
Due to the limitations of optical systems and the need for detecting faint and weak star point targets, optical- or image-based IMC methods cannot be used. Therefore, a mechanical IMC method was designed to compensate for the image motion in long-integration space cameras using a two-axis pointing platform. The rest of this paper is structured as follows: Section 2 provides a detailed description of the mechanical design of the camera. Section 3 analyzes the image motion of the camera in orbit using homogeneous coordinate transformations, as well as calculates the required compensation angles for the two-axis pointing platform. At the same time, an analysis of the factors that affect the accuracy of IMC is also conducted. In Section 4, the simulation analysis demonstrates that images without IMC exhibit trailing effects after imaging, while images with IMC achieve ideal imaging results. Section 5 presents the imaging results obtained in orbit. Section 6 concludes the paper.
## 3 Results and Discussion
### Interrelation between AOA and MC  
Figure 4 shows the linear dependence AOA = f(MC), describing the interrelation between the AOA of an aqueous extract of coffee beans and MC in coffee beans. A high positive correlation (r = 0.89, \(p\)\(<\) 0.001) was also found between AOA and MC. These data indicate that MC in coffee beans is a predictive index of the yield of AOA in the extraction process. Treatment methods associated with an increase in the total moisture of coffee beans may enhance the release of antioxidant compounds in the extraction process.
## 3 Simulation System  
An overview of the UAV flight path optimization method proposed in this paper is shown in Figure 4. As shown in the figure, our simulation starts with the deployment of a selected outdoor environment to be evaluated by our numerical analysis. An initial trajectory of our UAV sensor is set. Based on the position of the UAV sensor and the ground-based training points, a ray-tracing simulation is conducted to emulate the propagation channels between the discrete training points and the UAV sensor. Since the target illegal transmitter's locations might be different from those of the discrete training points, a propagation modeling approach is applied to emulate the propagation channels between arbitrary possible target locations and the UAV sensor. In the estimation phase, a machine-learning-based pattern matching algorithm is applied to compare the RF fingerprint of the target illegal emitter against the constructed RF fingerprint database. (Indeed, the pattern matching process as shown in Figure 4 can be classified as \"supervised learning\", one of famous machine learning algorithms. In the first phase, a fingerprint database is constructed via the regression process explained in Section 3.2. In the second phase, the pattern matching process is conducted, i.e., the observed RF signals are compared with the constructed database to estimate the location of the illegal emitter. This paper employed the ML approach shown in Equation (3) for the estimation phase.) Based on this process, the location estimation error of a specific target location is computed. This process is repeated for all candidate locations of the target emitter to finally derive the estimation error distribution. Based on the derived distribution, our optimization algorithm is run to select another UAV flight path that helps to improve the estimation error distribution. Details on the functionality of each block will be explained as follows.  
Figure 3: Outdoor localization using UAV.
## 3 Upper-Bound Solution for Different Potential Failure Modes on a Clay Slope  
### Block Failure  
Figure 3 shows the potential block failure mode of clay due to the movement of a track system on a slope. The inclination of the slope is defined by \(\psi\), where the failure line is assumed to be parallel to the clay slope. In this failure mode, the movement of the clay block defined by \(\theta=\pi/2\) is described by the velocity \(\mathbf{V}\) that is parallel to the slope surface. Therefore, the total external power, that is, the rate of work, can be calculated as:  
\[\begin{array}{l}P=\mathbf{F}\cdot\mathbf{V}=[F_{x}+F_{y}\text{sin}\,\psi,-F_{y}\text {cos}\,\psi]\cdot[V,0]\\ P=\mathbf{F}\cdot\mathbf{V}=(F_{x}+F_{y}\text{sin}\,\psi)V,\end{array} \tag{2}\]  
where \(V\) is the magnitude of the velocity \(\mathbf{V}\). Considering the speed of the track system, the failure mechanism is governed by the undrained strength of the clay (\(c_{u}\)). Accordingly, the dissipation rate followed by the failure line can be expressed simply as:  
\[D=c_{u}LV. \tag{3}\]  
Figure 3: Block failure mode of a single-track system on clay slope. \(F_{x}\), \(F_{y}\), \(V\), and \(\psi\) denote the soil thrust, overburden force transmitted from the weight of the vehicle, velocity, and slope angle, respectively.  
Figure 2: Schematics of a soil block with a definition of its coordinate system, dimensions \((H,L)\), soil thrust \(F_{x}\), overburden force (\(F_{y}\)), and inclination angle of slope (\(\psi\)).  
Since the total external work is fully dissipated upon failure, i.e., \(P=D\), the soil thrust can be written as:  
\[F_{x}=c_{u}L-F_{v}\sin\psi, \tag{4}\]  
which yields a consistent soil thrust with [12] for the flat clay ground (\(\psi=0\)). It is noted that the soil thrust becomes zero when:  
\[F_{v}=\frac{c_{u}L}{\sin\psi}. \tag{5}\]  
To showcase the influence of \(F_{v}\) on \(F_{x}\), different values of \(F_{v}\) were selected, and the normalized soil thrust values \(F_{x}/c_{u}\) are depicted in Figure 4. Note that values of \(c_{u}\) and \(L\) of 10 kPa and 0.15 m were adopted as an example. Please refer to [12] for the background of the parameter selections. Different from flat clay ground, the soil thrust \(F_{x}\) was affected by \(F_{v}\), which was directly related to the weight of the vehicle when there exits any slope in the clay ground. In addition, the soil thrust tended to decrease as the slope became stiff under the given vehicle weight (\(F_{v}\)) and undrained shear strength of the clay \((c_{u})\). Although the shear resistance of clay along the failure line is a given condition regardless of a vehicle and ground inclination, the combination of the vehicle weight and the slope could diminish the soil thrust under the block failure mode.
# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 3 Materials and Methods
### Main Algorithm Procedures
```
1:procedureReceiveResult(\(u_{w},r\))
2:\(area\gets findArea(\ell_{r})\)
3:\(smallArea\gets findArea(\ell_{r})\)
4:\(property\gets getProperty(r)\)
5:if\(bloomFilter(area)\_membshipTest(smallArea+property)\)then
6:\(sendBF(bloomFilter(area),k_{s})\)
7:else
8:if\(resultsCounter(smallArea+property)<k\)then
9:\(resultsCounter(smallArea+property)++\)
10:\(results(smallArea+property).add(r)\)
11:else
12:\(bloomFilter(area).add(smallArea+property)\)
13:endif
14:endif
15:endprocedure
```  
**Algorithm 3** Arrival of a new result in the area covered by the ME host  
Algorithm 4 shows a procedure when the \(ME_{i}\) host receives a new task \(\tau\) submitted by the requester \(u_{r}\). First, \(ME_{i}\) finds a \(km\) MGRS area in which \(\tau\) is created and adds \(\tau\) in the set of active tasks in this area (lines 2-3). Then, it checks if any of the workers who are currently located in the area can answer to \(\tau\) (lines 4-5). If yes, \(ME_{i}\) sends a current BF for this area and assigns \(\tau\) to the worker \(u_{w}\) (lines 6-7). ```
1:procedureReceiveTask(\(u_{r},\tau\))
2:\(area\gets findArea(\ell_{\tau})\)
3:\(activeTasks(area).add(\tau)\)
4:for each\(i\) in\(workersIntArea(area)\)do
5:if\(\mu_{\tau}(a)==\top\)then
6:\(sendBF(bloomFilter(area),m)\)
7:\(assign(u_{w},\tau)\)
8:endif
9:endfor
10:endprocedure
```  
**Algorithm 4** Arrival of a new result in the area covered by the ME host  
As already stated, a classic BF structure does not support the deletion of an element. As typical MCS service is time-dependent, i.e., sensor data becomes obsolete during the time, we overcome this problem by periodically resetting the BF structure maintained by an ME host, as presented in Algorithm 5. The \(ME_{i}\) host periodically resets BF for its corresponding areas (lines 2-3) and finds potential workers in each area who will be activated upon receiving an empty filter (lines 4-6). ```
1:procedureBloomFilter reset(\(bloomFilter\))
2:for each\(area\) in \(bloomFilter.keySet()\)do
3:\(filterReset(bloomFilter(area))\)
4:for each\(i\) in\(workersIntArea(area)\)do
5:\(sendBF(bloomFilter(area),m)\)
6:endfor
7:endfor
8:endprocedure
```  
**Algorithm 5** Bloom filter reset on the ME host  
This section proposed a decentralized and edge-compliant architecture for MCS, which uses a well-known Bloom filter data structure to orchestrate autonomous data acquisition on mobile workers participating in MCS tasks. In the remainder of this paper, we extensively evaluate the proposed algorithm to determine if it can be used to reduce energy consumption in a distributed MCS environment.
## 3 Prototype Description
### Stability by Cluster  
Global stability allows for an analysis of the solutions with respect to their change according to several executions of a clustering algorithm, but does not allow for a detailed analysis of the specific structure of the solutions, i.e., the clusters [57]. After selecting three segmentation candidates (\(k=4,k=5\), and \(k=6\)), it is possible to calculate the cluster stability, which is similar to the previous method, but with a focus on clusters instead of entire solutions.  
This stability allows for the detection of unstable clusters within stable solutions and vice versa, helping later in descriptive analyses and selection of the solutions themselves, since it provides a view by cluster, facilitating the choice of a potential customer segment. The method uses bootstrapping and Jaccard's index to calculate stability.  
The Jaccard index (\(J\)) measures the similarity between two datasets A and B, considering the union and intersection of these sets, as expressed:  
\[J(A,B)=\ \frac{|A\cap B|}{|A\cup B|}=\ \frac{|A\cap B|}{|A|+|B|-|A\cap B|}. \tag{7}\]  
The upper part represents the intersection of \(A\) with \(B\), thus containing values common to both sets. The lower part represents the union of \(A\) with \(B\), containing all the values of \(A\) and all the values of \(B\), then subtracting the values common to both sets to avoid their duplication. The Jaccard index returns a value between 0 and 1, with 1 being a value that represents the similarity between the two sets, and 0 representing the total dissimilarity between the sets [58].  
Through [PERSON]'s index, each cluster belonging to the original solution with its bootstrap representation is compared, generating an index for each cluster. Running the algorithm results in 100 values in a range from 0 to 1 for each cluster, which can then be displayed in a boxplot. The horizontal axis of each graph represents the different clusters contained in a solution, while the vertical axis represents the value of [PERSON]'s index, allowing for the intuitive visualization of the stability of each cluster within a solution.  
As there are three candidates for the solution (\(k=4,k=5\), and \(k=6\)), the algorithm was applied to each one resulting in Figure 5, where it is possible to compare the solutions with respect to the stability of their clusters. It can be observed in the solutions with \(k=4\) and \(k=5\) that the last cluster has great instability, reaching Jaccard values close to 0.4.  
In the solution with \(k=6\), the stability of the last cluster varies with less intensity. The solution still presents an instability in the first cluster, indicating a possible division of a large and unstable cluster into two smaller and more stable clusters.  
Figure 4: Boxplot of ARI for each number of clusters (circles represent the outliers and the orange lines represent the mean of the results).
## 2 Methods
### GFCC/GPLP features  
Mel-Frequency Cepstral Coefficients (MFCCs) [27] and Perceptual Linear Prediction (PLP) coefficients [28] are well-established feature representations for human speech analysis and recognition tasks. Both of these are representations of the vocal tract spectrum incorporating perceptual models of the human auditory system. The MFCC approach warps the linear frequency axis to match the Mel-scale cochlear frequency map, while the PLP method uses critical band filters, equal-loudness curve amplitude transformation, and cube-root power to intensity transformation. The use of MFCC and PLP coefficients have several beneficial characteristics and have been shown to be robust and highly effective for characterizing human speech for tasks such as speech and speaker recognition. These features, however, are specific to the human auditory system and need to be modified for application across a wider variety of animal species.  
The features used in this work are a generalization of the MFCC and PLP features [29] based on the work of [PERSON] [30], who showed that many terrestrial and aquatic mammals have a logarithmic cochlear-frequency map. MFCCs have been generalized to Greenwood Function Cepstral Coefficients (GFCCs) by the use of the Greenwood frequency warping function. PLPs have been generalized to generalized perceptual linear prediction (gPLP) coefficients, constructed by substituting a species-specific frequency warping function and equal loudness curve. It has been shown [10,31] that these generalized features are effective for acoustic pattern classification across a wide range of species and applications. The basic calculation method for GFCCs is to place triangular filters over the Fourier Transform of each frame, spaced according to the Greenwood frequency warping function, and then take the filter bank energies and use a Discrete Cosine Transform (DCT) to convert the filter bank energies into cepstral coefficients. In the experiments presented here, a 12-dimensional GFCC vector is used as the base feature, to which signal log energy is appended, after which velocity and acceleration coefficients (referred to as delta and delta-delta coefficients in the speech community) are calculated for each of the 13 original features, yielding an overall 39-element feature vector for each frame. In addition, the feature vectors are normalized using Cepstral Mean Subtraction (CMS), a common technique which simply subtracts the mean value across each vocalization. This compensates for fixed convolutional noise effects like microphone channel variation.
## 3 Results and Discussions
### Physical Reasons of the Statistically-Significant Terms
The major difference between the diffusion of the solid carbon source and its gaseous counterpart is that in our method, the solid carbon source generates the carbon radical from beneath the Cu layer, and it diffuses through the Cu grain boundaries to stabilize on the surface as graphene. The thickness of the carbon layer becomes an important factor in such a case and influences the growth. In the gaseous carbon source, the carbon radicals are generated on the Cu surface, and there is no stress-enhanced generation of carbon radicals. On the other hand, for a thicker a-C layer, the thermo-mechanical stress is lower, and Reaction (3) is more dominant. Under such circumstances, the rise in temperature will cause more etching of the  
Figure 5: ANSYS simulation of thermo-mechanical stress (equivalent von-Mises stress) at the a-C and Cu interface at 1020 \({}^{\circ}\)C.  
Figure 6: ANSYS simulation results of maximum principal stress distributions in the three samples with different a-C layer thickness at 1020 \({}^{\circ}\)C. The area of the maximum stress distribution is the largest in the sample with the thinnest a-C layer. synthesized graphene, resulting in an increase in the defective sites in the graphene layer due to the etching, and causing the \(I_{\rm D}/I_{\rm C}\) ratio to rise. 2. The presence of the significant interaction term \"BC\" mentioned in Table 4 can again be explained by the stress-related growth mechanism mentioned above. As mentioned earlier, for a thin a-C layer, the formation of graphene is enhanced, and the annealing time required is short, and vice versa for a thicker a-C layer. If the annealing time is sufficiently long, the response of the graphene growth will be independent of the a-C layer, as by then, even the case of the thick a-C layer would have graphene grown already. When the annealing time becomes short, there will be very little or even no graphene grown for a thick a-C layer in contrast to the case of a thin a-C layer. Hence, we can see the interaction of the factors \"B\" and \"C\" in Figure 3, which is statistically significant, as well. The reasons for the statistical significance of the factors \"A\", i.e., the annealing temperature, and \"B\", i.e., the a-C layer thickness, as mentioned in Table 4, can be easily understood from the above explanation of the stress-related graphene growth mechanism and governing reactions shown in Figure 4. Factor \"B\" controls the amount of carbon supply, which is related to the variable stress at different levels of the factor \"B\", whereas the factor \"A\" influences the rates of Reaction (1), (2) and (3), as shown in Figure 4.
## 2 Study Area and Datasets
### Datasets  
Various types of remote sensing and ancillary data were used in this study, including high-resolution satellite data (HJ-1B images), low-resolution satellite data (Terra/MODIS images), Digital Earth data (digital elevation models or DEMs), land use classification data [3], and in situ data describing radiation, heat fluxes and meteorological measurements.
## 3 Results
### Real-World Application Results
#### 3.1.1 Evaluation Metric  
The performance of the different approaches is visualized in the top graph of Figure 6. It shows how the MAE evolved over the evaluation horizon. All the batch learning evaluation methods produced comparable results. Initially, the performance degraded slightly and then improved continuously. The OML approach comparatively achieved constant results and outperformed the batch evaluations over the entire horizon.
# A Key Management Scheme for Mobile Wireless Sensor Networks
## 3 Public-Key Cryptography with an Authentication Table Key Management Scheme (PCAT)
### Predeployment Phase  
Before deploying the WSN, the administrator of the network sets the parameters of PCAT (i.e., \(m\) and \(l_{pu}\)), extracts the random numbers (i.e., the \(m\) indexes in the masks), generates the cryptographic material (i.e., the public and secret keys), computes the authentication tables, and distributes to each node the proper data. Each node knows a public cryptosystem, a symmetric cryptosystem and a hash function. Moreover, each node \(i\) is set up with the following unique data:  
* a node identifier (ID\({}_{i}\)),
* a couple of public (Puk\({}_{i}\)) and secret (Prk\({}_{i}\)) keys,
* a bit mask (Mask\({}_{i}\)), corresponding to a list of \(m\) indexes of bits inside the public keys,
* an authentication table for the authentication of the other nodes.  
The authentication table stores the information required by a node to authenticate any other node of the network. Each row of the authentication table corresponds to another node of the WSN. For each row two entries are generated starting from the public key of the node corresponding to that row. The bits indexed by the mask are directly copied into the table. On the remaining bits, a hash function H(0) is executed and its result is stored in the same row.  
Figure 1 shows an example of the data stored by the nodes. In the example, the mask stores 4 values and the length of the public key is 16 bits. The \(m\) indexes in the mask of Node 0 are 15, 11, 7 and 3. Since the first index is 15, the first bit of each row is equal to the bit with index 15, i.e., the most significant one, in the corresponding Puk: 1 for row 1, 1 for row 2, and 0 for row 2. In the same manner, since the second index is 11, the second bit of each row is equal to the bit at index 11 in the corresponding Puk. The described method is used to fill in the first field of all the rows in each table.  
In the authentication table, the second field of each row corresponds to a hash function executed on the bits that are not included in the first field of the row. For example, considering row 0 of the  
Figure 1: Example of data stored by the nodes with total number of nodes (\(n\)) = 4, quantity of bits indexed by the mask (\(m\)) = 4, and length of the public key (\(l_{pu}\)) = 16.  
authentication table of Node 1, the bits corresponding to the indexes in the mask of Node 1 (15,12,9,6, in bold) are extracted from the 16 bits of \(\text{Puk}_{0}\) (\(\mathbf{0110}\)\(\mathbf{0110}\)\(\mathbf{0110}\)\(\mathbf{0110}\)), and stored in the first field of the row, while the result of a hash function computed on the other bits is stored in the second field of the row.