esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Approach to Defect Detection Probability by Acoustic Emission Testing
## 4 Results and Discussion
### Results of Estimating the Type-I Error Probability  
In this study, we considered signals of two types as noise: stationary noise generated by the compressor of the loading device and unsteady impulse noise caused by turbulent fluid flow inside the object under test. Figure 15a,b shows the temporal realizations of this noise. Figure 15c,d illustrates the probability distribution density.  
The distribution of stationary noise is close to normal, while the distribution of non-stationary noise is different from the normal due to a heavy tail formed with an outlier's samples. With a standard time of AE testing of about 1 h and an average sampling frequency of ~5 MHz, about 10\({}^{10}\) samples are analyzed during the testing time.  
Figure 16 shows the POD calculation results obtained using the method described in paragraph 3.3 for stationary uncorrelated and stationary correlated noise at \(L\) = 5 m. For uncorrelated noise, the probability of type-I error turned out to be significantly lower than for correlated noise. This can be explained by the fact that the statistical independence of uncorrelated data measured by different AE channels reduces the probability of a false location. A sharp increase in the probability of false detection for uncorrelated noise is observed at a threshold of about 6 _std_, for correlated noise--at 4 _std_; therefore, at the same noise level, the detection threshold for uncorrelated noise can be 1.5 times lower than for correlated noise. An increase in the required number of indications in a location cluster from 5 to 20 allows reducing the value of the data registration threshold by 0.7 dB, which is an insignificant value in terms of a better likelihood of detection.  
Figure 15: Temporal realizations (**a**,**b**) and probability density functions (**c**,**d**) of noise caused by compressor (**a**,**c**) and turbulent fluid flow (**b**,**d**).  
Figure 17 shows the result of a type-I error probability calculation for the case of non-stationary noise. The impulse nature of the noise (Figure 15b) requires the detection threshold to be significantly higher than for stationary processes.  
An acceptable value of the probability of a type-I error is achieved at a threshold of the order of 10-15 _std_. At the same time, an increase in the \(N^{clust}\) parameter from 5 to 20 makes it possible to reduce the threshold by at least 4 dB at the same error level.
## 3 Results  
### AgNPs Synthesis  
The synthesis of AgNPs with the use of plant extracts generally involves the obtaining of extracts and silver salt solutions, formation of the AgNPs by mixing the two solutions in different amounts and under certain medium conditions (pH, stirring time, and temperature), steps that were also applied in our case. The final phase was represented by the separation and purification of nanoparticles, followed by the confirmation of AgNPs synthesis through different analytical approaches.  
Firstly, the formation of AgNPs was observed, taking into account the color change. After the reaction was completed, a change in color from yellow to brown was observed, which, according to other studies, can be attributed to the reduction of Ag\({}^{+}\) to metal nanosilver, Ag\({}^{0}\)[32; 33].  
The successful synthesis of AgNPs was afterwards confirmed by SPR detection via UV-Vis spectrophotometry in the 400 to 500 nm range [34; 35]. Furthermore, these optical features have been used for the analysis of optimal synthesis conditions.
###### Abstract  
In order to solve the difficulties of dispatching the regional integrated energy system (RIES) under the operating conditions of multi-energy complementary mechanisms, as well as to achieve the purpose of economic operation and low carbon operation of the system, an optimal dispatching model of RIES, including demand response (DR) and an improved carbon trading mechanism (ICTM), is proposed. Firstly, a demand response model is established, the cooling, thermal, electricity, and gas load models under demand response are built, and then an improved customer satisfaction model is proposed based on the four demand response load models. In addition, since EV trips fit a normal distribution, the charging load of EVs is predicted using a Monte Carlo method and incorporated into RIES as a demand-side load; moreover, for EVs, an improved genetic algorithm is used to optimize EV charging, aiming to reduce the peak-to-valley difference; secondly, carbon emission quotas are provided for systems and EVs based on the baseline method and gratuitous allocation, and a carbon trading model is constructed based on carbon quotas and actual A carbon trading model for the system and EV is constructed based on the carbon allowances and actual carbon emissions; finally, four operation scenarios are set up in this paper, and the unit output scheme is developed with the objective of achieving the lowest total system operation cost and lowest carbon emissions. The four typical scenarios are solved using the MATLAB/CPLEX solver and compared for analysis. The simulation results show that an improved genetic algorithm for optimizing the ordered charging method of electric vehicle charging reduces the peak valley difference by 23.06%, and the total operation cost and carbon transaction cost are reduced by 16.13% and 83.10%, respectively, which can provide a reference for the environmental protection and economic dispatch of RIES.  
improved carbon trading mechanism; regional integrated energy system; improved user satisfaction model; demand response; base-line method; Monte Carlo method +
Footnote †: journal: Applied sciences
## 6 Discussion
### Possible Applications of the Results  
There are several possible applications of this research.  
Outputs of the first phase of the research can be of further use as resources in the domain of NLP tasks focused on the Croatian language. Thus, Cro-CoV-cseBERT can be used in any NLP application with the task of analysis of COVID-19-related texts. The dataset Cro-CoV-Tweets of publicly available tweets can serve as a resource for other similar studies. Next, the dataset Senti-Cro-CoV-Tweets of 10,000 tweets labeled with sentiment is a valuable resource for training and/or evaluation of other supervised models in the task of sentiment analysis.  
Furthermore, the analysis and characterisation of tweets during the pandemic provides interesting information about COVID-19 communication on social media. It reveals public opinions and attitudes related to COVID-19 themes, allowing the authorities to address crisis communication problems, such as, for instance, perception of COVID-19 policies, public attitudes towards vaccines or opinions regarding problems related to the economy, etc. All these results will be available through an interactive web application that allows queries over different time periods and provides data visualizations of sentiment, topics, and retweets. This will enable scientists to exploit our results in other scientific fields such as psychology or sociology.  
As a study of COVID-19-related communication on Twitter limited to the Croatian language and Croatia, this study may serve as a resource for further comparative research of COVID-19-related communication in different languages. Additionally, the proposed framework can be extended and used for further analysis of tweets posted during the pandemic and post-pandemic periods. In addition to this, similar approaches could be applied to other languages using appropriate language resources.
# Generation of Sparse Antennas and Scatterers Based on Optimal Current Grid Approximation
## 3 Verification of the Modeling Results for Scattering Plate Using MoM with PBF Based on WG Model
Consequently, the sparse structures at these positions tend to include horizontal wires, although the quantity of horizontal wires may not be substantial (i.e., these horizontal wires do not repeat frequently in the sparse structures). Therefore, when  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Structure**} & \multirow{2}{*}{**BSCS\({}_{\text{max}}\)**} & \multicolumn{3}{c}{**Maximum BSCS Deviation**} \\ \cline{3-5}  & & \(\phi\) = 60–120\({}^{\circ}\), & \(\varphi\) = 60–120\({}^{\circ}\) & **xOy Plane** & **yOz Plane** \\ \hline S40 & 1.388 & 31.17 & 2.45 & 17.14 \\ FS\({}_{\text{r}}\)50 & 0.0003 & 18.15 & 1.98 & 0.37 \\ FS\({}_{\text{r}}\)100 & 0.014 & 28.51 & 2.1 & 19.58 \\ FS\({}_{\text{r}}\)200 & 0.33 & 23.45 & 2.2 & 12.84 \\ FS\({}_{\text{r}}\)300 & 0 using the threshold \(T\), these horizontal wires are inadvertently excluded, preventing them from contributing to the scattering characteristics of the FS. Figure 16b illustrates that the BSCSs of all FS structures match closely with each other and match well with that of the WG structure, while S40 shows significantly more deviation not only at the main direction but also at the first side lobe and across the region of \(\varphi\) = 0-45\({}^{\circ}\). Similar observations can be made from Figure 16c; however, as \(T\) changes, the deviations in the BSCSs for the FS structures from that of the WG also vary. Similar findings are reflected in \[corr(a,b)\ =\ \frac{\sum\limits_{i\ =\ 1}^{n}\left(X_{a,i}-\overline{X_{a}} \right)\left(Y_{b,j}-\overline{Y_{b}}\right)}{\sqrt{\sum\limits_{i\ =\ 1}^{n}\left(X_{a,i}-\overline{X_{a}}\right)^{2}\sum\limits_{i\ =\ 1}^{n}\left(Y_{b,j}-\overline{Y_{b}}\right)^{2}}} \tag{1}\]2 Clearly, among all the structures, the FS\({}_{r}\)50 BSCS most closely resembles the WG BSCS compared to the other FS structures. Moreover, as mentioned earlier, when \(T<400\), the reduction in mass for the FS does not change significantly. Therefore, FS\({}_{r}\)50 can be selected for production when the incident wave excited in the region of \(\theta\) = 60-120\({}^{\circ}\) and \(\varphi\) = 60-120\({}^{\circ}\).
## 3 Results
### Leaveening Test
#### 3.2.1.  
Both for dough weight and volume, data recorded within the leavening microtest could be processed by three-factorial ANOVA; the F1 was leavening test time, F2 essential oil type, and F3 essential oil dose. For the dough weight loss, a linear decrease for the non-fortified wheat dough (STD) as well as for both the THY- and LMG-enriched counterparts was observed. A weight loss step oscillated between 0.1 and 0.2 wt.%; to ensure the clarity of the ANOVA chart and to magnify significant differences, a dataset was reduced to the leavening times 0, 30, 60, and 90 min (Figure 1). During the entire leavening test, 1.21 g of the control wheat dough was metabolized by yeasts. For the 5 THY and 10 THY samples, the consumed mass portion increased nearly twice (1.97 and 2.04 g, respectively). Along with the magnifying THY doses, these total weight losses diminished surprisingly up to 1.34 g. For a row of the LMG-dough variants, the corresponding weight losses were lower--1.59 g,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Essential Oil**} & \multicolumn{5}{c}{**Mixolab Torque Point**} & \multicolumn{3}{c}{**Mixolab Time Value**} \\ \hline \multirow{2}{*}{**Type**} & **Dose** & **C1** & **C2** & **C3** & **C4** & **C5** & \(t_{\text{-C1}}\)* & **STA** **\\  & **(mg/100 g)** & **(N.m)** & **(N.m)** & **(N.m)** & **(N.m)** & **(N.m)** & **(min)** & **(min)** \\ \hline Control & 0 & 1.08 a & 0.50 a & 1.82 a & 1.68 a & 2.61 a & 3.75 a & 9.20 d \\ \hline THY & 10 & 1.10 a & 0.51 a & 1.82 a & 1.67 a & 2.60 a & 3.95 ab & 8.10 c \\  & 20 & 1.12 a & 0.41 a & 1.79 a & 1.71 a & 2.82 a & 3.73 a & 5.90 a \\  & 40 & 1.12 a & 0.41 a & 1.82 a & 1.71 a & 2.80 a & 3.90 ab & 5.80 a \\  & 80 & 1.09 a & 0.41 a & 1.81 a & 1.71 a & 2.87 a & 3.97 ab & 6.90 b \\ \hline LMG & 10 & 1.08 a & 0.51 a & 1.85 a & 1.67 a & 2.66 a & 3.70 a & 9.40 d \\  & 20 & 1.10 a & 0.52 a & 1.86 a & 1.74 a & 2.74 a & 3.93 ab & 9.20 d \\  & 40 & 1.07 a & 0.51 a & 1.83 a & 1.68 a & 2.65 a & 3.88 ab & 9.30 d \\  & 80 & 1.07 a & 0.53 a & 1.84 a & 1.63 a & 2.67 a & 4.18 b & 9.20 d \\ \hline \multicolumn{2}{c}{Subgroup means} & & & & & & & \\ \hline Control & 0 & 1.08 A & 0.50 AB & 1.82 A & 1.68 A & 2.61 A & 3.75 A & 9.20 B \\ THY & 10–80 & 1.11 A & 0.43 A & 1.81 A & 1.70 A & 2.77 A & 3.89 A & 6.68 A \\ LMG & 10–80 & 1.08 A & 0.52 B & 1.84 A & 1.68 A & 2.68 A & 3.92 A & 9.28 B \\ \hline \hline \end{tabular}  
* time of the occurrence of the maximal consistency C1; ** stability of dough consistency. a–d: averages in columns, signed by the same letter, are not statistically different (\(p\) = 95%). A–B: In columns, capital letters mark subgroup averages and statistically significant differences (\(p\) = 95%).  
\end{table}
Table 1: Mixolab testing of rheological behavior of non-fermented dough as affected by several doses of thyme and lemongrass essential oils (THY and LMG, respectively).  
1.76 g, and 1.38 g, respectively. Described differences in the effect of THY and LMG can be noticed in Figure 1, especially for specimen pairs 5 THY-5 LMG and 10 THY-10 LMG.
## 7 Conclusions  
The WSN research community has done an admirable job of addressing some of the limitations that currently exist for health care related applications. Proposals have mostly focused on the deployment of tiny wearable medical sensors, while others have developed infrastructures for monitoring individual patients during daily activity, at home, or at a hospital. In this paper, we review the state-of-the-art in wireless sensor network research and highlight the gaps between the existing technologies and the needs of a Health Care Wireless Sensor Network (HCWSN), with special emphasis on reliable communication. A survey of the existing HCWSNs reveals that MEDiSN [19] offers the most suitable and comprehensive system for the facilitation of reliable communication.  
MEDiSN adopts a multi-tiered architecture that uses a planned wireless backbone network to fortify network throughput and achieve high PDRs. Reliable communication in health care applications also encompasses the delivery of packet with low latency. We reviewed a number of MAC protocols from the literature that struck a good balance between energy efficiency and low latency packet transmission. We found that MD-SMAC was the most suitable for HCWsNs because it uses a combination of dynamic duty cycling and adaptive neighbor discovery frequency to handle mobility scenarios. Furthermore, MD-SMAC is able to achieve energy efficiency while not incurring exorbitantly high delivery latency. We also examined the performance of a number of different routing protocols employed in HCWSNs. Our comparisons showed that the routing protocol in MEDiSN and modified CTP outperforms the routing protocol in CodeBlue and TinyADMR, in terms of PDR for the same number of active sensors and also in terms of the maximum number of supported sensors. To ensure the QoS requirements (\(>\)90% PDR and 5 second latency [21]) of health care applications are met, we also investigated various mechanisms for reliable transmission. We concluded that the ART protocol is most adequate for HCWsNs mainly because it supports multicast transmissions and can provide reliability for upstream events and downstream queries. Finally, we consider network coding as an alternative to improving reliability, and the results from the performance evaluation of AdapCode show that network coding can increase PDR while also lowering the level of expended overhead (improved energy efficiency).
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 5 Tomography Experiments and Results
### Influences of Horizontal Resolution  
Two cases (CS06 and CS07) were carried out and compared in order to study the influences of different tomographic horizontal resolution on the tomography. The horizontal resolution in CS06 and CS07 is set to be 0.5 and 0.25 degrees, resulting in 5760 and 21,528 voxels, respectively. The GNSS locations were simulated with horizontal resolution of 0.25 degrees as described in Table 1, and comparisons of wet refractivity error for CS06 and CS07 are presented in Figure 8. We can find that wet refractivity errors in CS07 are generally smaller than in CS06, especially at the middle layers (layers 4 to 10). At the lowest layer, the average _Nw_ error is about 6.27 and 6.09 mm/km in CS06 and CS07, respectively, with about 3% improvement when the horizontal resolution increased from 0.5 to 0.25  
Figure 8: Comparisons of wet refractivity error profiles for CS04, CS05, CS06, and CS07 for a study of influences of ground-based station distribution and horizontal resolution on tomography.  
degrees. At middle layers, like the 9 th layer, _Nw_ accuracy in CS07 is improved by about 6% compared to CS06. At heights above 18 th layers, the differences are negligible because of the low humidity.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
## 4 c-Cbba
### Task Preemption Strategy
#### 4.1.2 Consensus Check

Table 6: c-CBBA’s rules for updating information. \begin{table}
\begin{tabular}{c c} \hline \(s_{i^{\prime}}\) (Sender) Think \(z_{ij}\) Is & \(s_{i}\) (Receiver) Think \(z_{ij}\) Is & Receiver’s Action (Default: Leave) \\ \hline \multirow{4}{*}{\(i^{\prime}\)} & \multirow{4}{*}{\(i\)} & if \(y_{ij}>y_{ij}\rightarrow\) update \\ \cline{2-3}  & & \(i^{\prime}\) & update \\ \cline{2-3}  & & \(i^{\prime}\
otin\{i,\,i^{\prime}\}\) & \(if\,h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\rightarrow\) update \\ \cline{2-3}  & & none & leave \\ \hline \multirow{4}{*}{\(i^{\prime}\
otin\{i,\,i^{\prime}\}\)} & \multirow{4}{*}{\(i\)} & if \(h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\) and \(y_{ij}>y_{ij}\rightarrow\) update \\ \cline{2-3}  & & \(i^{\prime}\) & \(if\,h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\rightarrow\) update \\ \cline{2-3}  & & \(i^{\prime}\) & else \(\rightarrow\) reset \\ \cline{2-3}  & & \(i^{\prime}\) & \(h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\rightarrow\) update \\ \cline{2-3}  & & \(i^{\prime\prime}\
otin\{i,\,i^{\prime},\,i^{\prime}\}\) & \(if\,h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\rightarrow\) update \\ \cline{2-3}  & & \(i\) & if \(h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\) and \(y_{ij}>y_{ij}\rightarrow\) update \\ \hline \multirow{4}{*}{\(i^{\prime}\
otin\{i,\,i^{\prime}\}\)} & \multirow{4}{*}{\(i^{\prime}\)} & if \(h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\rightarrow\) update \\ \cline{2-3}  & & \(i^{\prime}\) & else \(\rightarrow\) reset \\ \cline{2-3}  & & \(i^{\prime\prime}\) & \(h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\rightarrow\) update \\ \cline{2-3}  & & \(i^{\prime\prime}\
otin\{i,\,i^{\prime},\,i^{\prime}\}\) & if \(h_{i^{\prime}i^{\prime}}>h_{ii^{\prime}}\) and \(h_{i^{\prime\prime}i^{\prime}}>h_{ii^{\prime\prime}}\rightarrow\) update \\ \cline{2-3}  & & \(i\) & leave \\ \cline{2-3}  & & \(i^{\prime}\) & update \\ \cline{2-3}  & & \(i^{\prime\prime}\
otin\{i,\,i^{\prime}\}\) & if \(s_{i^{\prime}i^{\prime\prime}}>s_{ii^{\prime\prime}}\rightarrow\) update \\ \cline{2-3}  & & none & leave \\ \hline \end{tabular}
\end{table} 
Table 5: CBBA’s rules for updating information. It specifies the way in which a satellite updates its own information when it receives information transmitted by a neighboring satellite. and all tasks behind position \(\overline{n}\) in \(b_{i}\). Equations (12)-(14) provides specific update formulae. \[y_{ib_{in}}=0,\ \ \forall n>\overline{n}, \tag{12}\]  
\[z_{ib_{in}}=\text{none},\ \ \forall n>\overline{n}, \tag{13}\]  
\[b_{in}=\emptyset,\ \ \forall n\geq\overline{n}, \tag{14}\]
# A Vision-Based Micro-Manipulation System
## 4 Tests and Results
### Image Enhancement  
This study specifically addresses the assessment of fuzzy clustering performance in the context of yeast cell microscopic images. The dataset comprises 1000 raw images generated utilizing a camera equipped with the SONY IMX334 (Sony, Minato, Tokyo, Japan) sensor, which has a size of 1/1.8\", 2.0 \(\times\) 2.0 \(\upmu\)m pixel size and a maximum resolution of 3840 \(\times\) 2160, which was set to 1920 \(\times\) 1080 during testing. This camera was coupled with a 14X-90X optical zoom achromatic field objective, seamlessly integrated into a custom positioning system. Post-capture, a detailed post-processing routine, including resizing to 500 \(\times\) 500 pixels, was applied. For a comprehensive analysis, five distinct images were selected from the database, and three different techniques, such as Fuzzy Automatic Contrast Enhancement (FACE), Single Scale Retinex (SSR), and Histogram Equalization (HE) were applied. The outcomes of the contrast enhancement applied to each image, employing the designated FACE, SSR, and HE methods, are meticulously detailed in Table 1. Furthermore, to facilitate a comprehensive accuracy comparison, Table 1 also shows the 10-fold accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Accuracy** & **RAW** & **FACE** & **SSR** & **HE** \\ \hline Image 1 & & & & \\ Image 2 & & & & \\ Image 3 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image enhancement comparison results.  
Figure 8: Full experimental setup, outlined with red—micro-manipulator; green—micro-manipulator control; blue—object detection.
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 3 Compression-Assisted Adaptive ECC and RAID Scattering
### Power Loss Recovery Step of Metadata Log and Partial Parity  
The metadata log and partial parity retained in memory can be lost from power failure; however, it can be recovered by recovery process. Since the metadata changes after the snapshot are logged to the StripeMapLog block, we can recover metadata changes by scanning the MapLog blocks. For the paritis, almost all parity data are stored in flash memory as a status of parity for full stripe write; the actual portion to be recovered is just the partial parity of write data for the corresponding stripe before power loss.  
The metadata log and parity recovery steps are as follows. This step is power loss recovery step, not error recovery step. At the first step, flash storage tries to find the latest RootInfo by scanning _Root_ block. In the second step, it recovers the metadata snapshot that consists of StripeInfoList, StripeBlkInfo, BlkInfo, and page mapping table by retrieving StripeMap and PageMap blocks. In the third step, the metadata changes, that occur after the snapshot, were stored in the StripeMapLog blocks, so they are recovered by reading page by page and updating each information. The last step to be recovered is the metadata changes that were losted due to the power loss are recovered. Those were just maintained in-memory StripeMapLogInfo so were not flushed to StripeMapLog block. Metadata changes of the StripeMapLogInfo can be recovered by scanning the corresponding stripe group itself, and we can identify the stripe group number from last valid page in the StripeMapLog Block since we record the number of NextStripe group at last valid StripeMapLog. So, the mapping changes of the NextStripe group can be recovered by scanning the spare region of each page. If the page has valid logical address in the spare region, which means the data are stored safely so that its metadata should be updated. If not, the data are not stored safely and the pages are abandoned. For the scanning of each stripe page, if all the pages of the stripe have valid logical address, which means all the data for the stripe are stored safely, the corresponding parity is also safe. If some pages do not have valid logical addresses, which means not all the data in this stripe are valid, then there is no valid parity for this stripe. For this stripe, each valid page is retrieved and parity is calculated with those. It is the partial parity that is lost by power failure. Therefore, we recovered partial parity for this stripe.  
Figure 6: Stripe map log operations for RAID and parity management.