esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of Bacteria-Specific Antibodies  
Antibodies (Ab) against surface and intracellular _P. aeruginosa_ bacteria antigens were determined. For this assay, _P. aeruginosa_ bacteria (surface Ag) and bacterial lysate (intracellular Ag) were used. The mice sera samples were obtained at day 0 and day 7. A small volume of blood (no more than 200 uL) was collected from the tail veins of the mice and the extracted serum was used for the detection of bacteria-specific antibodies. Bacterial cell suspension (prepared according to Section 2.3 and re-suspended in PBS) was treated under UV for 30 min. Bacterial lysates were prepared cells subjected to liquid nitrogen and then a 37 \({}^{\circ}\)C water bath; the procedure was repeated 10 times. For the determination of antibodies, 96-well plates (Nunc MaxiSorp, Thermo Fisher Scientific, Waltham, MA, USA) were used. Each well received 50 uL of the appropriate bacterial suspension or lysate (equivalent to 8 \(\times\) 10\({}^{5}\) bacteria per well). The coated plate was incubated at 4 \({}^{\circ}\)C overnight. Each well was blocked using 150 uL of 10% fetal bovine serum (FBS) for 1 h at room temperature (RT) with shaking. The bacteria-coated wells were incubated with diluted murine serum (1:100) for 1 h at RT with shaking. Mixtures of biotin-labeled antibodies to mouse IgM (BD Pharmingen, San Jose, CA, USA), IgG1, IgG2a, IgG2b, IgG3 (BD Pharmingen) were used (1 h. RT). Then, 50 uL of streptavidin-horseradish peroxidase conjugate (BD Pharmingen) was added, followed by 30 min incubation at RT with shaking. Bound conjugate was detected colorimetrically using 100 uL of o-phenylenediamine dihydrochloride (OPD) substrate containing 0.025 M NaCOOCH\({}_{3}\) and 100 uL 35% H\({}_{2}\)O\({}_{2}\). The color reaction was stopped by adding 50 uL of 2 M H\({}_{2}\)SO\({}_{4}\). The plates were washed 3 times with phosphate-buffered saline with Tween (PBS-T) between each step. The optical density was read at 492 nm with a Synergy 2 microplate reader and Gen5 (v. 1.04.05) software (Biotek, Shoreline, WA, USA). The background level was subtracted from the obtained optical density values and the relative units (RU) were calculated. The antibody levels of mice after treatment (PEF + AA or AA only, n = 8 in each group) were normalized to the antibody levels of mice before the treatment (n = 6; RU = 100). Controls included the following: non-coated wells, bacteria-coated wells with PBST instead of serum and wells with no conjugate.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 3 RF Breakdown
### Breakdown Rate  
In the course of studies of high-gradient acceleration for NLC/JLC linear colliders, I found that if rf pulses of the same shape and amplitude are applied to the accelerating structure, then the probability of rf breakdown (or the number of breakdowns per number of rf pulses) is practically constant or changes slow. This result is more or less independent of the conditioning algorithm and the type of structure (SW or TW).  
I show a typical example for the quantity of accumulated breakdowns vs. pulse number for a short SW cavity [31] in Figure 1a. The number of accumulated breakdowns follows a linear fit, the slope of which is proportional to the breakdown rate. This is how almost every accelerating structure I have tested behaves.  
Further, I show the histogram of the number of pulses between subsequent breakdowns in Figure 1b. The histogram shows an exponential distribution, which means that events happen continuously and independently. Meanwhile, unlike in SW cavities, in TW structures the rf breakdown statistics are better described by a double-Poisson distribution [67; 68], where a short-time-scale distribution is associated with post-breakdown damage and the longer-time-scale distribution with the breakdown trigger. Since the physics of the interaction between the breakdown currents and electromagnetic fields stored in SW structures limits the degree of post-breakdown damage, the longer-time-scale Poisson distribution dominates the breakdown probability for the SW structures.  
Figure 1: An example of typical experimental breakdown data in which the rf power and rf pulse shape remain constant: (**a**) the number of accumulated breakdowns vs. the number of pulses, (**b**) the histogram of the number of pulses between successive breakdowns with a fit to an exponential distribution.  
Since the importance of the breakdown rate was understood two decades ago, it became common to present test results of high-power accelerating structures as _the breakdown rate map_ or dependence of the breakdown probability rate on rf pulse length or, more generally, on rf pulse shape [31; 69]. The use of these breakdown rate maps to characterize the high-gradient performance of the structures lets us avoid such non-precise terms as \"gradient limit\" or \"breakdown limit\".
## 1 Introduction  
The last two decades have known an exponential growth in the demand for optical network bandwidth. Since single-mode fiber (SMF) systems are approaching the nonlinear Shannon limit, space-division multiplexing (SDM) holds the promise to increase the capacity of future optical transmission systems [1, 2]. SDM can be realized either by few-mode fibers (FMFs) that allow the propagation of more than one spatial mode or multi-core fibers (MCFs) where each core can be single mode or few-mode [3]. In all schemes, cross-talk is inevitable especially if cores are close in MCFs or if the differential modal group delay (DMGD) is close to zero in FMFs. In both cases, it is compulsory to use multiple-input multiple-output (MIMO) approaches (already used in wireless communications) to recover the signals. In our work, we are interested in FMF sysystems. The impact of DMGD and unitary modal coupling in FMFs is very important to determine the MIMO equalizer complexity at the receiver [4, 5]. Moreover, in FMF systems, propagating modes can also be affected by a non-unitary cross-talk known as mode-dependent loss (MDL) that can be either distributed through the fiber or local at the insertion of optical components. MDL arising from the fiber line is due to manufacturing imperfections such as splices or micro-bends. Accumulated splice losses as low as 0.03 dB may lead to system outage after only a few hundred of kilometers [6]. Another source of MDL comes from optical components such as multiplexers/de-multiplexers (MUX/DEMUX) [7, 8] and few-mode optical amplifiers [9, 10].  
To mitigate the MDL effect, different optical and digital signal processing (DSP) solutions were proposed [11; 12]. The availability of information about the FMF channel at the transmitter known as channel state information (CSI) is an important factor that governs the capacity of FMF systems impaired by MDL. In the absence of CSI, all modes are excited with equal launch power which results in capacity reduction when modes have different attenuations. However, with the availability of CSI at the transmitter, capacity can be improved by allocating optimal powers to spatial modes [12]. Additionally, strong mode coupling was reported to reduce the accumulated MDL and hence results in a better system capacity [11]. Moreover, modal coupling can be intentionally added at local points thanks to mode scramblers [13].  
In this work, we investigate the FMF channel capacity impaired by MDL. First, we review our proposed statistical CSI that improves the channel capacity independently of channel variations [14]. Afterwards, we present a deterministic scrambling strategy to better average losses between spatial modes and reduce the accumulated MDL. Finally, we show that mode scrambling and the statistical CSI can be used together to reach higher capacities.
# Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot
## 1 Introduction
Some examples of these controllers are: the intelligent medication dispenser, which is based on the analysis of the data flow from a patient's body temperature [13]; a robust fuzzy algorithm to analyze asthma data [14]; the use of terms and linguistic expressions to capture the human way of reasoning at different accuracy levels [15]; clustering algorithm for wireless sensor networks with five input parameters (energy, centrality, distance to base station, number of hops and node density), which is fault tolerant [16]; selection and tuning of control law parameters to pilot an Unmanned Aerial Vehicle (UAV) [17]; stabilization of an inverted pendulum on a car [18]; general scheme for controlling non-linear, uncertain and chaotic MIMO systems [19; 20]; adaptation of a classic PID controller through an expert system based on fuzzy rules [21]; control of uncertain strict-feedback non-linear systems, with asymmetric output restrictions that vary over time in the presence of input saturation [22]; type-1 fuzzy logic-based controller for push recovery by humanoids [23]; Adaptive Neuro Fuzzy Inference System (ANFIS) controller for navigation of single as well as multiple mobile robots in highly cluttered environments [24], etc. In robotics, control system algorithms based on fuzzy logic are usually calculated using information related to position and speed, which is obtained by sensors located in each of the robot joints (encoders, tachometers, etc.) [25]. The main scientific advances that employ position and speed, _i.e._, error and first derivative of the error with respect to time, as linguistic variables of a control system based on fuzzy logic are: (1) Hybrid control systems, comprising force/motion, force/position and fuzzy-PID; (2) Fuzzy adaptive control with obstacle avoidance and dynamic uncertainty; (3) Fuzzy adaptive control for efficient surfing of mobile robots, whose main contribution is following trajectories and avoiding obstacles; (4) Fractional order fuzzy controllers; (5) Adaptive fractional order PID controller; (6) Adaptive control methods based on PID architectures to control highly non-linear industrial processes, even under conditions of parameter variability, noise and instabilities [1; 7; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36].
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 5 Quality of Code Observation
### Mp
In Figure 13, at the first frequency point B1C (L1/E1) and the second frequency point, B2a (L5/E5a) for multiple GNSS interoperability, two MP observations are formed, namely MP12 and MP21. The values of the MP12 combination are relatively close, and MP21 has the same conclusion. No matter which GNSS system, the MP21 value is significantly lower than MP12. In order to quantitatively analyze the MP value of the GNSS satellites, the MP statistics of different GNSS satellites are shown in Table 15. It can be found that the MP12 and MP21 values of different GNSS are lower than 0.3 m, while MP21 is significantly lower than MP12 (except for E13 satellites). In short, the MP values of different GNSS satellites at the same frequency are very closely. MP reflects the multipath noise characteristics of the GNSS signal. The multipath noise of MP12 and MP21 on BDS-3 B1C/B2a signals is about 0.5 m lower than traditional MP12 and MP21. In high-precision dual-frequency positioning, the combination of B1C and B2a is a better choice. In multi-system applications, the noise level of the combination of B1C and B2a and the combination of L1/E1 and L5/E5 is very close, and the multi-GNSS combination can be used in interoperability applications. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Satellite**} & \multirow{2}{*}{
\begin{tabular}{c} **Elevation** \\ **(Average)** \\ \end{tabular} } & **MP13** & **MP31** & **MP12** & **MP21** \\ \hline \multirow{4}{*}{**BDS-2**} & average & 32.41 & 0.36 & 0.22 & & \\  & C11 & 24.82 & 0.31 & 0.23 & & \\  & C12 & 25.36 & 0.33 & 0.23 & & \\  & C14 & 47.05 & 0.45 & 0.20 & & \\  & average & 39.33 & 0.24 & 0.17 & 0.17 & 0.14 \\  & C19 & 46.03 & 0.29 & 0.20 & 0.18 & 0.13 \\  & C20 & 35.78 & 0.29 & 0.23 & 0.20 & 0.18 \\  & C21 & 44.54 & 0.19 & 0.16 & 0.15 & 0.12 \\  & C22 & 43.58 & 0.20 & 0.15 & 0.18 & 0.15 \\  & C23 & 48.82 & 0.18 & 0.12 & 0.15 & 0.13 \\  & C24 & 40.28 & 0.21 & 0.13 & 0.17 & 0.14 \\  & C25 & 35.58 & 0.25 & 0.16 & 0.18 & 0.16 \\  & C26 & 54.39 & 0.19 & 0.14 & 0.14 & 0.10 \\  & C27 & 45.17 & 0.20 & 0.13 & 0.15 & 0.16 \\  & C28 & 44.87 & 0.19 & 0.12 & 0.15 & 0.11 \\  & C29 & 45.47 & 0.19 & 0.13 & 0.16 & 0.12 \\
**BDS-3** & C30 & 43.35 & 0.23 & 0.16 & 0.17 & 0.16 \\  & C32 & 30.87 & 0.26 & 0.16 & 0.18 & 0.15 \\  & C33 & 32.20 & 0.26 & 0.17 & 0.17 & 0.13 \\  & C34 & 22.87 & 0.28 & 0.20 & 0.21 & 0.17 \\  & C35 & 43.24 & 0.21 & 0.15 & 0.15 & 0.13 \\  & C36 & 29.44 & 0.28 & 0.19 & 0.19 & 0.17 \\  & C37 & 46.37 & 0.26 & 0.19 & 0.17 & 0.15 \\  & C41 & 25.93 & 0.32 & 0.22 & 0.21 & 0.17 \\  & C42 & 51.39 & 0.20 & 0.14 & 0.15 & 0.12 \\  & C43 & 35.44 & 0.22 & 0.15 & 0.16 & 0.13 \\  & C44 & 31.37 & 0.26 & 0.17 & 0.17 & 0.16 \\  & C45 & 32.36 & 0.34 & 0.19 & 0.19 & 0.15 \\  & C46 & 34.68 & 0.34 & 0.22 & 0.21 & 0.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Statistical results of MP values of all MEO satellites.
## 2 Materials and Methods  
### Simulation Model and Parameters  
The experimental tests of the simulation aim to improve the 5G performance by decreasing the PAPR and BER. The SLM-UFMC is proposed to find the optimum wave with the lowest PAPR and CM levels. Afterwards, three existing waveforms are tested via simulation program and the results are compared with the SLM-UFMC. Figure 3 represents the main steps that are used to implement the simulation of the SLM-UFMC and the other waveforms. The simulation model consists of five main modules. Firstly, the 5G environment Module A is employed to create simulation environments. This module integrates the main characteristics of 5G, such as data rate, packet size and a number of devices. Module B represents the implementation of the SLM-UFMC model and the existing UFMC [11] and SLM-CP-OFDM [20] models. In this module, several windows have been included to address the progress of the solutions. In Module C, different numbers of QAMs (e.g., four, eight, 16, 64, 256) are employed to generate testing scenarios with various states to comprehensively assess the performance of the proposed model compared with the tested waveforms. The scenarios are altered in each set of runs of a particular waveform. In Module D, the performance metrics of CCDF and BER are implemented to evaluate the SLM-UFMC, SLM-CP-OFDM, and conventional UFMC. Finally, Module E depicts the results and visualization graphs in a windows form.
## 6 Conclusions  
In this paper, we address the AMR task for class imbalance in the low-data regime from the perspective of extending the original data distribution. We do not follow the traditional idea of rebalancing the classes when the AMR tasks face both low-data and imbalanced-class regime problems, because this would lead to severe overfitting of the model, and the performance of different approaches varies widely across different datasets and deep neural networks. As a result, we propose a more fundamental data augmentation approach to address the problem of insufficient data and unbalanced classes. To augment the data online, we propose SigAugment, a practical and effective data augmentation strategy. Based on the 2016A dataset, we created four sub-datasets and thoroughly tested the algorithm on three representative SOTA models. The experimental results validate the effectiveness of the proposed method.  
SigAugment can be seamlessly integrated into existing data generation pipelines with just one line of code, and the algorithm can be easily extended by researchers. We hope that the proposed method can be validated and applied in more practical scenarios.  
Conceptualization, S.W. and Z.W.; methodology, S.W.; software, S.W.; validation, S.W.; formal analysis, F.L.; resources, Z.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, H.M., S.W., and Z.S.; visualization, F.L., Z.L., and H.M.; supervision, F.L., H.M., and Z.S.; project administration, H.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key R&D Program of China Grant No. 2022 ZD0115300 and the Scientific Research Plan of the National University of Defense Technology under Grant No. ZK20-38.  
Not applicable.  
Data Availability Statement: Data are available on request.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **DAE** & **SE-MSFN** & **LSTM2** \\ \cline{2-4}  & **Focal Loss** & **Lee** & **Polynom\_fit\_SMOTE\_poly** \\ \hline Baseline & 56.97 & 57.59 & 60.22 \\ Combination method & 57.91 & 24.30 & 67.91 \\ SigAugment-R & 60.16 & 70.93 & 72.61 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the proposed SigAugment method combined with the class rebalancing methods trained on the 2016A-50 dataset.
## 5 Copper Nanoparticles and Their Antibacterial Role  
Major health organizations such as The World Organization for Animal Health, the Food and Agriculture Organization and the World Health Organization agree on the growing threat posed by antimicrobial-resistant organisms. Taking into account that antimicrobial drugs are widely used, the rate of antimicrobial resistance is continuously growing [56].  
Research has shown that resistance to antibiotics is rising and a number of known antimicrobial agents encounter resistance by some microorganisms. As a result, there is no antimicrobial agent that has no resistance to microorganisms. This rise has forced clinicians to use in vitro antimicrobial susceptibility tests for diagnosis purposes. Obtaining nanoparticles with antimicrobial characteristics is a must and they can be further used to fight antimicrobial resistant microorganisms endangering human and animal health (Figure 1) [57].  
The US Environmental protection Agency has recognized copper as the first solid antimicrobial element due to its antimicrobial activity. It was first used to sterilize wounds and water, and it was later discovered it developed immunity to cholera for copper workers [58]. Considering the availability of copper and the fact that is has similar properties to silver and gold has made copper a better alternative. Additionally, it has been found that copper not only possesses antibacterial properties, but also has antifungal characteristics, which reduce the development of various microorganisms--such as _E. coli_, as later described in this article [59]. Research shows that copper and its composites have been used both due to their low-cost and effective methods for sterilizing liquids, surfaces, materials, textiles and human tissue for centuries [60]. However promising copper nanoparticles may be in the future, their stable synthesis is still challenging at the moment, as they undergo rapid oxidation in air or aqueous media [61].
## 6 Solutions for Security and Trust in IoV
### Future Directions
#### 6.4.4 IoV and Big Data  
Since the IoT is expanding and huge amounts of data are being produced in vehicle networks, researchers may investigate how these two technologies might be combined in the future to enhance trust models and security solutions. Researchers may obtain insights from vast amounts of data using IoV and Big Data, improving trust models and security in connected vehicle networks. Big data analytics can improve vehicle real-time communication reliability by developing adaptive trust mechanisms. By employing machine learning to identify anomalies and potential intrusions, this combination helps provide proactive security solutions. Combining IoV with big data could lead to more secure, efficient, and reliable intelligent transportation systems.
## 3 Sea Experiment
Figure 9a depicts the outputs of the synchronous detection of the original signal sent from sound source, while Figure 9b without using CCVTRM and Figure 9c using CCVTRM. Obviously, some pseudo correlation peaks appearing in Figure 9b will confuse the judgment of the sync header's position. On the contrary, the correlation peak can be easily found and the position of sync signal is complete clear after using CCVTRM, as shown in Figures 9c and 10 illustrates the decoded results, including Figure 10a of the original signal, Figure 10b without using CCVTRM and Figure 10c using CCVTRM. Figure 11 is a partial enlargement of decoded results shown in Figure 10. Indeed, the application of CCVTRM makes the decoding correlation peak of each symbol become single and easy to determine, consequently, reducing the BER. Figure 8: The waveforms of the data stream. (**a**) NO CCVTRM; (**b**) CCVTRM. Note: NO CCVTRM means only using ASP; CCVTRM means using CCVTRM after ASP. Figure 9: The outputs of the synchronous detection. (**a**) Original; (**b**) NO CCVTRM; (**c**) CCVTRM. Since UWA channel is rapidly time-varying, the duration of signal frame should be short. In this case, we transmitted 196 bits of information data in each communication trial and changed the communication rates. Table 3 shows the decoded results of UWA communication trials at different communication rates. The results indicate that the performance of UWA communication is dramatically improved thanks to the CCVTRM processing. It should be pointed out that zero BER means there is no error in the statistical sample, that is to say, the actual BER is very low. In order to verify the stability of the proposed UWA channel, we have to consider the effect of the error on the performance of UWA communication. Figure 11: A partial enlargement of decoded results. (**a**) Original; (**b**) NO CCVTRM; (**c**) CCVTRM. Figure 10: The decoded results. (**a**) Original; (**b**) NO CCVTRM; (**c**) CCVTRM. of the CCVTRM communication scheme, the trials described above were all repeated seven times. Table 4 shows all the decoded results of UWA communication trials using CCVTRM. It is obvious that CCVTRM communication scheme performs well and this scheme achieves a 66.7 bit/s communication rate at a very low BER under a 300 Hz bandwidth, indicating that CCVTRM technology is robust.
###### Abstract  
In this article, we investigate the spatio-temporal variability of tropospheric NO\({}_{2}\) Vertical Column Densities (VCDs) and surface concentrations that were retrieved using Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements over Thessaloniki, Greece, for the period June 2020 to November 2022. The MAX-DOAS system captures the NO\({}_{2}\) seasonal variability very well, reporting low concentrations during summer and higher concentrations in the winter, as well as the diurnal cycle with higher NO\({}_{2}\) amounts in the morning followed by a reduction towards noon. The \"weekend effect\" is evident, with approximately 30% lower NO\({}_{2}\) concentrations on the weekends compared to the working days. An excellent agreement is found with in situ data from a nearby air quality monitoring station with Pearson's correlation coefficients ranging between R = 0.90 and R = 0.99. The spatial variability is assessed by comparing the NO\({}_{2}\) concentrations at four azimuth viewing directions of the MAX-DOAS system. Despite the large variability due to short- and long-term temporal variations, higher NO\({}_{2}\) concentrations of up to 25% (statistically significant at the 95% confidence level) are reported for the azimuth that crosses the entire city center and an urban area compared to those pointing towards the sea. The MAX-DOAS tropospheric NO\({}_{2}\) columns are then compared to those measured by the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-SP satellite. Despite the generally good correlation (R = 0.72 and R = 0.89 for the daily and monthly data, respectively), a clear underestimation of TROPOMI is found (approximately 55% in winter, 21% in spring and 40% during autumn and summer), mainly due to the much larger satellite footprint that provides a smoother perception of the NO\({}_{2}\) concentration, while the MAX-DOAS measurements are more affected by local emissions.  
MAX-DOAS; TROPOMI; tropospheric; NO\({}_{2}\); air quality; Thessaloniki 2023  
13 February 2023  
18 February 2023