esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Research  
This study proposes the development of the PBRTQC approach [17; 21], which stands for Patient-Based Real-Time Quality Control, for a broader spectrum of applications than just quality control itself. Based on data from real-time patient studies, we can draw conclusions regarding quality control, as well as reagent management, analytical device overload, and patient profiles. The scientific novelty of this study lies in the fact that it presents a comprehensive approach to utilizing additional knowledge from ongoing analytical research and the very concept of the solution. Hence, there is less urgency to search for unique machine learning models, as our aim was to instill trust in results derived from relatively easy-to-interpret models that are also explainable to users whose domain is not computational models but traditional statistical quality control techniques. However, if such a solution gains full user trust, we can then explore areas for improvement in individual modules and strive to increase precision using modern, more sophisticated machine learning models for further studies. Research also indicates that there is further potential for system development. The obtained patient profiles may, in the future, allow for individual analyses of patients' test results to personalize recommendations for further examinations. However, this is a topic for future research.  
Conceptualization, K.R., A.O. and P.W.; data curation, P.W., J.S. and P.S.; formal analysis, K.R. and A.O.; funding acquisition, A.K.-S., P.W. and J.S.; investigation, K.R., A.O. and P.W.; methodology, K.R. and A.O.; project administration, A.K.-S.; supervision, P.W. and J.S.; validation, P.S.; writing--original draft, K.R. and A.O.; writing--review and editing, K.R. and A.O. All authors have read and agreed to the published version of the manuscript.  
The research was co-financed by the European Regional Development Fund under the Intelligent Development Program 2014-2020, project \"Automatic prediction system for laboratory research and optimization of quality control and workload of analyzers based on machine learning\", funded as part of a competition organized by the National Centre for Research and Development under grant POIR.01.01.01-00-1259/20-01 and Diagnostyka S.A.  
Data is contained within the article.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Diagnostyka S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential 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.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Benefits
#### 8.1.8 Improved Network Visibility  
KDN collects a wider variety of data compared to SDN. Non-conventional data, such as device sensor data, can be collected by the management plane to generate knowledge and gain more insight into the network infrastructure's availability, health, status, etc. In SDN, information-centric decisions are made based on collected information without generating knowledge or rules from it. However, knowledge has more decision-making power than raw data or information, as new patterns or concepts can be inferred from existing data. Thus, the generated knowledge provides more insight into the underlying network than information or raw data, thus providing a higher level of visibility into the network. Thus, improved visibility due to knowledge generation can affect the management and control planes to provide better network management and control decisions. KDN can provide granular network insight, including network performance at the device and application level, which helps identify any issue caused by the specific devices or applications. KDN can collect data on user behavior, including the applications that are being used and how much of each resource is consumed, which will help identify underutilization or overutilization. KDN can collect data such as traffic patterns, network device status information, network device resource utilization information, etc. to analyze patterns in the collected data using machine learning to obtain complete visibility on the network's performance, allowing it to identify areas for improvement and take corrective actions.
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 4 Experiment and Results  
In order to evaluate the effectiveness of the proposed system, different experiments are designed to test the system's accuracy in counting passengers, estimating speed, and detecting the seatbelt status. The hardware used in the experiments includes an Arduino Mega 2560 as a gateway connected to the sensors. This microcontroller is used as an edge for the preprocessing of data and aggregation of the data. The edge sends the data to a smartphone, which represents the fog layer, because smartphones possess processing power, local storage, and GPS sensors. We modified the work published in [17] to allow the speed estimation process to run on the fog layer. Table 2 lists the specifications of the three smartphones (smartphone 1 (s1), smartphone 2 (s2), and smartphone 3 (s3) that were utilized to evaluate the proposed system. In Table 1, CPU refers to the central processing unit specification, GPU identifies the Adreno graphical processing unit, and the last column defines the size of memory in gigabytes.  
We used CloudSim [53] to simulate the cloud layer, and it was operated on a server connected to the internet and hosting a MySQL database. The CloudSim simulation parameters are shown in Table 3. The edge and fog side are real devices, but the cloud is simulated due to resource limitations.  
To test the connectivity between the cloud and the traffic monitoring station, the web pages for the monitoring station's website were developed using PHP, and the database was created using MySQL. Other web pages will also open once a user logs in to the system using the log-in page, depending on the user's privileges. The user can view the violations and generate different reports.
# High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF
## 5 Imaging Algorithm
### Resampling Strategy for the CVPRF System  
Based on the MESRM, after demodulation to baseband, the received signal for a point target can be described as follows:  
\[s(\tau,t)=\sigma_{0}\omega_{a}\left(t-t_{0}\right)\cdot exp\left\{-\frac{j4\pi R( t)}{\lambda}\right\}\cdot\omega_{r}\left(\tau-\frac{2R(t)}{c}\right)\cdot exp \left\{j\pi K_{r}\left[\tau-\frac{2R(t)}{c}\right]^{2}\right\} \tag{8}\]  
where \(\omega_{r}(\cdot)\) and \(\omega_{a}(\cdot)\) denote the antenna pattern functions in the range and azimuth directions, respectively; \(\sigma_{0}\) represents the scattering coefficient; \(c\) is the speed of light; \(\lambda\) is the signal wavelength; \(K_{r}\) is the range chirp rate; \(\tau\) is the fast time in range; and \(t_{0}\) is the Doppler center time.  
To compensate for the impact of phase error caused by the sampling time, the received signal should be transformed into the range-frequency and azimuth-time domains using the principle of stationary phases and Fourier transforms. The expression of the echo signal in the range-frequency and azimuth-time domains can be obtained as  
\[S(f_{\tau},t)=\sigma_{0}\omega_{a}(t-t_{0})\cdot\omega_{r}\left(-\frac{f_{\tau }}{K_{r}}\right)\cdot exp\left\{-j\pi f\frac{f_{\tau}^{2}}{K_{r}}\right\} \cdot exp\left\{-j4\pi R(t)\left(\frac{f_{\tau}}{c}+\frac{1}{\lambda}\right)\right\} \tag{9}\]  
where \(f_{\tau}\) is the range frequency.  
Due to the non-uniform sampling in the azimuth, the phase error caused by the difference between \(R(t)\) and \(R(t_{uniform})\) will lead to a failure of the imaging. An effective resampling filter based on phase compensation and Lagrange interpolation has been proposed by [20]. The accuracy of \(R(t)\) and \(R(t_{uniform})\) is critical to the validity of the resampling. Hence, a geometry simulation is used to obtain \(R(t)\), and the MESRM is adopted to calculate the \(R(t_{uniform})\). After addressing the non-uniform problem of the CVPRF system, the traditional imaging algorithm for high-resolution wide-swath SAR can be applied.
## 3 Materials and Methods
### Design and Development of the LoRa Evaluation Experiments  
Figure 2 presents the prototype setting for the performed experiments. The \"web app\" component was not implemented.  
The characteristics of the performed experiments are different from those in the related works. This research used 72 settings in field tests, taking into account all the possible value combinations of the considered parameters (SF, BW, and CR). Although a few related works feature a similar or larger number of settings, the characteristics of the experiments (in terms of number of devices, packet rate, distance between sender and receiver, displacement of devices, and use of simulators) vary considerably. In addition, related works specifically related to tracking services in transportation have great limitations in terms of the used number of settings and the number of performed tests.  
Six experiments were performed for evaluation of LoRa in the specified service. Three of them were performed with fixed distance, and three with variable distance. The characteristics of the six experiments are presented in Table 1.  
Figure 2: Transit vehicle tracking service prototype.  
The first three experiments were performed with fixed distances for two reasons. The first reason was to show a possible linear relationship between the measured RSSI value and the change in distance in any of the settings, or between the RSSI value and some of the parameters of the setting (SF, BW, and CR). This type of linear relationship would help to identify the appropriate settings for the service context. The second reason was to limit the number of settings for the moving vehicle experiments. With the experiments at fixed distances it was possible to rule out some of the 72 possible settings for the final experiments with variable distance. Testing for all settings would have required too much experimentation time, using settings that would not have yielded adequate results.  
The first five experiments were performed in the city of Palmira (medium-sized city in the south-west of Colombia, South America). The gateway was located at the (3.548523 N, 76.288618 W) coordinates in the first five experiments. The last experiment (Experiment 6) was performed in the city of Popayan (another medium-sized city in the south-west of Colombia). The gateway in Experiment 6 was located at the (2.493120 N, 76.559722 W) coordinates. The change of location of the last experiment looked out for the best conditions in terms of needed materials, road conditions and the possibility of a better line of sight (LoS) between the gateway and the vehicles.  
The details of the two types of experiments are discussed below.
## 5 Conclusions  
In this work, we presented and demonstrated a RS-based UWOCC system utilizing an LSTM-NN with SGOF. In order to enhance the applicability of OCC systems, both the Tx and Rx sides should have a wide FOV. Recently, wide FOV optical Txs based on SGOF were proposed. They can provide 360\({}^{\circ}\) uniform light emission around the fiber circumference. This means that SGOF can act as an \"omni-directional optical antenna\". In the literature, SGOF was installed in a flying UAV to provide OCC transmission. Furthermore, SGOF was demonstrated to support multiple movable Rxs to obtain data signals along the fiber. SGOF is lightweight and flexibly bendable. Most importantly, SGOF is water resistant; hence, it can be installed in underwater environments. As SGOF Tx is lightweight, flexibly bendable, and water resistant, one potential application is that it can be easily installed  
Figure 13: BER measurements at different data rates and different fiber separations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fiber Separation (cm)** & **Occupied Pixel (Pixels)** \\ \hline
0.5 & 20 \\
3 & 113 \\
5 & 184 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Occupied pixels in the RS pattern at different fiber separations.  
on the surface of underwater AUVs for transmitting sensing or surveillance information to other nearby AUVs or divers equipped with CMOS image sensors for signal receiving. The proposed LSTM-NN has the time-memorizing characteristics to enhance OCC signal decoding. The proposed LSTM-NN is also compared with other decoding methods in the literature, such as the PPB-NN. The experimental results demonstrated that the proposed LSTM-NN outperforms the PPB-NN in the UUWOCC system. A data rate of 2.7 kbit/s can be achieved in UWOCC, satisfying the pre-FEC condition (i.e., BER \(\leq\) 3.8 \(\times\) 10\({}^{-3}\)). We also studied that at a fiber separation of 0.5 cm, the occupied number of pixels between the two fibers was \(\sim\)20 pixels. When using a CMOS image sensor with a 1920 \(\times\) 1440 pixel resolution, it can be estimated that 96 fibers can be captured in the image sensor. Hence, an aggregated data rate \(>\) 200 kbit/s (i.e., 96 fibers \(\times\) 3 kbit/s) could be possible.  
Data curation, [PERSON]-[PERSON], [PERSON]-H.C. and [PERSON]-[PERSON]; funding acquisition, [PERSON]-[PERSON]; investigation, [PERSON]-[PERSON] and [PERSON]-H.C.; writing--original draft, J.-F.L.; writing--review and editing, J.-F.L. and C.-W.C. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Science and Technology Council, Taiwan, under Grant NSTC-112-2221-E-A49-102-MY3, NSTC-113-2221-E-A49-055-MY3, NSTC-113-2218-E-011-009, and NSTC-113-2640-E-A49-006.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 5 Conclusions  
Inspired by the methodology in pharmacy to quantitatively describe the drug dose using the concept of LD\({}_{50}\), this study provides a proof of concept to estimate the plasma dosage in the field of plasma medicine by measuring the value of LD\({}_{50}\) with the CAP treatment of the in vitro kidney cells. This novel method opens a new and promising direction for a quantitative definition of the plasma dosage with the advantages of versatility and easy manipulation. In future research, more systematic research needs to be conducted in order to deepen our understandings to the CAP cell treatment effects quantitatively. For example, the cell morphological changes after CAP treatment with the aid of cell staining and a high resolution image recording facility can be investigated; complementary assays on the cell viability can be conducted to further improve the accuracy of the LD\({}_{50}\) values; the sub-lethal effects on living cells when the CAP treatment dosage is lower than the value of LD\({}_{50}\) should also be studied in detail. Consequently, a database including the macroscopic morphologies, CAP sub-lethal effects, and values of LD\({}_{50}\) for various cell lines using different types of CAP sources can be established. This may form the basis for investigating the quite complex biomedical effects of CAPs in plasma medicine.  
Conceptualization, Y.Z. and H.-P.L.; methodology, Y.Z., H.-P.L., J.L., L.-X.Z. and W.-W.D.; software, T.H., X.Z. and Z.-F.Z.; validation, J.L., L.-X.Z. and T.H.; formal analysis, X.-C. and L.-X.Z.; investigation, J.L., L.-X.Z., T.H. and Y.Y.; resources, T.H., N.Z. and Y.Y.; data curation, J.L., L.-X.Z. and T.H.; writing--original draft preparation, J.L., L.-X.Z. and T.H.; writing--review and editing, Y.Z. and H.-P.L.; visualization, J.L., L.-X.Z. and T.H.; supervision, Y.Z. and H.-P.L.; funding acquisition, Y.Z. and H.-P.L. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Tsinghua University Initiative Scientific Research Program (No. 20182000306), the projects from Tsinghua Precision Medicine Foundation, Tsinghua-Foshan Innovation Special Fund (TFISF, No. 2020 THFS0118), and partly supported by the National Natural Science Foundation of China (Nos. 11475103, 21627812).  
The authors declare no conflict of interest.
## 2 Results and Discussion
### General workflow
#### Unsupervised Analysis without Feature Selection  
Mutation, natural variation, and environmental conditions affect metabolic processes. Metabolome analysis can help us to understand the link between these factors and the overall characteristics of an organism. Unsupervised methods are the choices to investigate underlying data structure, unbiased by the knowledge of experimental design. For LC-MS data, PCA and clustering were used for preliminary data mining on whole sets of peaks detected by XCMS and MarkerView. GC-TOF-MS datasets were used after annotation. Searching and elimination of correlated variables introduced by the samples preparation and/or analytical methods applied is very important especially when it concerns values close to the margins of measurements like overload, limits of detection and background noise levels. Close proximity to both these factors can easily generate false positive values characterized with the convincing p-value that may actually not reflect real situation, but the absence of the particular variable which is not detected in a sample, or group of samples, since its level is below current instrument/method lower limit of detection. When biological question is related to the highest variance in the dataset, PCA is a powerful technique for dimensionality reduction, visualization and exploration.  
Visualization methods are often the best way to discover interesting grouping information in the data, whereas clustering methods provide mathematical rigor. Basically, there are three major categories of clustering methods: partitioning (clusters), hierarchical (trees), or probability model-based (models). Partitioning methods map peaks into multiple disjointed clusters using a chosen criterion. K-means is the most popular partitioning method, although it requires the input of an initial clustering number. The K-means clustering algorithm chooses a pre-specified number of cluster centers to minimize the within-class sum of squares from those centers. Hierarchical methods construct a binary tree in which the root is a single cluster containing only one element and the leaves each contain only one element. A divisive tree is top-down built and an agglomerative tree is bottom-up built. Recently, probability model-based clustering methods have become increasingly popular, with the advances in methods, software, and interpretability of the results. Probability modeling assumesthat the data pool is a mixture with all of the labels lost and tries to find the most possible label for each data point.
## 3 Results
### Biodegradation of UDMH in Soil  
The preliminary experiments had shown that the degradation percentage of UDMH with M12 and P4 without the addition of broth medium in SS was no higher than the sample with no added broth medium, so M12 and P4 without broth medium were not discussed in this study. The biodegradation percentage of UDMH in the soil is shown in Figure 3.  
It is worth noting that the M12 strain, which had the best degradation ability of UDMH in water, could not degrade UDMH in soil. In Figure 3a, no matter whether there was an added external carbon source or not, M12 degradation capability could not exceed that of the control samples, and M12 + _n_SS degradation rate was even lower than _n_SS. Because M12 did not reveal a good degrading capacity in water, it could be inferred that M12 might lack some biodegradation conditions in soil.  
In Figure 3b, the P4 strain had a good degradation ability with the broth medium and could co-metabolize with the UDMH in soil. During the first 48 h of degradation, P4 + SS showed the strongest degradation ability, and the degrading percentage was obviously higher than SS + broth and M12 + SS + broth, and even higher than P4 + _n_SS + broth. This showed that UDMH in soil could be degraded by the P4 strain, which had high adaptability to UDMH in soil environment and was inhibited by indigenous microorganisms in the early growth period. After 48 h, P4 adapted to UDMH, and the degradation rate of the P4 + _n_SS + broth samples significantly improved. The final degrading percentage of P4 in the _n_SS sample was 97.0%, which was the highest in all the samples. The final UDMH concentration of the P4 + _n_SS + broth sample was 5.8 mg\(\cdot\) kg\({}^{-1}\), and in the _n_SS + broth  
Figure 3: The degradation of UDMH in soil: (**a**) the degradation in sterile soil (SS) and non-sterile soil (_n_SS) with the M12 strain; (**b**) the degradation in SS and _n_SS with the P4 strain.  
sample it was 12.3 mg\(\cdot\) kg\({}^{-1}\). Given that UDMH could remain stable for a long time in the soil [27], the reduction in UDMH concentration can be attributed to the participation of the P4 strain. More experiments showed that the concentration of UDMH in soil underwent little change after five days, and the improvement of UDMH degrading percentage in the contaminated soil was a difficult process.
# Kinematics Model Optimization Algorithm for Six Degrees of Freedom Parallel Platform
## 2 Materials and Methods
### MPR-NR Algorithm Design  
The MPR-NR algorithm uses the MPR algorithm to initially locate the kinematics positive solution. Reduce the distance between the initial attitude and the target attitude in the NR algorithm. Then use the NR algorithm to iteratively solve the kinematics positive solution that meets the requirements according to the accuracy requirements. The algorithm flow of using the MPR-NR algorithm for the kinematics forward solution is shown in Figure 4.  
Figure 4: MPR-NR algorithm flowchart.  
It can be seen from Figure 4 that when the result calculated by the MPR algorithm for the first time meets the set accuracy, the Newton iteration process will not be entered. If the solution accuracy of the MPR algorithm does not meet the requirements, iterative optimization will be carried out through the Newton iterative algorithm, and finally the accuracy will be improved to the accuracy required by the system. Therefore, the goal of controllable precision can be achieved in this way. On the other hand, the number of iterations for Newton iterative solution is related to the distance between the initial attitude and the target attitude. Given a better initial solution through the MPR algorithm, the number of iterations can be reduced, and the time-consuming solution can be shortened.