esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Results and Discussion
### Comparison of the Data Formats
In fact, DSPs are minimized from 14 of the double configurations to 0 and 1 of the 32-bit fixed-point and 16-bit fixed-point configurations, respectively. In FPGA, the number of DSPs is crucial because they are in the order of hundreds, while LUTs and FFs are in the order of hundreds of thousands. Therefore, the low utilization of resources in fixed-point representations allows the FPGA to implement additional pre-processing steps on the fly. The best result comes from the 32-bit fixed point; this configuration is the only one that utilizes 0 DSPs and implements all operations with LUTs and FFs. Therefore, even if it utilizes more LUTs than 16-bit fixed-point, we can confirm that 32-bit fixed-point is the data format with the best performance in terms of resource utilization concerning the FPGA available resources. The 32-bit fixed-point has better performance than the 16-bit fixed-point due to the fact that Vitis(tm) HLS optimizes the 32-bit fixed-point format in the provided libraries. Finally, we analyzed the execution time in the different cases. The results in Table 5 show the advantage of using fixed-point representation compared to floating-point representation. In fact, 16-bit and 32-bit fixed-point configurations are 2.2\(\times\) and 4\(\times\) faster than the single-precision floating-point configuration, respectively. The conversion between the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resource Name** & **Half** & **Float** & **Double** & **Fixed 16** & **Fixed 32** \\ \hline BRAM 18k & 0 & 0 & 0 & 0 & 0 \\ DSP & 4 & 5 & 14 & 1 & 0 \\ FF & 382 & 527 & 1292 & 167 & 309 \\ LUT & 223 & 785 & 1796 & 941 & 1245 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Resource utilization of different data format configurations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resource Name** & **Half** & **Float** & **Double** & **Fixed 16** & **Fixed 32** \\ \hline MSE & 2.39 \(\times\) 10\({}^{-7}\) & 3.21 \(\times\) 10\({}^{-15}\) & 0 & 0.22 & 0.0039 \\ CNR Supra-Slice 1.0\% & 0.555 & 0.566 & 0.566 & 0.335 & 0.536 \\
[AHU] & 5.8 & 5.7 & 5.7 & 2.7 & 5.3 \\ Uniformity [AHU] & 3.54 & 3.57 & 3.57 & 2.44 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Image quality estimation of different data format configurations. The 32-bit fixed point is the best compromise between image quality and hardware preference. It maximizes the hardware performance, and the image quality after the reconstruction decreases only 7% in comparison with the double format. In contrast to [14], we do not consider the required external memory bandwidth for the different configurations because we are interested in finding only the best data format configuration in the pre-processing step. In this step, our CT pre-processing core processes projections on the fly and does not use any external memory, which usually is the main bottleneck GPU and CPU solutions.
## 4 Results
### Comparison of Mudstone Liquid/Plastic Limit Modification
#### 4.1.2 Changes in the Liquid/Plastic Limit of the Modified Mudstone  
As a fundamental property of mudstone, the liquid/plastic limit is extraordinarily sensitive to internal factors. The cementation and electrophoresis produced by electrochemical modification increase the plasticity index of the mudstone to a certain degree because the introduced ions affect the particle size composition and chemical composition of the  
Figure 3: The experiment and results of the combined determination method of Atterberg limits. **(a)** Three moisture content samples; **(b)** test results.  
soil. Figure 4 shows the joint test results to find the limit between liquid and plastic for modified specimens.  
At a cone depth of 2 mm, the difference in water content between Figure 4a,b is less than 2%, indicating that the experimental data are reliable. After the addition of 10% AlCl3 solution, the water content of the unmodified specimen was 27.322% of the plastic limit and 46.727% of the liquid limit. After modification, the water content of the plastic limit is 22.691%, and the water content of the liquid limit is 32.489%.  
The addition of the AlCl\({}_{3}\) solution significantly improved the liquid limit water content of the soil because polymeric AlCl\({}_{3}\), as a flocculant, can hydrolyze with water and produce a certain degree of aggregation of soil particles, resulting in the formation of large soil particles and water-stable agglomerates to improve the soil's water holding capacity. The liquid limit moisture content of the modified sample has decreased to a certain extent. The plastic limit has slightly increased, which is caused by the dual effects of ion introduction and electrochemical reaction.
## 4 Simulation and Analysis of Magnetic Field Distribution
### Magnetic Field Generated by Sideband Current and Harmonic Current  
#### 4.3.1 Sideband Current  
With the help of \(LCC\), the coupling coils resonate at a specific frequency of 85 kHz. At the specific frequency, energy can be transferred with a higher efficiency. However, the resonant frequency fluctuates at a small range when the system works. Thus, a lot of sideband currents around the resonant current exist that can affect the magnetic field.  
The corresponding current spectrum with the frequency step 200 Hz in the primary and secondary coil is obtained as shown in Figure 17. The current reaches a peak at a frequency of 85 kHz and a large amount of sideband currents exist around 85 kHz. The sideband currents on the primary side are much more than the secondary side, and the current's amplitude is greater at the same frequency.  
The harmonic of primary and secondary side at 84.8 kHz and 85.2 kHz are respectively extracted and injected into the Maxwell, and the magnetic field distribution is obtained as shown in Figure 18 and the results are recorded in Table 7. For A1 located in the working region, the magnetic field strength at A3 is the highest among these test points, reaching 1.75 A/m and 1.76 A/m at 84.8 kHz and 85.2 kHz. From the previous analysis, the magnetic field strength at the risk point A3 has reached 14.15 A/m. Thus, when considering the superposition of the magnetic field generated by the sideband currents, the total magnetic field strength at A3 will be slightly larger than the simulated result, and the risk of exceeding the limit will increase, which needs to be taken seriously. Then, the magnetic generated by current at sideband frequency should not be ignored in real practice.
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 4 Limitations and Challenges
### Instrument and Operational Limitations  
The acquisition and maintenance of MALDI-TOF MS instruments entail significant capital investment, typically exceeding EUR100,000, which may be prohibitive for small or resource-limited laboratories. Furthermore, the need for trained personnel, regular calibration, and adherence to strict quality-control protocols are essential, to ensure reproducibility and reliability of results [36; 72; 143].  
Additionally, although operational costs are low per sample, long-term return on investment depends on high sample throughput and consistent demand. For occasional or small-batch testing scenarios, the economic advantages may be less compelling.
## 2 Materials and Methods
### Procedures
#### 2.3.3 Velocity Measurements  
The MV was assessed using a linear position transducer attached to a free-weight barbell (Speed4 Lifts v.1, SPEED4 LIFTS, Madrid, Spain), during the same procedure as the 1 RM evaluation. The MV was measured at intensities of 80%, 85%, 90%, and 95% of the estimated 1 RM of the pre-test. This approach ensures consistency by using a standardized baseline load, as recalculating percentages based on post-intervention 1 RM would introduce variability unrelated to the intended evaluation of relative improvements in movement velocity. As an increase in velocity at a consistent absolute load is widely acknowledged as an indicator of strength improvement [35; 36; 37], these percentages were determined during the same sets used to estimate the direct measurement of 1 RM.  
Data were transmitted wirelessly to a smartphone application for real-time load-velocity profiling. The Speed4 Lifts device has been validated against devices such as the T-Force System (T-FORCE System, ERGOTECH Consulting SL, Murcia, Spain) and gold-standard Trio-Optitrack (V120, NaturalPoint, Inc., Corvallis, OR, USA) for accuracy in measuring barbell velocity during bench press exercises. In the bench press exercise performed on a Smith machine, the Speed4 Lifts demonstrated high correlations in MV with the Trio-OptiTrack (r = 0.994), confirming its suitability for measuring lifting velocity due to its high precision, user-friendliness, and affordability [38].
## 3 System Description
### Statistical Parameters for Assessment of Illuminance and SNR Uniformities  
The evaluation of uniformity of illuminance is mainly based on two unitless statistical quantities, namely, uniformity (\(U_{o}\)) and interquartile range (IQR). \(U_{o}\) is the ratio of minimum (\(E_{min}\)) to the averaged illuminance value \(\overline{\text{E}}\) over \(N\) points, as given by:  
\[U_{o}=E_{min}[\frac{1}{N}\sum_{i=1}^{N}(E_{i})]^{-1}=\frac{E_{min}}{\overline {\text{E}}} \tag{20}\]  
where \(E_{i}\) is the illuminance value at the \(i_{th}\) point on the receiving plane. Here, the values of \(U_{o}\) approaching 1 represent uniform illumination where, in general, the desired value is \(\geq\) 0.7 [18]. Besides, in order to analyze the spread of illuminance, the interquartile range (IQR) of simulated data has also been considered. IQR is a statistical quantity that measures the dispersion of data by calculating the difference between its first and third quartiles, i.e., \(3(N+1)/4-(N+1)/4\). A high IQR value reflects a larger spread and vice versa.  
On the other hand, the reduction in SNR fluctuations is evaluated using the standard deviation (\(\sigma\)). It is a statistical quantity that measures the dispersion of a data set relative to its average value (\(\mu\)) and is given by:  
\[\sigma=\sqrt{\left(\frac{1}{N-1}\sum_{i=1}^{N}|X_{i}-\mu|^{2}\right)} \tag{21}\]  
where \(X_{i}\) is the quantity being evaluated (e.g., \(P_{r}\) or \(SNR\)) and \(N\) is the number of points in the dataset. Ideally, the \(\sigma\) values should be close to zero.
## 2 Preliminaries  
### System Configuration  
In an incomplete resource aggregation environment, the overall framework of the multi-market players participating in the day-ahead transaction model among VPP, SES, PVP alliance, and the outer power grid is shown in Figure 1.  
As shown in Figure 1, VPP aggregates large-scale centralized renewable energy generation systems, gas turbines (GT), gas boilers (GB), power-to-gas (P2G), and carbon capture and storage (CCS) devices. The PVP alliance consists of multiple PVP members, mainly referring to electricity users who have distributed photovoltaic power generation nearby. The PVP load consists of flexible electric and thermal loads and typical rigid loads, and it can prioritize the use of photovoltaic power generation to meet its own needs. SES participates in transactions as a separate market entity.
## 4 Experiment
### Experimental Results
#### 4.2.2 Experiment 2: Comparison with Single Classifiers  
To further validate the versatility of the two CTS algorithms, we continued to compare them with several common individual classifiers in supervised learning on the same datasets and recorded their performance. Tables 6 and 7, respectively, present the accuracy and F1 scores of each method across the datasets, with the table settings consistent with Table 4. It is evident that the CTS algorithm achieved the highest accuracy on five datasets, while the CTS-SR algorithm also attained the highest accuracy on two datasets. Together, the two algorithms achieved the highest F1 scores on three datasets. Their performance is significantly superior to that of individual machine learning classifiers. This demonstrates the broad superiority of the strategies we have proposed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Accuracy (\%)** & **CTS** & **CTS-SR** & **Major** & **RF** & **AdaBoost** & **HMTS** \\ \hline Australian & 86.95 & 87.24 & 86.95 & 84.92 & **88.69** & 83.18 \\ German & **75.6** & 74.2 & 74.8 & 71.4 & 72.6 & 72.2 \\ spam & **94.08** & 89.56 & 93.61 & 92.95 & 90.83 & 92.78 \\ biodeg & 85.98 & **87.12** & 86.36 & 83.52 & 81.25 & 84.84 \\ magazine & **81.04** & 80.53 & 80.28 & 78.60 & 75.82 & 77.84 \\ all-beauty & 85.82 & **86.61** & 85.43 & 82.67 & 81.10 & 85.03 \\ musk & **84.13** & 81.43 & 80.83 & 80.23 & 75.44 & 83.53 \\ Hilly & **55.94** & 50.82 & 51.65 & 54.62 & 48.18 & 55.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of accuracy among different ensemble algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**F1 Score (\%)** & **CTS** & **CTS-SR** & **Major** & **RF** & **AdaBoost** & **HMTS** \\ \hline Australian & 84.94 & 85.23 & 84.84 & 82.31 & **86.59** & 79.16 \\ German & **49.58** & 37.07 & 46.15 & 45.69 & 42.67 & 48.52 \\ spam & **92.08** & **84.99** & 91.71 & 90.96 & 88.24 & 92.07 \\ biodeg & **79.89** & **80.34** & 79.77 & 75.63 & 74.01 & 78.60 \\ magazine & **87.32** & 82.65 & 87.28 & 85.63 & 84.39 & 84.64 \\ all-beauty & **82.52** & **82.52** & 82.46 & 78.43 & 75.75 & 80.80 \\ musk & **81.27** & 76.69 & 76.74 & 75.73 & 73.02 & 80.96 \\ Hilly & 59.48 & 57.53 & **65.03** & 56.69 & 64.23 & 57.63 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of F1 score among different ensemble algorithms.
# SPOT: Testing Stream Processing Programs with Symbolic Execution and Stream Synthesizing
## 5 Case Studies
### How Reordering Situations Can Be Covered  
Our fourth case study demonstrates how PBT-based approaches can cover different stream reordering situations. We chose TaxiDistance as the subject program and generated test data with DiffStream.  
For TaxiDistance, we adopted its original version (i.e., it consists of _project_ operator and _keyBy_ only). Since there was no internal window or other built-in mechanisms to divide streams into groups, we manually split the data series following a count-based method, i.e., every \(N\) (we set \(N\) as 7, 8, 9, and 10 separately in our experience) consecutive data were placed into one group. Then, we got various data fragments that were ordered or were out of order. We calculated entropy for each of these fragments and analyzed the distribution of entropy values. We generated eight sets of test data ranging from 500 to 4000 for each window setting. We calculated the reordering density and entropy for each group. Results are shown in Figure 10. The \(x\)-axis denotes the entropy value of any fragment of series data in one group. The \(y\)-axis represents each experience that we took with the test data ranging from 500 to 4000. The \(z\)-axis indicates the discrete density of the fraction of entropy, i.e., the percentage of the number of fragments that share the same entropy.  
Figure 10: The distribution of entropy.  
We can conclude from the results that the distribution of entropy was not even in each experience. In particular, the fragments with large entropy (e.g., larger than 1.4 in Figure 10a) were in an absolute majority. There were few fragments with litter entropy. There were no fragments whose entropy was below some small value (0.4 for Figure 10a, 1.0 for Figure 10b, 0.9 for Figure 10c, and 1.0 for Figure 10d). These results expose the limitation of data generation approaches in PBT-based solutions in terms of the entropy distribution. This forces us to think about how to explicitly generate a test stream to cover as many reordering scenarios as possible. In our study, SPOT can potentially generate an possible reordering scenarios, and it will give out one stream for each reordering situation. Thus, our method can evenly produce test data.  
In summary, although the random strategies in PBT-based methods generate individual data in an even distribution, the reordering entropy values are imbalanced when splitting them into various time-series fragments by grouping a few data nearby. Streams with small entropy values are seldom produced by these random generation methods. This, in turn, forces a requirement for our research to present SPOT to cover as many reordering situations as possible.
## 3 Results
### Verification of FY3C/GNOS IRO Products in Ionospheric Climatology with COSMIC
#### 3.2.1 Ionospheric Climatological Characteristics of NmF2 between FY3C/GNOS and COSMIC
With the enrichment of the global IRO data, many studies [62; 63; 64] found that the WSA is not limited to the Weddell Sea in summer ionosphere but also occurs in mid-latitude longitude sections in both northern and southernsummer ionosphere, where the magnetic equator shifts farthest toward the geographic pole, this is called the general WSA as denoted by the red arrows in Figure 7b,d and Figure 9b,d. It is not hard to see that general WSA in northern summer ionosphere is not as evident as that in southern summer ionosphere. It can be seen from Figure 6e,f, Figure 7e,f, Figure 8e,f and Figure 9e,f that at nighttime, the NmF2 of FY3C/GNOS are higher than those of COSMIC in mid-high latitudes but lower in low latitudes, especially in equinoxes. The white area (relative error within \(\pm\)10%) occupies the vast majority of the low-mid latitude regions, showing that NmF2 morphology between FY3C/GNOS and COSMIC are more consistent in these areas. Figure 6: Ionospheric climatological characteristics of season median NmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100 LT and 2000–2300 LT during ME-month (\(\pm\)45 days to March equinox of 2016) from 2016.035 to 2016.125. (**a,b**) The climatological characteristics of season median NmF2 observed by FY3C/GNOS at 0800–1100 LT and 2000–2300 LT, respectively. (**c,d**) The climatological characteristics of season median NmF2 probed by COSMIC at 0800–1100 LT and 2000–2300 LT, respectively. The dip contours in (a–d) are represented by white curves, which are 60\({}^{\circ}\) dip, 0\({}^{\circ}\) dip, -60\({}^{\circ}\) dip from top to bottom, respectively. (**e,f**) The relative errors of season median NmF2 between FY3C/GNOS and COSMIC at 0800–1100 LT and 2000–2300 LT, respectively. The acquisition of NmF2 relative error can be seen in Section 2.2. Figure 8: Ionospheric climatological characteristics of season median NmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100 LT and 2000–2300 LT during SE-month (\(\pm\)45 days to September equinox of 2016) from 2016.221 to 2016.311. Refer to Figure 6 for more detailed explanations. Figure 7: Ionospheric climatological characteristics of season median NmF2 observed by FY3C/GNOS and COSMIC and their differences at 0800–1100 LT and 2000–2300 LT during JS-month (\(\pm\)45 days to June solstice of 2016) from 2016.128 to 2016.218. The red arrow points to the diurnal variation of NmF2, in which the nighttime NmF2 enhancement, namely, the general Weddell Sea anomaly (WSA) can be clearly observed. Refer to Figure 6 for more detailed annotations.
# In-Plane Monolithic Integration of Scaled III-V Photonic Devices
## 5 Conclusions  
In this work, we first provided a brief introduction to different monolithic III-V on Si integration techniques, with a focus on the TASE technique developed by our group. We presented in-plane integrated photonic devices which make use of the benefits of the innovative TASE platform. This represents an extension to our electronic devices for which we originally developed these techniques, but we exploit the same underlying technique for the integration of in-plane homo- and heterojunctions. We believe that this opens up new opportunities for co-integration between active and passive elements. In particular, the ability to integrate III-Vs locally on silicon and within silicon features in a self-aligned manner enables novel paradigms for device design. In this way, we can exploit silicon for what it does best--ease of processing and low optical losses in the NIR for transmission and passives--while adding the III-V only and exactly where we need it.  
For an integrated platform, electrical and not optical actuation should be the goal. We have demonstrated electroluminescence for the forward-biased \(p\)-\(i\)-\(n\) detectors, which contain the same materials as the so far optically pumped PhC cavities. In fact, both emitters and detectors can be fabricated in the same step with TASE. With the added gain material within a PhC cavity, we show a path towards efficient and tailorable light emitters, covering the entire telecom bands. Additionally, the ability to play around with optical loss and gain at will as well as mixing different III-Vs on the same wafer will also allow for making more exotic devices--for example, exploiting topological effects [45, 46].  
All the authors created the device concepts, where [PERSON] and [PERSON] have led the initial development of TASE. [PERSON], [PERSON], [PERSON], [PERSON] fabricated the samples supported by [PERSON] for MOCVD growth. [PERSON] and [PERSON] performed FDTD simulations. [PERSON], [PERSON], [PERSON], [PERSON] characterized the samples and analyzed data. [PERSON] and [PERSON] led the projects. [PERSON] and [PERSON] prepared the original draft, all the authors contributed to this manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by H2020 ERC project PLASMIC, grant number 678567, Swiss National Science Foundation Spark project SPILA, Grant CRSK-2_190806, and a Korean-Swiss joint program, SNF grant No. 188173 and KNSF grant 2019K1A3A1A14064815,2020R11A3071811.  
The data presented in this study are openly available in Zenodo at [[http://doi.org/10.5281/zenodo.4550529](http://doi.org/10.5281/zenodo.4550529)]([http://doi.org/10.5281/zenodo.4550529](http://doi.org/10.5281/zenodo.4550529)) (accessed on 21 February 2021).  
The authors gratefully acknowledge [PERSON] for TEM analysis, [PERSON] for chemical mechanical polishing (CMP), and the Binnig and Rohrer Nanotechnology Center staff for technical support.  
The authors declare no conflict of interest.