esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Equipment
#### 2.2.3 Vacuum Infiltration  
The vacuum infiltration sintering process is meticulously conducted within a vacuum infiltration furnace, which is specifically designed to offer a controlled environment for the infiltration of liquid copper into the tungsten skeleton.  
The vacuum infiltration furnace is equipped with advanced technology that maintains a vacuum negative pressure environment of 10\({}^{-2}\) Pa. This high vacuum ensures that there are minimal impurities and gases present during the infiltration process, which can potentially interfere with the penetration of the liquid copper.  
Moreover, the furnace is capable of reaching temperatures exceeding 1500 \({}^{\circ}\)C, providing the necessary thermal energy for the infiltration process. The high temperature environment promotes the flow and wetting behavior of the liquid copper, enabling it to penetrate deeply into the pores and interstices of the tungsten skeleton. To further ensure the effectiveness of the infiltration process, the vacuum infiltration furnace employs a gradient heating program to gradually increases the temperature, which allows the liquid copper to infiltrate the tungsten skeleton uniformly and without causing any thermal stress or damage.  
By combining the high vacuum controlled heating program and the liquid copper's wetting properties, the vacuum infiltration sintering process ensures that the liquid copper fully penetrates into the depth of the tungsten skeleton. This not only enhances the compactness of the material, but also improves its mechanical strength, electrical conductivity, and thermal stability, making it suitable for a wide range of applications.
## 4 Case Study
### Voltage Profiles  
In the previous sections, the analysis has focused on each step of the proposed method. In this final section, the performance of the _Decentralized APP_ solution is analyzed in absolute terms. To this aim, the voltage profiles of the feeders and subfeeders are reported and compared with the ones obtained by the _Benchmark VOP_ and the one obtained without any optimization, referred to as _no VOP_ that is obtained by solving the DistFlow problem with fixed active powers and null reactive powers injected by DERs. Figures 15-17 show the network voltage profiles respectively for _Case 1_, _Case 2_, and _Case 3_, also including the HV busbar (slack node) and the MV substation busbar. It is evident how the profiles obtained by the _Decentralized APP_ are closer to the _Benchmark VOP_ ones than the ones obtained by _no VOP_, in all the considered cases. These results show that, although the proposed algorithm does not guarantee the same profiles as the reference centralized VOP, it represents a significant improvement with respect to the absence of optimization. At the same time, it is worth mentioning that, compared with the reference centralized VOP, the proposed _Decentralized APP_ requires a very limited number of voltage measurements and a significant reduction of communication requirements.
## 3 Results
### Allan Variance  
The stability of the instrument system is of vital importance. To test the stability of the system, we implemented continuous measurement for a fixed concentration of chloroform gas for 4000 s. During this experiment, 104 ppm chloroform gas was obtained through setting the flow rates of the pure nitrogen path and 625 ppm chloroform path at a ratio of 5:1. The target gas was continuously flowing through the PA cell at a total flow rate of 0.3 L/min, and the PA cell was always kept at room temperature (about 20 \({}^{\circ}\)C) and atmospheric pressure. The system got a data point of chloroform gas concentration every 20 s, so a total of 200 data points were obtained in 4000 s. As shown in the upper left corner of Figure 7, the collected concentration data fluctuated around 104 ppm (from 102 ppm to 105 ppm), and the measurement error was limited within \(\pm\) 2%. Allan-Werle variance analysis is widely used to evaluate the sensitivity of a gas detection system. By calculating the Allan-Werle variance of the measured data, we found the theoretical detection sensitivity under different averaging times. We achieved a sensitivity of 0.28 ppm with a time period of 20 s (no averaging) and a sensitivity of 10 ppb with an averaging time of 500 s, respectively. This means that our system has the ability to accurately detect low concentrations of chloroform gas.
## 3 Inertia Weight PSO Algorithm  
Let us assume that a population is composed by \(m\) particles in \(D\) dimension space and a particle is viewed as a point in the \(D\) dimension space, representing a solution described by its position and speed. The position of the \(i\)th particle can be expressed as \(X_{i}=[X_{i1},X_{i2},\cdots,X_{iD}]\), \(i=1,2,\cdots,m\). The speed can be expressed as \(V_{i}=[V_{i1},V_{i2},\cdots,V_{iD}]\). The optimal position of \(i\)th particle can be expressed as \(P_{i}=[P_{i1},P_{i2},\cdots,P_{iD}]\). The optimal position of the entire population can be expressed as \(P_{g}=[P_{g1},P_{g2},\cdots,P_{gD}]\). For the \(t\)th iteration, the position and speed of each particle can be updated by Equation (9):  
\[\left\{\begin{array}{l}V_{id}^{t+1}=\omega^{t}\cdot V_{id}^{t}+c_{1}\cdot rand \cdot(P_{id}^{t}-X_{id}^{t})+c_{2}\cdot rand\cdot(P_{gd}^{t}-X_{id}^{t})\\ X_{id}^{t+1}=X_{id}^{t}+V_{id}^{t+1},\;\;d=1,2,\cdots,D\end{array}\right. \tag{9}\]  
Equation (9) is so-called as the basic PSO, where \(t\) is the number of iterations \(\omega\) is the inertia weight coefficient, \(c_{1}\) and \(c_{2}\) are the acceleration constant, the values are generally selected as \(c_{1}=1.5,c_{2}=1.7\), \(rand\) is the random number falling within a normal distribution in [0, 1], \(V_{id}\) is the current speed of \(i\)th particle and \(V_{id}\in[-V_{\max},V_{\max}]\), where \(V_{\max}\) is the maximum speed limit. When \(V_{id}>V_{\max}\), taking \(V_{id}=V_{\max}\); when \(V_{id}<-V_{\max}\), taking \(V_{id}=-V_{\max}\). The iteration termination criteria of the PSO algorithm is usually given by that the number of iterations reaching to the maximum limit or the tolerance that is less than a specified required value.  
The global and local search ability of the particle can be adjusted by changing the size of \(\omega\). The larger the \(\omega\) value, the stronger the global search ability. On the other hand, it has a stronger local search ability, as its dynamic weight has a better optimization ability than a fixed one. Therefore, the linear decrease dynamic inertia weight strategy is employed. Its formula is given by the Equation (10):  
\[\omega^{t}=\omega_{\max}-\frac{\omega_{\max}-\omega_{\min}}{t_{\max}}\times t \tag{10}\]  
where \(\omega_{\max}\) and \(\omega_{\min}\) are the maximum and minimum values respectively, generally \(\omega_{\max}=0.9\), \(\omega_{\min}=0.4\). Inertia weight \(\omega\) will increase with the number of iterations, as indicated in Equation (10) from the initial value \(\omega_{\max}\) gradient to \(\omega_{\min}\) linearly; \(t\) and \(t_{\max}\) are the current iteration number and the maximum number of iterations.
## 4 Results and Discussion
### System Stress Test  
By using Jmeter 3.1 to run the stress test for the system, we set 50, 100, 200, 400, 800, and 1000 users respectively to test the system. Through HTTP requests, it can test the various aspects performance of system, e.g., database I/O rate, waveform extraction rate, template-matching rate. Each user runs for the five iterations. The configuration parameters of test environment are shown in Table 5, and the test results are shown in Table 6.  
Figure 14: The examples of observed waveform for classification.  
Multiple threads are used to simulate the concurrent connections of users in Jmeter. i.e., the number of threads represents number of users. In general, the 90% response time of users is longer than 2 s when the number of concurrent users is 400, the user experience is bad. Especially when the number of users is up to 800 and 1000, the most response time is longer than 10 s. Taking into account all indices, i.e., the average response time, the median response time, 90% Line, when the number of concurrent users is approximately 100, the user has a good experience. However, when the number of concurrent is 200 or 400, the user experience becomes bad, some requests will have a high delay. When the number of concurrent is up to 800, the request response time becomes longer, and performance is degradative. By analyzing the single HTTP request, it is found that the request time for database read, waveform extraction, and template matching has a significant increase relative to other requests when the load increases. To improve the performance of the system in the future, waveform extraction algorithm and template matching algorithm can be optimized to reduce the time complexity of the algorithm and the query in the database can be speeded up by using the extended index. Moreover, the technology of load balancing and increasing the number of servers will reduce the pressure on the single server. The database can also use read/write splitting technology for performance improvement.
## 4 Experimental Evaluations and Results
### Focus Value: Quantifying the Desired Properties  
To measure the fulfilment of the generated explanation with the desired properties as calculated by the focus value formula Equation (8), the user must set a relevance table (\(M_{ic}\)). In this experiment, the configured relevance table is presented in Table 3 with the rationale explained in the following sentences. The RSRP, SINR, and throughput reward component are highly correlated with the RSRP, SINR, and throughput input features, respectively. The tilt input feature also has a high correlation because it directly affects the antenna performance in all aspects. However, RSRP, SINR, and throughput are also correlated with each other in a moderate manner. Therefore, we set the correlation value at 0.5 since we want each reward component to focus on its most correlated input feature. The relevance table can be set by the user as she/he formulates the desired properties, and the rationale of the given example (Table 3) is explained above.  
The generated explanations (Figures 5 and 11) are evaluated with the relevance table (Table 3) to generate the focus values shown in Table 4. The focus values for both experiments are relatively similar because adjusting the weight does not affect the proportion of the input features contribution to each reward component. The RSRP reward component has the highest focus value because the RSRP input feature has a significant contribution compared to other features. On the other hand, the SINR reward component has the  
Figure 12: The average network metrics from fifty episodes showing the performance of the agent with original (blue) and adjusted reward weights (orange). (**a**) The RSRP signal on the evaluation phase. (**b**) The SINR signal on the evaluation phase. (**c**) The throughput signal on the evaluation phase. (**d**) The total reward on the evaluation phase.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input** & \multicolumn{3}{c}{**Reward Component**} \\ \hline
**Feature** & **RSRP** & **SINR** & **Throughput** \\ \hline Tilt & 1.0 & 1.0 & 1.0 \\ RSRP & **1.0** & 0.5 & 0.5 \\ SINR & 0.5 & **1.0** & 0.5 \\ Throughput & 0.5 & 0.5 & **1.0** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Relevance table configured by the user quantifying the desired properties.  
lowest focus value because it has significant contributions from the tilt and throughput input features.  
The weighted mean has a higher value than the unweighted mean focus value, which means that reward prioritization mitigates unwanted behavior because components with high focus values have a higher priority. On the other hand, the unweighted focus value is lower, indicating that it has less desired behavior if we treat all components equally (i.e., do not apply prioritization).
# An Approach to Analyze Vulnerability of Information Flow in Software Architecture
## 3 Proposed Approach
### Construction Approach of Service Invocation Diagram Based on Software Architecture
#### 3.4.1 Determination and Representation of Service Invocation Elements
It can be denoted by a tuple, \(A=\{r,w\}\), in which \(r\) represents reading operation and \(w\) represents writing operation._  
**Definition 10**. : _Direction of data flow between services. Direction of data flow between services refers to the direction of data flow when reading and writing operation occur during service invocation, which can be denoted by the symbol \"\(\rightarrow\)\"._  
For example, if there exist data D\({}_{i}\in\) S\({}_{s}\), D\({}_{j}\in\) S\({}_{o}\), and a set of service invocation I = {S\({}_{s}\),S\({}_{o}\),r}, then this means invoking service S\({}_{s}\) performs the reading operation on invoked service So. The direction of data flow can be represented by S\({}_{o}\rightarrow\) S\({}_{s}\), which means that when an invoking service performs the reading operation on an invoked service, the data flow from the invoked service to the invoking service. Similarly, if there exists data D\({}_{i}\in\) S\({}_{s}\), D\({}_{j}\in\) S\({}_{o}\), and a set of service invocation I = {S\({}_{s}\),S\({}_{o}\),w}, this means invoking service S\({}_{s}\) performs the writing operation on invoked service So. The direction of data flow can be represented by S\({}_{s}\rightarrow\) So, which means that when an invoking service performs the writing operation on an invoked service, the data flow from the invoking service into the invoked service. In addition to performing the reading and writing operations separately, an operation sequence of service invocation can perform both operations concurrently. In this case, the direction of data flow can be explained as follows: If there exists data D\({}_{i}\in\) S\({}_{s}\), D\({}_{j}\in\) S\({}_{o}\) and a set of service invocation I = {S\({}_{s}\),S\({}_{o}\),r and w}, it means the invoking service S\({}_{s}\) performs both the reading and writing operations  
Figure 5: Types of service invocation. on the invoked service \(\mathsf{S_{0}}\). The direction of data flow can be represented by \(\mathsf{S_{0}}\rightarrow\mathsf{S_{s}}\) and \(\mathsf{S_{s}}\rightarrow\mathsf{S_{0}}\), which means that when an invoking service performs the reading and writing operations concurrently on an invoked service, data flows from the invoked service to the invoking service and from the invoking service into the invoked service at the same time.
## 1 Introduction
Next, a class-conditional MS image radiometric enhancement of TOARF into SURF values corrected for atmospheric, adjacency and topographic effects is accomplished in sequence, stratified by the same SCM product generated at first stage from TOARF values, see Figure 11.  
As the fourth original contribution of this review section, it is worth recalling here that TOARF, SURF and surface albedo values, estimated as intermediate or final data products by an ESA EO Level 2 product generator and whose physical domain of change is 0.0-1.0, can be rescaled to the discrete and finite range {0, 255} according to an unsigned char data coding expression, e.g., byte(SURF/255. + 0.5) where operator byte() truncates any decimal part of a number whose data type is float. If byte coded into range {0, 255}, then TOARF, SURF and surface albedo values, whose physical range is 0.0-1.0, are affected by a quantization (discretization) error equal to (Max \(-\) Min)/number of bins/2 (due to rounding to the closest integer, either above or below) = \((1.0\,-\,0. )/255/2.=0.002=0.2\%\), to be considered negligible. It means that, in addition to providing DNs with a physical unit of radiometric measure, where DNs in EO Level 0 up to Level 2 imagery are typically coded as 16-bit unsigned short integer [139; 140; 141; 142; 143; 163], radiometric _Cal_ of DNs into TOARF, SURF and surface albedo values at either EO Level 1 or Level 2 imagery allows pixel coding as 8-bit unsigned char, with a 50% save in memory storage at the cost of a 0.2% quantization error. This observation, though straightforward, is neither obvious nor trivial. In practice, for example, the Planet Surface Reflectance (SR) Product [163] and the U.S. Landsat ARD format [139; 140; 141; 142; 143], coded as a 16-bit unsigned short integer, can be transcoded into an 8-bit unsigned char, affected by a quantization error as low as 0.2%, with a 50% save in memory storage. For comparison purposes, it is worth recalling here that when per-image metadata files of radiometric parameters (e.g., gain, offset, acquisition time, etc.) are available to transform, first, DNs into top-of-atmosphere radiance (TOARD) values, based on a band-specific gain and offset metadata parameter pair, with TOARD values \(\geq\) 0, and, next, to transform TOARD values into TOARF values belonging to range 0.0-1.0, it is well known that a typical approximation of the sun-Earth distance to 1 independent of the image-specific acquisition time typically causes TOARF estimation errors of about 3-5% [67; 143]. Despite common practice, where the sun-Earth distance estimation is oversighted by large portions of the RS community, in [143] a community-agreed standardization of the sun-Earth distance estimation in radiometric _Cal_ methodologies was recommended for improved harmonization/interoperability of multi-sensor multi-temporal EO _big data cubes_.
## 5 Case Study
### Experimental Results and Analysis
#### 5.2.2 Assembly Cost Analysis  
A truss hinge's assembly cost is largely affected by the tolerance of the more rigorous direction. Figure 17 shows each hinge's minimum tolerance during the assembly process, and the axis \"Hinges\" represents the LSDM's hinge, while the axis \"Assembly process\" represents the assembly steps that are expressed by the process number. The tolerances are allocated to all the hinges at each step. Figure 17 shows the tolerance change as the assembly progresses.  
Figure 18 shows each hinge's tolerances allocated by the dynamic allocation method and the previous method. More than half of the hinges have looser tolerances allocated by the proposed method than the previous method. This indirectly shows that the dynamic tolerance allocation can reduce the assembly difficulty and cost.  
Figure 16: Comparison of pointing accuracy under different assembly methods.  
Figure 17: Dynamic allocation of hinge minimum tolerance.  
Figure 18: The tolerance allocation results of different methods.  
Figure 19 shows the LSDM's assembly cost when assembled by different method. The values in the figure are calculated using the quintic cost function in Equation (16) and the tolerances allocated in each assembly process. The horizontal axis represents the assembly process expressed by the process number. The vertical axis represents the cost calculated from the tolerance allocated to each hinge. Each process calculates the cost of all hinges of the LSDM. The bar of each process represents the total cost of the corresponding tolerance scheme, and the short bars with different colors mean the cost of different hinges. For the dynamic tolerance allocation method, the unassembled hinge's tolerance can be changed during assembly. Thus, the length of the corresponding short bar changes with the assembly process. The assembled hinge's tolerance will not be reallocated. Thus, the corresponding short bar remains unchanged. The total cost of the first process (Process 1) represents the cost under initial tolerance of the proposed method, and it is the same as the tolerance allocated by the previous method, which takes place before assembly and remains unchanged during assembly. As assembly progresses, the tolerances are continuously optimized by the proposed method, and the calculated assembly costs change accordingly. When the last process (Process 9) is executed, all tolerances have been optimized, and they are actual execution values, so the corresponding assembly costs can be viewed as actual execution costs of the proposed method. Comparing these, the assembly cost can be reduced from CNY 547,232 to CNY 473,380 using the dynamic tolerance allocation method, which represents a cost reduction rate of 13.5%.
## 5 Conclusions  
The multiwavelength absolute phase retrieval from noisy intensity observations is considered. The system is lensless with multiwavelength coherent input light beams and random phase masks applied for wavefront modulation. The light beams are formed by light sources radiating all wavelengths simultaneously and CMOS sensor equipped with a color filter array (CFA) is used for spectral measurements. The developed algorithm is based on maximum likelihood optimization in this way targeted on optimal phase retrieval from noisy observations. The algorithm for the Poissonian and Gaussian noise distribution is presented. The sparse modeling enables a regularization of the inverse imaging problem and efficient suppression of random observation errors by BM3D filtering of phase and amplitude. The effectiveness of the developed WM-APR algorithm is verified by simulation experiments demonstrated in this work only for the Poissonian noise. The high-accuracy performance of the algorithm is achieved for a high-dynamic range of absolute phase variations while the algorithms operating with the single wavelength observations separately failed. Physical experiments are our priorities for a further work. We consider nonidealities of SLM as a phase modulator and the AS propagation operator as the main challenges for the accuracy of profile and absolute phase reconstruction.  
**Author Contributions:** [PERSON] developed the algorithm; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], and K.E. analyzed the data and wrote the paper.  
**Acknowledgments:** This work is supported by Academy of Finland, project no. 287150, 2015-2019, Russian Ministry of Education and Science (project within the state mission for institutions of higher education, agreement 3.1893.2017/4.6) and Horizon 2020 TWINN-2015, grant 687328--HOLO.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 6: Logo TUT phase reconstructions: \(RRMSE=0.084\) (\(SNR=6.3\) dB, \(N_{photon}=1.42\)), (**top row**)—3D absolute phase surfaces, (**bottom row**)—the corresponding 2D absolute phases images. From left to right: WM-APR algorithm, and phase reconstructions obtained separately for \(\lambda_{1},\lambda_{2},\lambda_{3}\) wavelengths, respectively, followed by the 2D phase unwrapping.
## 2 Related Work
### Localization Schemes
#### 2.2.1 Weighted Centroid Localization (WCL)  
WCL is a localization method where the locations of UNNs are calculated using the average of the coordinates of the location information they receive from the beacons. Communication consumption and computation cost are low. In the WCL method proposed in the study [32], a \(w_{ij}\) weight function was defined based on the RSSI value that varies according to the distance between the UNN and the AN to examine each \(B_{j}(x,y)\) coordinate information's effect received. \(w_{\bar{y}}\) can be calculated as:  
\[w_{ij}=\frac{1}{\left(d_{ij}\right)^{\bar{g}}} \tag{1}\]  
where \(g\) is a default degree for different situations; thus, the node location can be formulated as follows (indicates the number of beacons received from the anchor):  
\[\mathrm{P_{i}}=\frac{\sum_{j=1}^{n}w_{ij}B_{j}(x,y)}{\sum_{j=1}^{n}w_{ij}} \tag{2}\]  
After replacing \(w_{\bar{y}}\) with \(RSSI_{\bar{y}}\), the final equation is obtained as:  
\[\mathrm{P_{i}}=\frac{\sum_{j=1}^{n}RSSI_{\bar{y}}B_{j}}{\sum_{j=1}^{n}RSSI_{ \bar{y}}} \tag{3}\]