esa-sceva/satcom-chunk-collection huggingface dataset:
### Shanghai Synchrotron Radiation Facility (SSRF)  
The SSRF is the first medium-energy, third-generation synchrotron radiation source on the Chinese mainland. It features a 150 MeV linear accelerator, a 3.5 GeV booster, a 3.5 GeV storage ring, 27 operational beamlines, approximately 40 operational experimental endstations, support facilities, and a dedicated data center [22; 23; 24; 25; 26; 27]. With the ongoing development of the SSRF, and the expansion of its application scope, the amount of data generated exhibits a similar upward trend, including varying computing requirements for various beamlines, as shown in Appendix A Table 11.  
In 2019, the SSRF generated over 0.8 PB of unprocessed data and 2.4 PB of processed data. Once the Phase II project is completed, the SSRF is expected to generate approximately 30 PB of raw data and 100 PB of processed data per year. Assuming a dataset size of 10 GB, on average, the SSRF can currently process one dataset every 3 min. If the daily volume of data processed reaches 160 TB and totals 16,384 datasets, it would take 819 h to complete a single day of computing tasks [25].  
In this context, the processing and analysis of the data from the large-scale synchrotron radiation sources, as well as the improvement of the computing resource usability and data transfer efficiency, are subject to intensive research. As an emerging computing paradigm, edge computing [28; 29] relocates the computing resources and data processing capabilities to the network edge, thereby addressing latency, bandwidth bottlenecks, and other challenges inherent to the conventional computing models. Thus, it has the potential for being an effective data processing and analysis solution for the synchrotron radiation facilities [30; 31].  
With this article, we propose a cloud-edge hybrid intelligent system design scheme tailored to the SSRF's specific conditions and requirements, in order to improve the efficacy and reliability of the data processing and analysis.
# Efficient Online Log Parsing with Log Punctuations Signature
## 4 Evaluation
### Robustness  
In this part, we evaluate the robustness of log parsers on different datasets. Figure 3 shows the accuracy distribution of each log parser across the 16 log datasets in the boxplot. Each box has five horizontal lines from the bottom to the top, corresponding to the minimum, 25 th percentile, median, 75 th percentile, and maximum accuracy values, respectively. Diamond marks indicate outliers since LenMa only has an accuracy of 0.174 on HealthApp, and LogPunk gets 0.504 on Proxifier and 0.741 on Linux. Although LogPunk seems to  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **IPLoM** & **LenMa** & **AEL** & **Spell** & **Spell+** & **Drain** & **Drain+** & **LogPunk** & **Best** \\ \hline Android & 0.712 & 0.88 & 0.682 & **0.919** & **0.922** & **0.911** & **0.913** & **0.936 * & 0.936 \\ Apache & **1 *** & **1 *** & **1 *** & **1 *** & **1 *** & **1 *** & **1 *** & **1 *** & 1 \\ BGL & **0.939** & 0.69 & **0.957** & 0.787 & 0.822 & **0.963** & **0.97** & **0.979 * & 0.979 \\ Hadoop & **0.954** & 0.885 & 0.869 & 0.778 & 0.795 & **0.948** & **0.949** & **0.992 * & 0.992 \\ HDFS & **1 *** & **0.998** & **0.998** & **1 *** & **0.998** & **0.998** & **0.998** & **1** \\ HealthApp & 0.822 & 0.174 & 0.568 & 0.639 & 0.686 & 0.78 & 0.78 & **0.901 * & 0.901 \\ HPC & 0.829 & 0.83 & **0.903** & 0.654 & 0.898 & 0.887 & **0.926** & **0.939 * & 0.939 \\ Linux & 0.672 & 0.701 & 0.673 & 0.605 & 0.739 & 0.69 & 0.749 * & 0.741 & 0.749 \\ Mac & 0.671 & 0.698 & 0.764 & 0.757 & 0.804 & 0.787 & 0.858 * & 0.852 & 0.858 \\ OpenSSH & 0.54 & **0.925** & 0.538 & 0.554 & 0.803 & 0.788 & 0.788 & **0.995 * & 0.995 \\ OpenStack & 0.331 & 0.743 & 0.758 & 0.764 & 0.764 & 0.733 & 0.733 & **1 *** & 1 \\ Proxifier & 0.517 & 0.508 & 0.495 & 0.527 * & 0.527 * & 0.527 * & 0.527 * & 0.504 & 0.527 \\ Spark & **0.92** & 0.884 & **0.905** & **0.905** & **0.905** & **0.92** & **0.92** & **0.923 *** & 0.923 \\ Thunderbird & 0.663 & **0.943** & **0.941** & 0.844 & **0.95** & **0.955** & **0.955 * & **0.951** & 0.955 \\ Windows & 0.567 & 0.566 & 0.69 & **0.989** & **0.99** & **0.997 * & **0.997 * & **0.996** & 0.997 \\ Zookeeper & **0.962** & 0.841 & **0.921** & **0.964** & **0.964** & **0.967** & **0.967** & **0.995 * & 0.995 \\ \hline Average & 0.756 & 0.767 & 0.791 & 0.793 & 0.848 & 0.866 & 0.877 & 0.919 & N.A. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of log parsers on different datasets.  
perform poorly on Proxifier and Linux datasets, other methods do not work well either, and we have even achieved the highest accuracy on Linux.  
For comparison, the log parser is arranged in ascending order of the average precision from left to right. We can observe that LogPunk has the highest average accuracy and the minimum variance, which means robustness. We also observed that Spell+ is more robust than Spell, and Drain+ and Drain have little difference in robustness. Because Drain has some strong assumptions, the preceding tokens of the same log type are the same. Such assumptions are the main reason for Drain's error in accuracy.
# Short-Term Prediction of COVID-19 Cases Using Machine Learning Models
## 3 Experiment Result
### Cloud Based Short Term Forecasting
#### 3.1.6 6 th Round (12 April 2020-13 May 2020)  
As indicated in Table 2, the growth factor of infected cases (1.29) was increased by more than the fatality factor (1.25) in the 6 th round. The training stages of this analysis were initiated with 621 infected and 34 fatality cases on 12 April 2020. We therefore trained a cumulative sequence of 25 days cases with regression models where prophet showed the lowest RMSE, MAE, and highest \(R^{2}\) values (see Table 3) for the test set. It estimated almost  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Round** & **Method** & **RMSE** & **MAE** & **R2** & **Method** & **RMSE** & **MAE** & **R2** \\ \hline \multicolumn{8}{c}{**Infected Cases**} & \multicolumn{8}{c}{**Fatality Cases**} \\ \hline
1 st Round & LR & 1.0950 & 1.0489 & 0.7716 & LR & 0.2766 & 0.2500 & 0.5921 \\
2 nd Round & SVR & 19.5371 & 17.4783 & 0.8372 & SVR & 2.0445 & 1.6569 & 0.7710 \\
3 rd Round & PR & 13.2350 & 10.4837 & 0.9966 & Poly-MLP & 0.9218 & 0.8057 & 0.9777 \\
4 th Round & Poly-MLP & 53.2986 & 43.5731 & 0.9882 & MLP & 0.7134 & 0.6773 & 0.9956 \\
5 th Round & [PERSON] & 43.9722 & 39.9999 & 0.9951 & [PERSON] & 1.0051 & 0.8383 & 0.9758 \\
6 th Round & [PERSON] & 13.0955 & 10.9061 & 0.9998 & [PERSON] & 0.5113 & 0.4621 & 0.9751 \\
7 th Round & [PERSON] & 38.8117 & 32.6514 & 0.9989 & [PERSON] & 1.7241 & 1.5615 & 0.9870 \\
8 th Round & Prophet & 59.5438 & 56.5706 & 0.9987 & Prophet & 1.8866 & 1.6031 & 0.9925 \\
9 th Round & Prophet & 125.7346 & 105.7328 & 0.9946 & Prophet & 0.8403 & 0.5109 & 0.9988 \\
10 th Round & Prophet & 151.9238 & 132.1162 & 0.9974 & Prophet & 2.8667 & 2.5618 & 0.9939 \\
31 th Round & Prophet & 7.13 \(\times 10^{-11}\) & 5.82\(\times 10^{-11}\) & 1.0000 & Prophet & 9.25\(\times 10^{-12}\) & 8.41\(\times 10^{-12}\) & 1.0000 \\
32 th Round & Prophet & 2.10\(\times 10^{-10}\) & 1.46\(\times 10^{-10}\) & 1.0000 & Prophet & 4.55\(\times 10^{-13}\) & 2.27\(\times 10^{-13}\) & 1.0000 \\
33 th Round & [PERSON] & 7.70\(\times 10^{-11}\) & 7.28\(\times 10^{-10}\) & 1.0000 & [PERSON] & 4.55\(\times 10^{-13}\) & 2.27\(\times 10^{-13}\) & 1.0000 \\
34 th Round & [PERSON] & 2.93\(\times 10^{-09}\) & 2.49\(\times 10^{-09}\) & 1.0000 & [PERSON] & 1.36\(\times 10^{-12}\) & 1.14\(\times 10^{-12}\) & 1.0000 \\
35 th Round & [PERSON] & 11.7169 & 9.0584 & 0.8144 & [PERSON] & 213.5905 & 201.4884 & 0.9923 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental Results for Both Infected and Fatality Cases from Different Regression Models.  
12,365-16,602 confirmed cases (see Figure 2f) and 191-211 fatality cases (see Figure 3f) from 7 to 13 May 2020.
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
###### Abstract  
This paper presents a compact vector quantizer based on the self-organizing map (SOM), which can fulfill the data compression task for high-speed image sequence. In this vector quantizer, we solve the most severe computational demands in the codebook learning mode and the image encoding mode by a reconfigurable complete-binary-adder-tree (RCBAT), where the arithmetic units are thoroughly reused. In this way, the hardware efficiency of our proposed vector quantizer is greatly improved. In addition, by distributing the codebook into the multi-parallel processing sub-blocks, our design obtains a high compression speed successfully. Furthermore, a mechanism of partial vector-component storage (PVCS) is adopted to make the compression ratio adjustable. Finally, the proposed vector quantizer has been implemented on the field programmable gate array (FPGA). The experimental results indicate that it respectively achieves a compression speed of 500 frames/s and a million connections per second (MCPS) of 28,494 (compression ratio is 64) when working at 79.8 MHz. Besides, compared with the previous scheme, our proposed quantizer achieves a reduction of 8% in hardware usage and an increase of 33% in compression speed. This means the proposed quantizer is hardware-efficient and can be used for high-speed image compression.  
image compression; vector quantization; self-organizing map; FPGA +
Footnote †: journal: Applied Sciences
## 6 Data Analysis
### Data Segmentation into Subsamples  
In the previous section, results of the ML algorithm's training were obtained by using the dataset with statistical values of the samples analyzed in their entirety. In this section, the data is sub-divided by creating and analyzing partitions of 2-s durations.  
The future application of this work is achieving that the driving environment can be accurately classified in almost real-time; the information generated is relevant for automated driving systems only if it is available and updated during real-time decision making, along with other information that is being processed from the vehicle's state.  
With this premise, the data from the original dataset was divided using discrete time-windows that contained information from 2 s of sampling and was subsequently processed with the statistical methods described in the beginning of this section to generate a new dataset. The division into small windows allowed for information of more events to be maintained; for example, if a driver maintains a constant speed for 20 s and then suddenly speeds up for another 10 s, a time-window of size 30 s will not be able to capture the two different events that took place in it ([PERSON] et al., 2009) [40].  
The graph in Figure 10 presents this analysis of division in windows, where the \(x\)-axis is divided into segments of 2 s. It can be seen how the average speed value of the entire sample (green line) differs from each of the average speed values of the subsamples  
Figure 9: Relationship of the characteristics obtained after the dimensional reduction.  
(red line), which is why it was decided for each test to be analyzed as a succession of discrete events.
## 4 Main Features in Wireless Sensor Networks Design
### Case Study: Environmental Monitoring  
In this section a low-cost hardware and software WSN test-bed developed for agricultural monitoring is described. The platform has been designed to provide the farmer with a periodic and punctual monitoring of physical parameters (e.g., temperature, air pressure, humidity) for a realtime control of different micro-climates in a cultivation. Thanks to this information it is possible to increase the quality and amount of production, cut costs, and reduce the pollution caused by weed-killers. The test-bed developed in cooperation with the start-up company SeNet s.r.l. has been working since 2006 in a peach field located in Italy. Each node is composed of an IEEE 802.15.4 radio transceiver based on Chipcon CC2430 operating in the 2.4 GHz band, photovoltaic panels, rechargeable batteries, temperature and humidity sensors. Each node emits a maximum power equal to 0 dBm and it is capable of communicating with neighbor nodes up to a maximum distance of 100 meters in line-of-sight conditions. The communication protocol has been designed to allow an extremely low duty cycle where nodes wake up for 10 seconds (activity mode) at intervals of 15 minutes. During the activity mode, each node collects measured data from its sensors and transmits the information to the sink node. In case the sink node is out of the radio link range, data are forwarded by intermediate nodes, which act as relays, in a multi-hop fashion according to a mesh network topology. To this purpose, a robust ad hoc network synchronization protocol has been designed to compensate relative clock drifts among nodes thus avoiding that nodes wake up in not overlapped time intervals. In sleeping mode, node consumption is only 0.5 mA, whereas during the activity mode the overall consumption is 30 mA. The resulting network lifetime is in the order of several weeks (in the absence of photovoltaic panels) and it is limited only by the rechargeable battery lifetime when photovoltaic panels are present. All sensed data collected by the sink node (coordinator) are then forwarded to the Internet every 2 hours through a GPRS link. The periodic report as well as each node status can be examined through a remote standard Internet connection. In Figure 3 an example of data report related to the temperature behavior measured in June 2006 is shown.  
Being energy efficiency one of the most important requirement for this application, we show the behavior of the battery status. In particular, in Figure 4 we show the battery charge in Volt as a function of time, expressed in hours, when photovoltaic panels are used and not. Two square panels with side of 10 cm are used.
## 3 Results and Discussions
### Antiviral Activity
The size distribution of colloids, as determined in our DLS studies, was 8-288 nm for nanostructured gold particles and 15-297 nm for gold-PVP nanostructured particles. These size distributions lie within the range of the sizes of the Orthomyxoviruses family to which H9N2 belongs [46]. These compatibilities in the size distributions of nanostructured gold particles, particularly in composite form, and the possible size of influenza viruses have provided an ideal platform to demonstrate biological interactions [8]. The supporting role of PVP in enhancing the inhibiting ability of nanostructured gold composites is also reflected in UV-Vis spectroscopic studies. The increase in the intensity peak with PVP association revealed the provision of the possible character of PVP as co-reducing agent [47] in addition to microplasma, resulting in the fabrication of more colloids and, hence, more interactions with the biological system and, hence, more antiviral activities [48]. In light of our present investigations, it was evidenced that gold nanostructured particles and nanostructured gold composites fabricated by an atmospheric pressure microplasma have been proven to exhibit a strong tendency of inhibiting the avian influenza H9N2 virus strain. These gold nanostructured particles are expected to equally fight multiple influenza virus strains with the same potential. This argument is well supported by the previous investigations with porous gold nanoparticles [9] and the effective inhibition of multiple influenza virus strains with anionic gold [49]. The mechanisms of antiviral activity of metallic nanostructures and their composites fabricated by any kind of physical [20], chemical [21], and biological routes [22] against multiple influenza virus strains may be hypothesized within the same pattern of action. These nanostructures adsorb to the virions spikes (viral envelope glycoproteins) by Van der Waals interactions [50]. This adsorption may disrupt the virus attachment to the cellular receptors, thereby inhibiting virus penetration to the host cell [51]. In light of already reported studies and our present investigations, attention must be paid to the further exploration of metal NPs in a nanocomposite form as antiviral agents that could be better alternatives to currently available antiviral drugs.
# Measures and Methods for the Evaluation of ATO Algorithms
## 3 Approach for ATO Testing  
A survey [34] as part of the _X2 Rail 1 (Shift2 Rail)_ project showed that 75 % of the responding companies in the railway sector are in favour of harmonizing test strategies. This includes suppliers as well as customers, namely infrastructure management companies, and regulative authorities.  
Defining a structured and general way of evaluating the performance of ATO algorithms is just one part of the overall necessity of harmonizing test strategies. In [35], [PERSON], gives a detailed overview of the process of software testing, and based on that different test properties are outlined.  
The envisaged test level of the approach is the system level. The approach can also be used for acceptance tests since one or multiple algorithms could be tested against certain data and criteria, e.g., provided or set by the customer, respectively.  
Test criteria are defining the scope of the test, in other words, what is the aspect getting tested. This can either be a functional, operational, or temporal aspect [35]. The envisaged performance tests are part of operational and temporal testing. They are intended to give an impression of how well the system under test can handle certain test scenarios with increasing complexity.  
[PERSON] is listing black-box, white-box, and grey-box testing as different test methods. For the performance evaluations of ATO algorithms, black-box testing is the appropriate test method because normally the customer does neither have insight into the specifics of the algorithm nor its implementation. Thus, defining test cases based on the algorithm itself is not feasible.  
These principles are the basis for defining a general test strategy. By incorporating the knowledge extracted during the literature review phase following major steps were identified:  
1. Defining indicators for performance measurement,
2. Finding a strategy to enable structured scenario definition,
3. Identifying ATO performance relevant scenarios.  
For further discussion, a clear definition of the term scenario in the context of simulating railway operation is beneficial. Therefore, the literature-based definitions for a scene and scenario in [36] can be transferred to the railway sector.  
**Scene:**: A scene captures the current state of the environment and all dynamic elements as well as relationships between them for one specific point in time. This includes static objects (rail network, signs, stations, etc.) and dynamic elements (trains, passengers, environmental conditions, etc.). Interactions and relations between all elements are static within a scene.
**Scenario:**: A scenario represents the temporal development of a sequence of scenes. The timespan of a scenario can vary. How the scenes and their elements change over time is influenced dynamically by external constraints.
## 3 Generic Routing Solutions
### Generic Routing Solutions vs. LLN Requirements
DYMO assessment shows some potential to meet the loss response criterion but requires precise specifications on how to meet the criterion while maintaining broken links. In contrast, DSR advertises unreachable destinations to the source node and passes the loss response criterion. Moreover, AODV and DSR fail both link cost and node cost criteria because they only use the hop count metric for path-finding. RIP mainly uses the hop count metric but also provides the option of using other routing metrics. However, using real-time metrics such as link quality in RIP may lead to network instability; therefore, RIP needs additional implementations to pass the link cost criterion. In DYMO, the distance of a link can vary from 1 to 65535; thus, some improvements are required to use the link metric efficiently and pass the link cost criterion. Furthermore, DYMO may have the mechanisms to use node properties but also require additional implementations in order to use them properly and pass the node cost criterion. The remaining DVRPs do not support any node properties as a routing metric and thus fail the node cost criterion. It should be emphasised that none of the protocols, LSRPs or DVRPs, satisfy all the five criteria. Table 3 summarises the general differences between LSRPs and DVRPs.
## 3 Results and Discussion
### Quality Analysis of NFC Goji Juice  
Physical, chemical, and nutritional quality analyses were conducted on freshly pressed goji juice and NFC goji juice produced under optimised processing conditions. The results are detailed in Table 8.  
The TSS/TA ratio significantly affects the sensory characteristics and consumer acceptance of juice [33]. As shown in Table 8, under optimised processing conditions, the TSS/TA ratio (12.80 \(\pm\) 0.1a) of NFC goji juice is significantly higher than that of fresh-pressed goji juice (11.91 \(\pm\) 0.2b) (\(p\) < 0.05). This is because high-temperature sterilisation during processing causes the evaporation of organic acids, reducing the TA content [37] and increasing the TSS/TA ratio in NFC goji juice compared to fresh-pressed juice. Turbidity is an important indicator of system stability; the higher the turbidity, the more stable the juice [34]. In this study, the turbidity of goji juice increased by 15% after processing compared to fresh-pressed juice. This increase is attributed to homogenisation and ultrasonic treatment, which reduce the size of large particles such as fats, fibres, and proteins, ensuring uniform dispersion and enhancing juice stability [35]. Colour is a key factor influencing consumer acceptance of juices. The L, a, and b values of NFC goji juice are lower compared to fresh-pressed juice, indicating a darker juice colour. The total colour difference \(\Delta\)E > 5 suggests significant changes due to homogenisation, ultrasonication, and sterilisation, likely caused by polyphenol oxidation and the Maillard reaction involving amino acids and reducing sugars during processing [34; 36]. The total phenolic content in processed NFC goji juice decreased by 23.5% compared to fresh-pressed goji juice, likely due to the degradation of phenolic substances under high-temperature treatment [36]. Conversely, the contents of \(\beta\)-carotene, polysaccharides, and betaine in NFC goji juice increased by 1.73 mg/mL, 2 mg/mL, and 0.127 g/100 g, respectively, compared to fresh-pressed goji juice. This increase is due to processing steps like homogenisation, ultrasonication, and sterilisation, which disrupt the cellular matrix and facilitate nutrient release [36; 38]. Both freshly pressed goji juice and NFC goji juice had a total colony count of <10 CFU/mL, and no _E. coli_ was detected, meeting national standards. Therefore, the quality of processed NFC goji juice was superior to that of freshly pressed juice.