esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This trend towards a healthier life has simulated a desire for flavorings, supplements and preservatives of natural origin [44], and for the development of bioactive compounds, which are used as a tool to complement and/or replace chemically synthesized compounds in current production models [45]. Peppermint plants are negatively affected by salinity-affected soil, which severely decreases the biomass and essential oil yield [46]. Therefore, different techniques have been proposed to mitigate the negative effects of salt stress in peppermint crops. These approaches include the use of salt-tolerant varieties, stress signaling molecules, osmoprotectants, green algae and plant extracts, with plant and green algae extract having demonstrated a great deal of promise for crop enhancement in moderate stress conditionsin recent years [47]. It is still necessary, however, to look for new alternatives to minimize this problem since the amount of soils affected by salinity is increasing. In previous studies, we have shown that direct inoculation of native _P. putida_ SJ04 produces beneficial effects on _M. piperita_, including growth promotion and increased essential oil yield, plant VOC emission and total phenolic compounds [48; 49; 50]. Furthermore, SJ04 mVOCs induce changes in the essential oil composition [51]. Taking into account these properties of direct inoculation, as well as VOC emission from _P. putida_ native to the _M. piperita_ rhizosphere, we proposed to determine their capability for helping plants to cope with adverse salt stress conditions. The study of volatile organic compounds of microbial origin is still a little explored field, so the specific effects produced by the volatile compounds in the plant need also to be evaluated under abiotic conditions. The _Arabidopsis thaliana_ plant has been widely used as a study model in plant-microorganism interactions. However, it is necessary to analyze other models in order to determine whether these discoveries can be applied more generally in the plant kingdom, since there is a possibility that the mechanisms may be different [52]. Therefore, the present study was carried out using the aromatic and medicinal plant species _M. piperita_. Thus, the present survey was designed to analyze the bioactivity of mVOCs emitted by _P. putida_ grown under control and salt stress conditions as a biocontrol of phytopathogenic fungi and as a plant growth promoter: (i) exploring whether VOCs produced by native _P. putida_ under salt stress conditions affect the tolerance of peppermint plants; (ii) determining if mVOCs produced by _P. putida_ under salt stress conditions affect the growth of major fungal phytopathogens; and (iii) verifying whether the salt condition of growth media affects the VOC emissions of _P. putida._
# Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
###### Abstract  
Pre-trained language models perform well in various natural language processing tasks. However, their large number of parameters poses significant challenges for edge devices with limited resources, greatly limiting their application in practical deployment. This paper introduces a simple and efficient method called Autocorrelation Matrix Knowledge Distillation (AMKD), aimed at improving the performance of smaller BERT models for specific tasks and making them more applicable in practical deployment scenarios. The AMKD method effectively captures the relationships between features using the autocorrelation matrix, enabling the student model to learn not only the performance of individual features from the teacher model but also the correlations among these features. Additionally, it addresses the issue of dimensional mismatch between the hidden states of the student and teacher models. Even in cases where the dimensions are smaller, AMKD retains the essential features from the teacher model, thereby minimizing information loss. Experimental results demonstrate that \(\text{BERT}_{\text{TINY}}\)-AMKD outperforms traditional distillation methods and baseline models, achieving an average score of 83.6% on GLUE tasks. This represents a 4.1% improvement over \(\text{BERT}_{\text{TINY}}\)-KD and exceeds the performance of \(\text{BERT}_{\text{4}}\)-PKD and \(\text{DistilBERT}_{\text{4}}\) by 2.6% and 3.9%, respectively. Moreover, despite having only 13.3% of the parameters of \(\text{BERT}_{\text{BASE}}\), the \(\text{BERT}_{\text{TINY}}\)-AMKD model retains over 96.3% of the performance of the teacher model, \(\text{BERT}_{\text{BASE}}\).  
BERT; knowledge distillation; model compression +
Footnote †: journal: Applied
###### Abstract  
This work compared the effectiveness of several Methylene Blue (MB)-based protocols for photo-eradication of biofilms formed on the surface of the glass and stainless steel discs by _S. aureus_ MRSA isolates using a diode laser (\(\lambda\) = 665 nm; output power 40 mW; energy fluence was 189 J cm\({}^{-2}\)). The results obtained showed that MB alone, up to a concentration of 62.5 mgL\({}^{-1}\), had limited photo-bactericidal activity. It was possible to enhance the activity of MB using two types of spherical gold nanoparticles of similar sizes, 15 \(\pm\) 3 nm/20 \(\pm\) 3 nm, but differing in the method of their synthesis and stabilization. The enhancement of the photodestruction effect was related to the increased production of hydroxyl radicals by the MB+gold nanoparticles mixture, and this mixture showed dark cytotoxicity against the _occi_ studied. Effective destruction (mortality above 99.9%) of the biofilms formed by MRSA isolates was also possible without the use of gold nanoparticles, but the concentration of MB had to be at least 125 mgL\({}^{-1}\). A highly efficient protocol of photodestruction of biofilms, consisting of triple exposure of biofilms to laser light in the presence of MB alone, combined with the removal of dead bacteria protecting deep layers of pathogens against photosensitization, was also described.  
aPDT; biofilm; methylene blue; gold nanoparticles; biofilm washing; disinfectant 2023
# End-Effectors Developed for Citrus and Other Spherical Crops
## 3 Picking Performance Tests
### Single-Factor Tests
#### 3.3.2 Effect of End-Effector Speed on Picking Performance  
The end-effector steadily grips the citrus fruit through the flexible fingers, and then the whole end of the robot arm is controlled to rotate the end-effector as a whole to make the blade cut the citrus fruit stalk. Therefore, the rotational speed of the end-effector determines the cutting speed of the fruit stalk. Five factor levels of 80, 120, 160, 200, and 240 mm/min were selected for a single-factor experimental study to analyze the effect of the change of the rotational speed of the end-effector on the picking performance.  
According to Figure 14, the picking success rate increases and then decreases as the speed of the end-effector increases, and the picking success rate is the highest at the end-effector speed of 160 mm/min, which can reach 96.19%; the single fruit picking time decreases as the speed of the end-effector increases, and it takes the least time at the end-effector speed of 240 mm/min, which is 4.65 s. At the end-effector speed of 120 mm/min, the slope of single fruit picking time decreases. In summary, it can be concluded that the picking performance is optimal at the end-effector speed of 160 mm/min.
## 5 Results and Discussion
### Brier Score Loss  
Another measure we can use for the Calibrated Naive Bayes is the Brier Score Loss [27]. This is an evaluation metric for classification tasks and can be implemented in this case, having two categories. Brier Score Loss is calculated according to the formula below:  
\[BSL=\frac{1}{N}\sum_{t=1}^{N}(f_{t}-\sigma_{t})^{2} \tag{9}\]  
where \(f_{t}\) represents the probabilistic value of the prediction, and \(\sigma_{t}\) is the result of the event of the instance t, with N the number of instances. Thus, the Brier Score Loss calculates the quadratic average of the errors between the predicted value and the current one. The result obtained is always between 0.0 and 1.0, where an ideal model has a score of 0, and in the worst case, a score of 1. In practice, models that have a Brier Score Loss around 0.5 are more difficult to interpret, because that is a point of uncertainty, in which several factors can influence the outcome. Thus, the goal is to obtain a score as low as possible.  
Figure 11: Prediction accuracy for classic, isotonic, and sigmoid Naive Bayes cases.  
Figure 10: Graphical representation of the number of posts classified correctly/incorrectly for the algorithms Multinomial NB, Gaussian NB, Bernoulli NB.  
The low values for BSL for the Naive Bayes algorithm and its calibrated variants can be seen in the figure above. These are in the range 0.2-0.3, with better values obtained in the case of Naive Bayes calibrated isotonic and sigmoid. Thus, the models created show good performance for the calibrated variants.
# Comparing PlanetScope and Sentinel-2 Imagery for Mapping Mountain Pines in the Sarntal Alps, Italy
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference**} \\ \hline  & & Class 0 & Class 1 & Total \\ \cline{2-5} Classification & Class 0 & 280 & 123 & 403 \\ \cline{2-5}  & Class 1 & 115 & 147 & 262 \\ \cline{2-5}  & Total & 395 & 270 & 665 \\ \hline  & \multicolumn{4}{c}{Overall Accuracy and Kappa coefficient} \\ \hline OA & OA & Kappa \\ \hline
0.6421 & 0.6421 & 0,2545 \\ \hline  & \multicolumn{4}{c}{Class Statistics} \\ \hline PA & PA & UA \\ \hline Class 0 & 0.7089 & 0.6948 \\ \hline Class 1 & 0.5444 & 0.5611 \\ \hline Class 1 represents mountain pines. Class 0 represents other land cover types. OA = overall accuracy; PA = producer ’s accuracy; UA = user ’s accuracy; Kappa = Kappa coefficient. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CHM classification (“scheme 1”). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Reference**} \\ \hline \multirow{4}{*}{Classification} & \multicolumn{2}{c}{Class 0} & Class 1 & Total \\ \cline{2-5}  & Class 0 & 381 & 36 & 417 \\ \cline{2-5}  & Class 1 & 24 & 223 & 247 \\ \cline{2-5}  & Total & 405 & 259 & 664 \\ \hline \multicolumn{5}{c}{Overall Accuracy and Kappa coefficient} \\ \hline \multirow{4}{*}{Classification} & \multicolumn{2}{c}{Overall Accuracy and Kappa coefficient} \\ \hline \multirow{4}{*}{Classification} & \multicolumn{2}{c}{OA} & Kappa \\ \cline{2-5}  & 0.9096 & 0.8085 \\ \hline \multicolumn{5}{c}{Class Statistics} \\ \hline \multirow{4}{*}{Class 0} & \multicolumn{2}{c}{PA} & UA \\ \cline{2-5}  & Class 0 & 0.9407 & 0.9137 \\ \hline \multicolumn{5}{c}{Class 1} & 0.8610 & 0.9028 \\ \hline \end{tabular} Class 1 represents mountain pines. Class 0 represents other land cover types. OA = overall accuracy; PA = producer ’s accuracy; UA = user ’s accuracy; Kappa = Kappa coefficient.  
\end{table} 
Table 7: PS + GLCMMAX + Topo classification (“scheme 3”).
## 4 Simulation Results and Analysis
### When \(H\) and \(D\) Remain Unchanged, the Influence of \(D_{1}\) Changes the Flow Force  
When the fixed values \(H\) = 4 mm, \(D\) = 8 mm, spool opening \(x_{v}\) = 1 mm are unchanged, \(D_{1}\) changes within a range of 14-22 mm, and every 2 mm is a measuring point. As can be seen from Figure 17, when \(D_{1}\) becomes larger, the force on surface 1 basically remains unchanged. Compared with the unimproved spool, the difference is only 0.74 N, and the reason for this is the same as in the above analyses. Since changing \(D_{1}\) does not affect the spatial distribution of surface 2, it can be seen from Figure 18 that the force on surface 2 remains basically unchanged.  
With the increase of \(D_{1}\), the included angle between surface 3 and the horizontal plane becomes larger, the impact of the liquid flow on surface 3 increases and the force on surface 3 increases, as shown in Figure 19. As can be seen in Figures 20 and 21, with the increase of \(D_{1}\), the included angle between surface 4 and the horizontal plane decreases, the force along the positive direction generated by the liquid flow impacting surface 4 decreases, the reverse diversion effect of surface 4 increases within a certain range and the jet angle also increases.  
Figure 16: Flow forces of two spools.  
Figure 17: Force on surface 1 of the two spools.  
Although the force in the negative direction on surface 2 of the improved spool becomes larger, it can be seen, in Figure 19, that the sum of the forces on surface 3 and surface 4 of the improved spool is larger than that on surface 3 of the unimproved spool, both in the positive direction, and the resultant forces on surfaces 2, 3 and 4 of the improved spool are larger than those of the unimproved spool, which produces a compensatory effect. It can be seen from Figure 22 that the flow force first increases with the increase of \(D_{1}\), and the minimum flow force is generated near \(D_{1}=18\) mm, which then decreases slightly. This is because when \(D_{1}\) is too large, the included angle between surface 4 and the horizontal plane is too small; the force in the positive direction generated by the liquid flow impacting  
Figure 19: Force on surface 3 and 4 of the two spools.  
Figure 18: Force on surface 2 of the two spools.  
surface 4 becomes smaller, and the decrease is greater than the increase of the force on surface 3. Thus, the sum of the forces on surface 3 and surface 4 becomes smaller. When the angle between surface 4 and the horizontal plane is too small, the reversed, diverting effect of surface 4 is weakened, and the jet angle increases slowly or even decreases. The relationship between jet angle and \(D_{I}\) is shown in Figure 23.  
Figure 23: Jet angles of the two spools.  
Figure 21: Velocity nephogram. (**a**) \(D_{I}\) = 14 mm; (**b**) \(D_{I}\) = 18 mm.  
Figure 22: Flow forces of the two spools.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 2 Method
### Nominal RPAS Conditions and Contingencies into Non-Segregated Airspace  
The International Civil Aviation Organization (ICAO) establishes that RPASs need to demonstrate an Equivalent Level of Safety (ELOS) to that of manned aircrafts, operate in compliance with existing aviation regulations, and appear transparent to other airspace users. In order to fulfil the ICAO requirements, it is necessary to define the flight plans properly for RPAS missions by specifying the RPAS behaviour along the whole nominal route (see Section 4.1), including the case of contingency and emergency events (see Sections 4.2 and 4.3) to be the most predictable and compliant with the expected requirements for Instrumental Flight Rules (IFR).
## 2 Materials
### Data Acquisition and Preprocessing  
#### 2.2.1 Sentinel-2 Data  
The Sentinel-2 satellite is a next-generation Earth observation system developed and launched by the European Space Agency (ESA). It consists of two identical satellites, Sentinel-2A and Sentinel-2B, which were successfully launched on 23 June 2015 and 7 March 2017, respectively, forming a dual-satellite constellation. Each satellite is equipped with an advanced multispectral imager capable of capturing data across 13 spectral bands at spatial resolutions of 10 m, 20 m, and 60 m. This system supports a wide range of applications, particularly in Earth observation and environmental monitoring. In this study, the zenith reflectance dataset (Product ID: COPERNICUS/S2_SR) available on the Google Earth Engine (GEE) platform was utilized. Image data from the period of 1 June to 1 September 2023 were selected for vegetation index calculations. This zenith reflectance dataset is a Level-2A product that has undergone topographic corrections using a digital elevation model (DEM) and atmospheric correction via the sen2 cor tool. As a result, the dataset is pre-processed and does not require additional image correction, ensuring reliable and ready-to-use data for analysis.
## 7 Applications
### Load Balancing in Google Search Backend  
Another application of this large-scale balanced-partitioning algorithm is in cache-aware load balancing of data centers [47]. While this is not the focus of the present work, we present a brief overview to demonstrate a different application.  
The load-balancer of a big search engine distributes search queries among several identical serving replicas. Then the server pulls, for each term in the query, the index of all pages associated with the search term. As significant time and network traffic is consumed in loading these index tables, a cache is employed on the serving replicas. This leads to better utilization if each search term is usually sent to a specific machine. The complication, though, originates from the fact search queries often consist of several search terms, each with its own index table.  
The proposed solution is a voting table to select the right replica for each query. The voting table is constructed via a multi-stage algorithm, yet the initial voting table--a decent solution in and of itself--comes from a balanced-partitioning formulation: We aim to partition the search terms in a balanced way, where the sizes denote lengths of index tables, so as to minimize the expected cache miss rate. This is done by carefully building a term-query bipartite graph, whose vertex weights correspond to index table sizes and whose edge weights are related to cache miss rates. See Reference [47] for more information.  
Deployed in 2015, the said solution decreases cache miss rate by about 0.5% and increases end-to-end throughput by double-digit percentage.
## 3 Results
### Step III: Identification of Useful Features  
In Step III of the MTS, OAs and S/N ratios were used to examine the effects of every feature. Since 11 attributes were assembled, an L\({}_{12}\) (2\({}^{11}\)) OA was used. As revealed in Tables 8 and 9, X1, X2, and X8 did not have a significant effect on the MD. Hence, the number of attributes was reduced from 11 to 8. The PG could be clearly distinguished from the HG group, as their MDs deviated significantly in the MS (PG: 2.308-1509.811, HG: 0.560-1.190).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**PG** & **X1** & **X2** & **X3** & **X4** & **X5** & **X6** & **X7** & **X8** & **X9** & **X10** & **X11** & **MD** \\ \hline P1 & 0.52 & 1.05 & 0.202 & 0.96 & 0.55 & 1.15 & 0.46 & 63.7 & 28.8 & 0.38 & 39.55 & 15.77 \\ P2 & 0.55 & 1.05 & 0.189 & 0.84 & 0.54 & 1.01 & 0.34 & 61.0 & 25.2 & 0.48 & 43.25 & 2.308 \\ P3 & 0.51 & 1.08 & 0.232 & 1.08 & 0.55 & 1.14 & 0.47 & 66.3 & 29.3 & 0.39 & 36.74 & 26.035 \\ P4 & 0.58 & 1.12 & 0.172 & 1.03 & 0.62 & 1.16 & 0.46 & 62.3 & 28.6 & 0.38 & 36.18 & 5.721 \\ P5 & 0.57 & 1.11 & 0.217 & 0.98 & 0.73 & 1.34 & 0.42 & 57.2 & 24.0 & 0.36 & 34.95 & 17.78 \\ P6 & 0.60 & 1.26 & 0.051 & 0.68 & 0.55 & 1.02 & 0.20 & 55.0 & 16.8 & 0.59 & 48.58 & 9.979 \\ P7 & 0.56 & 1.14 & 0.149 & 1.08 & 0.66 & 1.29 & 0.43 & 62.1 & 24.9 & 0.38 & 34.38 & 4.404 \\ P8 & 0.59 & 1.20 & 0.086 & 0.75 & 0.53 & 1.05 & 0.27 & 58.3 & 20.4 & 0.51 & 46.69 & 3.133 \\ P9 & 0.54 & 0.55 & 0.136 & 1.26 & 0.72 & 0.57 & 0.46 & 63.7 & 44.9 & 0.29 & 30.22 & 1287.16 \\ P10 & 0.55 & 1.18 & 0.102 & 0.92 & 0.56 & 0.48 & 0.35 & 61.8 & 42.5 & 0.44 & 40.32 & 1509.81 \\ \hline \multicolumn{10}{c}{Average} & & & & & & & & & & & 288.21 \\ \hline \end{tabular}
\end{table}
Table 7: MD for ACLR group (PG) data.  
Figure 3: Mahalanobis distance (MD) for the healthy group (HG) and ACLR group (PG).
## 3 Results and Discussion
### Electrical Conductivity  
Surface resistivity measurements were performed to evaluate the electrical properties of the in situ polymers synthesized with different weight ratios of PS, PETE, and MWNT. As shown in Table 2, both materials exhibited a decrease in surface resistivity with an increasing MWNT content. As shown in Figure 5, both the PS and PETE materials exhibited a gradual and nearly linear decrease in surface resistivity with each 0.2 wt% increment of MWNT added beyond the initial 1.0 wt%. This trend suggests that the increased MWNT content enhanced the electrical conductivity of the materials, leading to the observed decrease in surface resistivity. Based on the FE-SEM and TEM analyses, it could be confirmed that the MWNTs were well dispersed throughout the PS and PETE polymer matrices. This well-dispersed morphology contributed to the observed reduction in surface resistivity when 2.0 wt% MWNTs were incorporated, compared to the pristine PS and PETE samples. Based on the experimental results, the materials can be considered high-performance antistatic materials, exhibiting surface resistivity values approximately two orders of magnitude lower than the commonly accepted threshold of 10\({}^{5}\) Ohm/sq for effective antistatic behavior [32].  
Figure 4: DSC results of materials. (**a**) PS, (**b**) 1.0 wt% PS, (**c**) 2.0 wt% PS, (**d**) PETE, (**e**) 1.0 wt% PETE, (**f**) 2.0 wt% PETE. (inset: expansion of the \(\mathrm{T_{g}}\) region).  
Figure 3: TGA results of materials. (**a**) PS, MWNT/PS, (**b**) PETE, MWNT/PETE.
## 4 Proposed Q-Learning Method for Smart Grid Price Forecasting
### Q-Learning-Based Electricity Price Forecasting
#### 4.2.1 Producer Input Selection  
The system uses a 0/1 knapsack to determine the best possible value for electricity produced from an array of producers in the market. Knapsack derives a case in which the aim is to maximize the value of a knapsack while staying within the weight limitation. As per the functionality of a 0/1 knapsack, it considers either an entire item or rejects it altogether, given a collection of objects with certain weights and corresponding weight values. The algorithm takes the electricity produced per unit from the produced as weight and its corresponding wholesale price as the value of electricity. Moreover, the highest demand from customers is taken as the maximum weight capacity of the algorithm. Therefore, the maximum customer demand W is the knapsack capacity. Here, the two arrays, wholesalePrice [0...n - 1] and producerGenerator [0...n - 1], represent the wholesale price of electricity and the amount of electricity produced from each producer in a certain region. The system uses a 0/1 knapsack since electricity units cannot be broken down and a 0/1 knapsack takes complete units.