esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Principle
### Lines Loaded with a Single Symmetric Resonator (Resonance-Based Structures)
Finally, if the resonator is not aligned with the line, _i.e.,_ rotated or laterally displaced (Figure 2c), perfect cancellation of fields in the particle is not possible and a notch in the transmission coefficient emerges (exceptionally, the structure may be still transparent when only a small amount of field lines illuminates the resonator). Due to symmetry disruption, the resonator is effectively coupled to the line, the coupling level (and hence the notch magnitude) being intimately related to the level of asymmetry. Figure 2: CPW transmission line loaded with a single SRR. (**a**) Line and resonator aligned and excitation with the fundamental (even) mode; (**b**) line and resonator aligned and excitation with the slot (odd) mode; (**c**) line and resonator misaligned and excitation with the fundamental (even) mode. The cross and dot point to the direction of the magnetic field axial to the SRRs. From the previous words, it follows that transmission lines loaded with movable symmetric resonators can be used for sensing linear and angular displacements and velocities. Obviously, the line and resonator geometry need to be optimized in order to achieve optimum sensor performance (linearity, sensitivity, dynamic range, _etc._). This depends on the specific type of measurement and is left for Section 4, where various examples are reported. Since the symmetry is not affected by changes in environmental conditions, these sensors based on symmetry properties are robust against variable ambient conditions (temperature, moisture, _etc._) [30; 31; 32; 33]. It should be highlighted that these sensors are especially suited to detecting the lack of alignment between the resonator and the line, and are therefore useful as alignment sensors. Symmetry disruption can also be achieved by means of different mechanisms (not only by linear or angular displacements), for instance, by asymmetric loading of additional inclusions. Thus, these structures may be of interest as comparators or sensors for dielectric measurements [33], where small samples of dielectric materials are loaded to the structure. Figure 3 illustrates the different possibilities concerning discrete loading, where it can be appreciated that symmetry rupture generates a notch in the transmission coefficient. Lastly, it is also worth pointing out that frequency-domain barcodes implemented by multi-resonating structures [22] and using the resonance-based approach have also been reported [33].
## 2 Materials and Methods  
### Mild Pyrolysis Process  
Bulk samples of the woody mixture and its torrefied sample comprising spruce, pine, and fir were received from RFT, Liverpool. A sample of valorized material was produced using patented torrefraction technology, as shown in Figure 1, developed by the company. The technology is based on the indirect delivery of the process heat through phase change of the heat transfer medium (Thermal fluid). The pilot unit can process approximately 20 kg/h of biomass. Processing capacity is determined mostly by the moisture content of a feedstock and requirements for the parameters of the obtained torrefied product. The torrefraction process used in this work heats biomass by direct contact with hot, hollow, flat surfaces (trays). The temperature was measured by K-type thermocouples for each tray individually, with the tip of the thermocouple being in proximity to the bottom side of the top plate. 3 K-type thermocouples measure temperature in proximity to the bottom side of the bottom plate for Trays 1, 3, and 4. Due to rotating elements (paddles), it was not possible to measure the temperature inside the bed or on the top side of the (top) plate.  
Material delivered for the test performed for this work (test in the 1 m\({}^{3}\) vessel) was a subject of mild pyrolysis. The average temperature for all the trays of the reactor, measured in proximity to the bottom side of the top plate, was 302.9 \({}^{\circ}\)C. An important novelty of this particular reactor, in comparison to other modern torrefraction reactors [44; 45; 46], is the ability to achieve very low residence times due to enhanced mixing of the bed. The mean residence time of biomass particles inside the reactor was 7 min. Typically, the residence time for mild pyrolysis of biomass ranges between 30 and 90 min [47; 48; 49]. However, shorter residence times of 5-12 min have been proposed [50; 51]. Shorter residence times could be considered beneficial, as the productivity of a reactor of a certain size depends on its residence time in a linear manner. However, heat exchange limitations within the bed should always be carefully considered, with mixing being especially important.
###### Abstract  
The nonlinear Kerr effect and chromatic dispersion are the fundamental causes of optical signal degradation in single-mode fiber (SMF) and erbium-doped fiber-amplification (EDFA)-based wavelength division multiplexing (WDM) transmission. Dispersion management combined with a midway optical phase conjugator among the technologies for compensating for such optical signal distortion is known to not be limited by the modulation format and multiplexing technology. Optimization of the dispersion map can partially alleviate the capacity and maximum transmission distance limitations of the SMF and EDFA system. In this paper, we propose various types of symmetric dispersion maps in which the position of zero-crossing place of the cumulative dispersion is not constant, and analyze the effect of each dispersion map configuration on 40 Gb/s \(\times\) 24-channel WDM signal distortion compensation. When designed with the residual dispersion per span (RDPS) around 400 ps/nm, it is confirmed that most of the proposed dispersion maps are more effective in compensating the distorted WDM signal than conventional dispersion map. In particular, we confirm that, among the proposed dispersion maps, the dispersion map in which the RDPS is designed uniformly for all fiber spans can increase the power margin of WDM channel and expand the range of the total residual dispersion in the dispersion-managed link.  
dispersion management; dispersion map; midway optical phase conjugator; chromatic dispersion; residual dispersion; nonlinear Kerr effect; wavelength division multiplexed
# Large Scale Implementations for Twitter Sentiment Classification
## 2 Related Work
### Sentiment Analysis and Classification Models
A more recent approach [27] builds a sentiment and emoticon lexicon to support multidimensional sentiment analysis of Twitter data. In addition, several works in the SemEval competitions addressed the task of classifying the sentiment of tweets with hundreds of participants [32; 33; 34; 35]. The evaluations are intended to explore the nature of meaning in language, as meaning is intuitive to humans and so transferring those intuitions to computational analysis has proved elusive. Moreover, other learning methods were implemented in Hadoop for classifying the polarity of tweets, e.g., the large-scale formulation of the Support VectorMachine learning algorithm as presented in [36; 37]. Another similar work is introduced in [38], where authors propose techniques to speed up the computation process for sentiment analysis. Specifically, they use tweet subjectivity in order to select the right training samples, and, in the following, they introduce the concept of EFWS (Effective Word Score) of a tweet that is derived from polarity scores of frequently used words, e.g., an additional heuristic that can be used to speed up the sentiment classification with standard machine learning algorithms. They achieve overall accuracies of around 80% for a training dataset of \(100K\) tweets, a result very similar to our proposed manuscript. Previous works regarding emotional content are the ones in [39; 40]; they presented various approaches for the automatic analysis of tweets and the recognition of the emotional content of each tweet based on [PERSON] emotion model, where the existence of one or more out of the six basic human emotions (Anger, Disgust, Fear, Joy, Sadness and Surprise) is specified. Moreover, a cloud-based architecture was proposed in [41] where authors aim at creating a sentiment analysis tool for Twitter data based on Apache Spark cloud framework. The proposed system was trained and validated with real data crawled by Twitter and in following results are compared with the ones from real users. In addition, in [42], a novel method for Sentiment Learning in the Spark framework is presented; the proposed algorithm exploits the hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification procedure of diverse sentiment types in a parallel and distributed manner. The approach in [7] evaluates the contribution of different features (e.g., n-grams) together with a \(k\)NN classifier. Authors take advantage of the hashtags and smileys in tweets to define sentiment classes and to avoid manual annotation. In this paper, we adopt this approach and greatly extend it to support the analysis of large-scale Twitter data. A large-scale solution is presented in [43] where the authors build a sentiment lexicon and classify tweets using a MapReduce algorithm and a distributed database model. Although the accuracy of the method is good, it suffers from the time-consuming construction of the sentiment lexicon.
## 3 Results
### Test-Retest Reliability  
The level of test-retest reliability was somewhat lower than intra-rater reliability. However, for all variables, the CV and SEM were relatively low, indicating good data consistency. With both eyes open and eyes closed, no significant differences were found between the left and right sides in any variable. For measurements with eyes open, good to excellent reliability was observed for COP length and velocity in the M-L direction and for total COP length (ICC = 0.77-0.91). However, for COP length and velocity in the A-P direction, as well as for COP area, reliability was moderate (ICC = 0.60-0.70). Additionally, for these variables, Pearson's correlation was also moderate and non-significant (Table 3). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Outcome Measure** & **Side** & \multicolumn{2}{c}{**Mean \(\pm\) SD**} & **SEM** & **CV** & \(p\) & **Mean \(\pm\) SD** & **SEM** & **CV** & \(p\) & **ICC** \\ \hline Total & R & 265 \(\pm\) 110 & 30 & 41 & & 321 \(\pm\) 98 & 27 & 30 & & 0.93 \({}^{*}\) & 0.96 \\ length & L & 288 \(\pm\) 109 & 30 & 37 & 0.06 & 361 \(\pm\) 140 & 39 & 38 & 0.07 & 0.90 \({}^{*}\) & 0.93 \\ \hline Length x & R & 143 \(\pm\) 64 & 17 & 45 & & 194 \(\pm\) 71 & 19 & 35 & & 0.86 \({}^{*}\) & 0.92 \\ (ML) & L & 162 \(\pm\) 77 & 21 & 47 & 0.06 & 215 \(\pm\) 105 & 29 & 49 & 0.21 & 0.89 \({}^{*}\) & 0.92 \\ \hline Length y & R & 206 \(\pm\) 90 & 26 & 43 & & 232 \(\pm\) 70 & 20 & 30 & & 0.90 \({}^{*}\) & 0.93 \\ (AP) & L & 216 \(\pm\) 76 & 22 & 35 & 0.40 & 258 \(\pm\) 90 & 26 & 34 & 0.16 & 0.88 \({}^{*}\) & 0.93 \\ \hline Velocity x & R & 5.73 \(\pm\) 2.5 & 0.71 & 45 & & 7.78 \(\pm\) 2.87 & 0.79 & 36 & & 0.86 \({}^{*}\) & 0.92 \\ (ML) & L & 6.49 \(\pm\) 3.1 & 0.86 & 47 & 0.07 & 8.63 \(\pm\) 4.2 & 1.17 & 49 & 0.21 & 0.89 \({}^{*}\) & 0.85 \\ \hline Velocity y & R & 7.99 \(\pm\) 3.5 & 0.99 & 44 & & 9.01 \(\pm\) 2.9 & 0.80 & 32 & & 0.90 \({}^{*}\) & 0.94 \\ (AP) & L & 8.45 \(\pm\) 3.1 & 0.83 & 35 & 0.29 & 10.15 \(\pm\) 3.5 & 0.97 & 34 & 0.10 & 0.89 \({}^{*}\) & 0.93 \\ \hline Area 95\% & R & 1208 \(\pm\) 60 & 318 & 50 & 1688 \(\pm\) 780 & 297 & 47 & & 0.64 \({}^{*}\) & 0.62 \\ \hline \hline \end{tabular} \({}^{*}\)—\(p\)-value \(<\)0.05; SD—standard deviation; CV—coefficients of variation; SEM—standard error of measurement; ICC—Intraclass Correlation Coefficients; r—Pearson correlation coefficient; R—right; L—left.  
\end{table} 
Table 1: Intra-rater reliability with eyes open.
## 2 Materials and Methods
### Random Perturbator  
Including fabrication tolerances in the design process is a crucial step to obtaining an optimal agreement with experimental results minimizing the necessity of trimming tools. The proposed algorithm performs this analysis by returning the Probability Density Function (P.D.F.) of the resonant frequency for arbitrary values of deposition tolerances for the piezoelectric and top electrode layers.  
The random perturbator performs this through the following operations.  
1. For each \(k_{1}\in\mathrm{A}\), the thicknesses of the piezoelectric and top-electrode layers are varied according to Equation (5): \[k_{i}^{-}=\frac{h_{i}+\Delta h}{d_{i}+\Delta d}\] (5) where \(\Delta h\) and \(\Delta d\) are aleatory variables following uniform distributions between the specified values of the fabrication tolerances.
2. The resonant frequency of each (\(k_{i}^{-}\)) is estimated by taking the value of the phase velocity from the corresponding calibration curve and using Equation (6). \[f_{ri}=v_{pi}(d_{i}+\Delta d)\] (6)
3. Operations 1 and 2 are repeated until the convergence of the P.D.F. occurs; in this case, the algorithm is repeated 50,000 times.
4. The P.D.F. of the resonance value is obtained.  
It is emphasized that using the calibration curves drastically reduces the computational time and allows the Monte Carlo simulations to be performed in a reasonable time range. For example, considering a computer using an Intel i9-9900K processor with 64 GB of RAM, FEM simulation of a single resonator requires approximately 7 min and 1.6 GB of RAM. With the same computer, the simulation of one resonator using this approach reduces the time by 2 orders of magnitude to about 30 ms.  
The P.D.F. of the resonant position, exploited through the Monte Carlo simulations, is given in about 90 s when considering 50,000 iterations. If the same study had been applied with FEM models, it would have taken 243 days.
## 5 Discussion
### Clustering and Heterogeneity Across EU Countries  
The examination of hierarchical clustering reveals notable differences in the level of digital preparedness among EU members.  
Luxembourg, the Netherlands, Finland, Ireland, and Sweden are among the top cluster of digitally advanced nations that have strong digital workforces, good broadband infrastructure, and high cloud adoption rates. According to earlier studies on digital frontrunners in Europe, these nations are defined by developed digital ecosystems and advantageous socioeconomic circumstances [69; 70; 71].  
Mid-tier nations like Austria, Germany, Portugal, Italy, and the Czech Republic are included in a second cluster. These countries have moderate ICT workforce density and infrastructure levels, exhibiting balanced but not exceptional digital performance. In comparison to the digital leaders, their positioning implies consistent digital growth but also points to holes that still need to be filled.  
Bulgaria, Romania, Slovakia, Latvia, and Hungary are among the digitally lagging economies in the third cluster, which has lower ratings on important metrics of economic development, human capital, and digital adoption. Concerns over unequal progress in attaining digital cohesion are reiterated by this cluster, which demonstrates ongoing digital inequalities inside the EU [72; 73]. According to [PERSON] et al. [74], the rise of Industry 4.0 breakthroughs is expected to significantly disadvantage populations from less educated nations, highlighting the persistent problems related to uneven technical progress throughout the EU.  
Despite Romania's relatively robust ICT infrastructure and broadband penetration rates, it is noteworthy that the two countries are clustered inside the same sub-cluster. Their combined ranking with nations like Slovakia and Latvia implies that although digital infrastructure is a vital enabler, overall digital maturity is largely shaped by broader socioeconomic factors like GDP per capita, unemployment rates, and the uptake of higher education. This finding lends credence to the idea that infrastructure by itself cannot propel digital transformation unless it is combined with concurrent investments in equitable economic growth, institutional capacity, and education [75].
# Adaptive Suppression Method of LiDAR Background Noise Based on Threshold Detection
## 5 Conclusions  
In order to effectively suppress the interference of the background radiation noise in the instantaneous detection field of view on the system, the influence of the background radiation noise and the detection performance of the system is analyzed. According to the principle of CFAR control, on the basis of the traditional adjustment method, an adaptive suppression method of LiDAR background noise is realized through the adaptive threshold adjustment current steering architecture with a back-end DAC correction. In order to solve the problem of accurate noise detection quantification in a short time, a dynamic comparator is used to replace the traditional continuous comparator, and the measurement of the pulse duration of the noise is added to improve the noise detection accuracy. Finally, the experiment is carried out based on the LiDAR designed by our team, and the experimental results show that under the strong background noise, compared with the data obtained by the traditional method, the noise ratio can be reduced by more than 80%, which has a better background noise suppression capability. However, this method increases the complexity and design difficulty of the hardware, and has certain application limitations. It is suitable for laser radar with non-mobile scanning applications. For mobile applications, the noise change is more complex, and the DAC correction algorithm needs to be further improved, which is also the direction of future research.  
Conceptualization, Y.J.; methodology, Y.J.; software, T.X.; validation, Y.J., C.J. and T.X.; formal analysis, Y.J. and C.J.; investigation, R.L.; data curation, T.X. and R.L.; writing--original draft preparation, Y.J.; writing--review and editing, Y.J. and J.Z.; visualization, C.J. and T.X.; supervision, J.Z. and Y.W.; project administration, J.Z. and Y.W.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the method of this study are available from the corresponding author upon request.  
The authors thank the members of the LiDAR group in the Optoelectronics Technology Department for their help in experimentation.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### LC-MS/MS Confirmation  
As shown in Table S2, all marker components in the LC-MS system were identified from their molecular ion peaks in the positive or negative ion modes. Six analytes (LIQA, LIQ, NAR, NARG, GLY, and HON) were detected using the negative ion mode [M - H]\({}^{-}\) at _m/z_ 549.4, 417.3, 579.5, 579.5, 821.6, and 265.1, respectively. Seven analytes (NOD, HES, NPON, PON, API, DEC, and DECA) were also detected using the positive ion mode [M + H]\({}^{+}\) at _m/z_ 409.2, 611.4, 595.1, 595.1, 271.2, 329.2, and 329.2, respectively. In the case of PRAA, the molecular ion peak was confirmed at _m/z_ 404.1 in the form of [M + H\({}_{2}\)O]\({}^{+}\) in the positive ion mode. The \(r^{2}\) of the prepared calibration curve for each analyte was more than 0.99, demonstrating excellent linearity at the tested concentration ranges (Table S4). The LOD and LOQ values of each component were 0.001-0.710 ng/mL and 0.003-2.131 ng/mL, respectively, based on signal-to-noise ratios of 3.3 and 10. (Table S4).
## 3 Results and Discussion
### Biological Evaluations
#### 3.2.3 Antimicrobial Activity  
As observable in Table 2, most of the compounds tested inhibited the growth of gram-positive bacteria, but none was effective against the gram-negative strain tested. This inhibition was found to be concentration-dependent (Figures S13 and S14).  
The most efficient compounds against _Microoccus luteus_ were **6** and **7**, both with similar IC\({}_{50}\), which suggests that cyclization does not influence the toxicity of these molecules against this species. These results are concordant with the literature, since it is described that these compounds presented similar activity against _Mycobacterium tuberculosis_[58]. Although some compounds tested exhibit an IC\({}_{50}\) value greater than 200 \(\upmu\)g/mL, the percent of growth inhibition exhibited at the maximum concentration tested allows one to deduce some interesting structure/activity relationships, which are discussed below. The chalcone **1** had no effect against _M. luteus_, while chalcone **8** presents 41.71% of growth inhibition at 200 \(\upmu\)g/mL. These results suggest that the presence of methoxyl groups at ring A reduces its activity against this species. The same effect is observed when comparing the activity of compounds **4** and **5** (Figure 5).  
Figure 4: Structure/activity relationships established for antioxidant activity.  
Compound **5** inhibits 43.08% of bacterial growth at 200 \(\upmu\)g/mL, while this value is almost double for compound **4** (74.43 \(\pm\) 1.95%), which confirms that the methoxyl groups at ring A decrease the molecule's antibacterial effect.  
The strongest activity against _Bacillus subtilis_ was displayed by compound **6** (IC\({}_{50}\) = 9.33 \(\upmu\)g/mL), presenting even better activity than streptomycin (Table 2). The corresponding flavanone **7** has a higher IC\({}_{50}\) (20.48 \(\upmu\)g/mL), which leads to the conclusion that the \(\upalpha\),\(\upbeta\) unsaturated carbonyl bridge linking ring A to ring B is important in the mode of action of the compounds against this bacterial strain. The molecules with the higher numbers of hydroxyl groups are the ones with better activity, showing that these groups also play an important role in the molecule's mode of action against _B. subtilis_. Again, the compounds with two methoxyl groups in _meta_ positioning (**1** and **5**) were the ones with lower or none antimicrobial activity. Comparing the results from compounds **4** and **7** (both flavanones with two hydroxyl groups), one can conclude that the presence of the methoxyl group in ring B (compound **4**) reduces the compound's activity against this bacterial strain (Figure 6).
## 3 Results and Discussion  
### Characterization of the PPy NTs  
The FE-SEM image captured of the PPy NTs is shown in Figure 1, illustrating a one-dimensional nanotube structure of the PPy that is 87-150 nm in diameter and several micrometers in length, which is consistent with that reported in the previous literature [53].  
The FT-IR spectrum of the PPy NTs ranged from 400 to 4000 cm\({}^{-1}\) (Figure 2). The peaks at 1552 and 1450 cm\({}^{-1}\) can be related to symmetric and asymmetric polypyrrole ring stretching bonds, sequentially. Moreover, the peak at 1038, 1333, 1110, and 1603 cm\({}^{-1}\) corresponds to C-H deformation vibrations, C-N stretching vibrations, C-C stretching, and C=C stretching, sequentially. A broad peak at 3446 cm\({}^{-1}\) can also be related to the N-H stretching vibrations of the pyrrole ring. The successful PPy polymer formation is evident based on the FT-IR patterns [54; 52].  
Figure 1: FE-SEM image of the PPy NTs.  
The XRD pattern obtained of the PPy NTs can be seen in Figure 3. There is a wide peak at the 20 value of 24.8\({}^{\circ}\) (Figure 3). The wide peak indicates the amorphous polymer and intramolecular stacking structure [55].