esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Plan
### Testing Procedure  
During the exposure period, each sensor was electrically connected to the column's rebar where it was embedded, which corresponds to Position 1 of the previously described INESSCOM system (Section 2, Figure 2), presented in Figure 4A. Monitoring of the state that each sensor was in was periodically carried out following the procedure described in Section 2, which corresponds to that proposed to be implemented in the INESSCOM monitoring system. This process is summarised as follows:  
* First, the macrocell current (\(i_{CORR,MACRO}\)) between the sensor and column reinforcement was measured by a Zero Resistance Ammeter (ZRA; [PERSON] 2000 Tektronix model). The value obtained for each sensor was normalised by its surface (\(\approx\)1571 mm\({}^{2}\)). The value was recorded 3 min after measurements commenced to ensure that the recorded signal was stable enough. Macrocell currents were measured according to the criterion set out in Figure 4B.
* Second, the local corrosion (\(i_{CORR,MICRO}\)) of each sensor was determined by the Potential Step Voltammetry method. This technique, used by the INESSCOM system, has been introduced in former works and previously validated [45; 46; 48; 49; 50]. This measurement was taken with an Autolab PGSTAT 100 Potentiostat/Galvanostat, and the Nova 1.11 software was employed for signal processing. The measurement cell configuration was a 2-electrode one, as described in Section 2. The working electrode was each sensor. The three continuous rebars of each column were used as the pseudoreference (Figure 4C).
* Third, the sensors' double layer capacity (\(C_{DL}\)) was determined from the voltamogram (\(\Delta I-\Delta E\)) obtained after applying CSV, \(E_{CORR}\)\(\pm\) 50 mV \(\times\) 2 cycles at a sweep speed of 1 mV/s. This measurement was also taken with an Autolab PGSTAT 100 Potentiostat/Galvanostat, and the Nova 1.11 software was used for signal processing. This procedure, previously applied by other authors in the corrosion field [55; 56; 57], has been validated in a former work [33].  
Figure 3: Accelerated carbonation method.  
Finally, sensors were once again connected to the column's rebar until the next measurement was to be taken. Figure 4 shows the scheme of the measurement process.
## 3 Residual Displacement Ratios
### Definition of Residual Displacement Ratios  
In view of the significant variability in residual displacements (with a coefficient of variation reaching up to 1.5 in most cases [13]), several indicators, such as elastic spectral displacements and peak inelastic displacements, which exhibit relatively high correlation with residual displacements, are used to normalize the residual displacements. By employing such normalization, the high uncertainty of residual displacements associated with ground motions can be excluded feasibly. Furthermore, such normalization facilitates the estimation of residual displacements in practical engineering design.  
To this end, the correlation between residual displacements and elastic spectral as well as peak inelastic displacements is first examined for mainshock-aftershock sequence excitation. To provide a visual insight, the scatter density plots of residual displacements (\(d_{\rm r}\)) versus elastic spectral displacements (\(S_{\rm d}\)) and peak inelastic displacements (\(d_{\rm m}\)) are presented in Figure 6, where a large ensemble of 151,200 samples (144 sequences \(\times\) 30 natural  
Figure 5: Three typical relations between mainshock-induced residual displacements and sequence-induced residual displacements: (**a**) \(d_{\rm r,seq}>d_{\rm r,rms}\); (**b**) \(d_{\rm r,seq}=d_{\rm r,rms}\); and (**c**) \(d_{\rm r,seq}<d_{\rm r,rms}\).  
periods (\(T\) = 0.1-3.0 s) \(\times\) 5 levels of displacement ductility (\(\mu\) = 2-6) \(\times\) 7 relative intensity ratios (\(\kappa\) = 1/4, 1/3, 1/2, 2/3, 4/5, 1, and 4/3) generated by the elastoplastic hysteretic system under all mainshock-aftershock sequences are considered for illustration.  
A comparison of Figure 6a,b reveals a stronger correlation between residual displacements and peak inelastic displacements (the Pearson correlation coefficient \(R\) = 0.88) than with elastic spectral displacements (\(R\) = 0.71), suggesting that structures experiencing large transient peak inelastic displacement is more susceptible to sustaining excessive permanent residual displacement when subjected to sequential ground motions. This observation motivates the adoption of peak inelastic displacements as the normalization basis. The residual displacement ratio for mainshock-aftershock sequences is therefore defined as follows:  
\[C_{\rm r,seq}=\frac{d_{\rm r}}{d_{\rm m,seq}}=\frac{\max\ \{d_{\rm r,ms}\,\ d_{\rm r,seq}\}}{\max\ \{d_{\rm m,ms}\,\ d_{\rm m,seq}\}} \tag{3}\]  
For the special case of \(\kappa\) = 0, the above definition can be converted to the residual displacement ratios under single earthquake record excitation (mainshock only), that is written as follows:  
\[C_{\rm r,ms}=\frac{d_{\rm r,ms}}{d_{\rm m,ms}} \tag{4}\]  
where \(d_{\rm m,ms}\) and \(d_{\rm m,seq}\) are respectively the peak inelastic displacement under mainshock only and mainshock-aftershock sequence.
###### Abstract
There are several mechanisms mediating quinolone resistance, as follows: (1) Mutations in the subunits of DNA gyrase and topoisomerase IV enzymes, which cause a decrease in the affinity of ATB to the enzyme-DNA complex [9]. (2) Plasmid gene-mediated resistance, where _qnr_ limits the binding affinity of enzymes and limits the number of available target sites on the chromosome, _aac(6)-lb-cr_ modifies or inactivates quinolones, and _oqr_AB, _qepA_1, and _qepA_2 genes encode efflux pumps that reduce the intracellular concentration of ATB [12]. (3) Chromosome-mediated resistance ensures changes in the permeability of porins in combination with the excessive expression of efflux pumps [13]. Resistance to tetracyclines is the result of mutation as well as acquired mobile genetic elements. The three mechanisms counteracting tetracycline resistance include ribosomal protection, the enzymatic inactivation of the most important ones, and the efflux system [14]. In the case of _E. coli_ bacteria, nine genes (_tet_A, _tet_B, _tet_C, _tet_D, _tet_E, _tet_G, _tet_I, _tet_L, and _tet_Y) encoding efflux pumps that actively expel antimicrobials from cells have been identified [15], in addition to three genes (_tetM_, _tet_O and _tet_W) encoding proteins providing ribosomal protection to drug delivery from the ribosome or its modification, and one gene (_tet_X) encoding an oxidoreductase that inactivates tetracyclines [16]. Another important and increasingly frequent problem is the adaptation of pathogens to environmental conditions in food processing establishments and the resulting biofilm formation. A biofilm is a collection of one or more species of microorganisms whose cells attach to surfaces or other cells and embed in an extracellular polymeric matrix that provides protection due to decreased permeability. It has been shown that bacterial antimicrobial resistance can increase by several hundred times in a biofilm. Industrial processes are significantly affected by the formation of _E. coli_ biofilms, which can have a negative impact on food safety and lead to financial losses [17]. The aim of the present study was to detect the presence of and isolate _E. coli_ from cloacal swabs of broiler chickens and chicken thigh muscle meat purchased from a commercial network. The key aim of the study was to obtain and compare results on their phenotype and genotype resistance to antimicrobial substances, and to evaluate biofilm formation. In genotype analysis, we mainly focused on the identification of quinolone resistance genes (_qnr_A, _qnr_B, and _qnr_S, which encode protective proteins) and tetracycline resistance genes (_tet_A and _tet_B, which encode efflux pumps). With this study, we want to provide an up-to-date view of the occurrence of antimicrobial-resistant _E. coli_ during meat processing in the Slovak Republic. Overall, testing _E. coli_ from chicken cloacal swabs and chicken thigh muscle meat also provides a comprehensive view of contamination risks, and helps ensure the safety of poultry products.
## 4 Uncertainties of Price, Load and PV Generation  
In order to consider the forecasting uncertainties in the operating parameters such as load, price and PV generation, stochastic scenarios are generated. To this end, a large number of possible scenarios of the above parameters are generated based on their PDFs. In fact, we assume the manager can fit the appropriate distributions on the forecasting error data. Therefore, the forecasting error of the system's load and price are modeled with a normal distribution with an average of zero and a standard deviation of 10% of their hourly forecasted values [37]. Furthermore, the historical solar irradiance data is modeled with the beta distribution [38]. Using the MCS method, 2000 stochastic scenarios are generated for each of the above parameters based on their PDFs. So, the probability of each scenario is 1/2000. Then, the set of system scenarios is formed. Each stochastic scenario represents a possible operating mode of the system and consists of a vector as \(\rho_{s,t}=[\rho_{s,t}^{PV},\rho_{s,t}^{price},\rho_{s,t}^{load}]\) which includes the PV generation, prices and loads scenarios. In this study, the correlation analysis between the scenarios of different parameters is ignored. Although the high number of stochastic scenarios increases the accuracy of the answers, it slows down the problem-solving time. In stochastic optimization problems, one of the most commonly used scenarios reduction methods is the Kantorovich distance. In this paper, this method is used to reduce the number of random scenarios. Further details of this method are given in [39]. So, the system scenarios are reduced to 200 numbers. In fact, it is assumed that these 200 stochastic scenarios can cover the most possible modes of the system.
## 4 Results
### Organ-Level Segmentation
#### Evaluation of Computational Costs  
During our performance evaluation, the Stratified Transformer emerged as the most accurate model for segmentation. To thoroughly assess each model's effectiveness, we recorded their training times, which reflect computational costs. Each model underwent training on a single GPU, optimizing the batch size to match the GPU's capacity. Table 8 details the time efficiency and segmentation accuracy of each model. Notably, the Stratified Transformer, while delivering superior segmentation results, required the most extensive training time at 88.16 h. Conversely, the PointMLP model, with its efficient architecture, logged the shortest training period of just 2.86 h.  
Figure 8 provides a comprehensive comparative analysis of various segmentation models, illustrating their performance and computational efficiency. Additionally, the figure summarizes key performance metrics--overall accuracy (OA) and mean IoU (mIoU)--for all evaluated crop types. The comparison highlights the high computational demands of the Stratified Transformer. In contrast, models like PointCNN and PAConv offer impressive accuracy with much shorter training times, making them suitable for simpler crops.  
The field of point cloud data processing has become a focal point in both academic and industrial circles, fueled by innovative models that have significantly enhanced segmentation technologies. These models excel at segmentation, utilizing a range of techniques including convolutional operations, graph-based methods, and attention mechanisms to extract local features effectively.  
However, despite these advancements, the task of accurately segmenting point clouds, particularly for plant species with complex morphologies, continues to be a pressing challenge. Models like the Stratified Transformer demonstrate exceptional segmentation abilities but are hindered by high computational demands. There is an evident need for models that optimally balance computational efficiency and segmentation accuracy. This balance is crucial and represents a key direction for future research in point cloud segmentation. The ongoing efforts to improve the precision and efficiency of these models are expected to catalyze the next generation of innovations in this field.
## 5 Advanced MPPT Techniques
### Hybrid MPPT Techniques
#### 5.3.1 PSO-P&O Algorithm  
The PSO-P&O hybrid MPPT algorithm is an advanced method that combines the global search capabilities of PSO with the local tracking accuracy of P&O [98]. In this hybrid approach, PSO is used to explore a broad range of potential MPPs by adjusting the operating voltage of the PV system and optimizing the search process based on the particles' performance. Once the PSO algorithm converges to a near-optimal region, the P&O method is employed to finely adjust the operating point and precisely track the MPP with high accuracy. Advantages of the PSO-P&O hybrid algorithm include its improved ability to handle complex and dynamic environmental conditions due to PSO's robust global search capability, which helps in avoiding local maxima and ensuring better convergence to the actual MPP. The P&O component enhances the precision of power tracking once a promising region is identified, providing efficient operation under varying conditions. Disadvantages include increased computational complexity and implementation challenges. The PSO component requires significant computational resources to perform the global search, which can be demanding in terms of processing power and time. Additionally, managing the transition between PSO and P&O, as well as tuning their parameters effectively, can be complex and may require careful calibration to balance the global search and local tracking benefits [34; 36].
## 3 Experimental Setup
### GAr Purity Monitoring  
Purity was monitored using the well-established \(\tau_{2}\) lifetime method [43]. Argon S1 VUV scintillation occurs through two decay paths: (singlet excimer) \(\tau_{1}\) and (triplet excimer) \(\tau_{2}\), the fast and slow component, respectively. As the \(\tau_{2}\) decay time increases with argon purity, this value can be used as a relative purity monitor. It was important to ensure similar purity conditions for each G-THGEM/THGEM study as electronegative impurities, within GAr, could potentially vary the charge gain and S2 scintillation light production.  
The highest values of \(\tau_{2}\) lifetime reported for GAr are 3200 \(\pm\) 300 ns [44] (with variation depending on fitting models) and more recently 3480 \(\pm\) 65 ns [45]. The PMT at the base of the TPC was used for all purity measurements. Purity measurements were taken after the initial fill of N6.0 GAr, and subsequent measurements were taken while argon was continually flushed through the detector. Data collection began only once \(\tau_{2}\) exceeded 2000 ns (which approximately corresponds to N6.0 argon gas [43]). A final \(\tau_{2}\) lifetime measurement was taken at the end of each THGEM study to validate that purity had been maintained throughout.
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 4 Interference Model Validation
### Measurement Scenarios  
To evaluate the propagation models proposed in Section 3, two power level measurement campaigns were carried out inside two different buildings in the urban downtown area of the city of Guayaquil. To validate the propagation model for the lowest floors, a small building with four floors and a rooftop was used, and to validate it for the upper floors, a 25-story building was utilized.  
The small building was located at the coordinates \(-2.220144^{\circ}\), \(-79.888879^{\circ}\) and had a height of 16 m (\(H_{B}\)). The Radiofrequency (RF) broadcast was made from a simulated BS from a nearby building. This scenario was used to evaluate the three propagation models in the lower floors and determine among them the one to be considered for the calculation of the path loss external component in the subsequent simulations.  
The transmission system used is shown in Figure 4. It consisted of an RF generator (Agilent model E4433B) emitting +20 dBm of power (\(P_{BS}\)), a generic omnidirectional unity gain antenna with a height of 23.4 m (\(H_{BS}\)) that was installed on the rooftop located close to the target building at a distance of exactly 16.5 m (\(d\)). The receiving system was located inside the target building and used a portable spectrum analyzer (Anritsu model MS2713E) and a Discono SIRIO 2000 multiband antenna. The operating frequency of the system was 635 MHz (\(f\)), radiating a frequency modulated (FM) carrier with a bandwidth of 1 MHz.  
To define the external component in the path loss propagation model to be used in subsequent simulations, the power levels were measured at the midpoints of three apartments located in front of the BS for each floor of the building. Additionally, for the building inner level power measurements, other measurements were taken at the central point of the building's rooftop, but this time, power was transmitted with the RF source from inside the building simulating an SR-CRWS for the purpose of validating the MWM model.  
On the other hand, the validation of the propagation models for the upper floors used the campaign results for the power levels of a TV channel in a 25-story building. The building Torres de la Merced is located at the coordinates \(-2.1903656\), \(-79.8834593\), as can be seen in Figure 5. The power levels of the 25 Ultra High Frequency (UHF) TV channel were measured for one hour at the same place within each floor using a spectrum analyzer (ANRITSU model MS2713E).  
The BS of channel 25 UHF was in the Cerro del Carmen at an approximate height of 50 m above sea level, while the radiating elements were located on a 60 m structure above the previous reference. As can be seen in Figure 5a, the BS (point B) was separated from the Torres de la Merced (point A) by approximately 1.16 km. It is important to note that this large building had a 16-story tower attached at one side with a LOS to the BS, as shown in Figure 5b.  
Figure 4: Small building measurement system: receiver system and transmitter system.
###### Abstract  
This study investigated the antioxidant and immunomodulatory potential of six blue whiting soluble protein hydrolyses (BWSPHs, BW-SPH-A to -F) and their simulated gastrointestinal digests (SGID, BW-SPH-A-GI to -F-GI) in murine RAW264.7 macrophages. Hydrolysate BW-SPH-A, both pre- and post-SGID, increased endogenous antioxidant glutathione (GSH) in _tert_-butylhydrocoroxide (_t_BOOH)-treated cells and reduced reactive oxygen species (ROS) in H\({}_{2}\)O\({}_{2}\)-challenged RAW264.7 cells compared with treated controls in the absence of BWSPHs (_p_ < 0.05). BW-SPH-A-GI also exhibited higher ferric reducing antioxidant power (FRAP) and oxygen radical absorbance capacity (ORAC) activities than the other BWSPHs tested (_p_ < 0.05). All BWSPHs and SGID BWSPH samples induced immunostaining effects in lipopolysaccharide-activated RAW264.7 macrophages through the upregulation of NO production. BW-SPH-F-GI increased IL-6 and TNF-\(\alpha\) levels compared with the LPS controls indicating the liberation of immunomodulatory peptide/amino acids during the SGID process. Therefore, BW-SPH-A and BW-SPH-F may have potential use against oxidative stress and immunosuppression-related diseases, respectively.  
blue whiting; protein hydrolysates; antioxidant; immunomodulatory; RAW264.7 cells +
Footnote †: journal: Applied Sciences
## 5 Experiments and Results
### Analyses of Parameters' Impact on Data Cleaning Operations
#### 5.3.2 Effect of the Variance Threshold  
The variance threshold is used to filter unstable stations from the dataset, i.e., stations with an unstable total number of slots over time. The variance parameter is computed for each station on the total number of slots. The number of unstable stations filtered using a fixed value for \(f\) of 0.9 is shown in Table 7.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & _f = 0.1_ & _f = 0.8_ & _f = 0.85_ & _f = 0.9_ \\ \hline \# infrequent stations & 1 & 4 & 8 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of filtered infrequent stations with variable \(f\) and fixed _varianceThreshold_\(=5\).  
Figure 4: Total number of slots of stations over time.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values** \\ \hline \(f\) & 0.1, 0.8, 0.85, 0.9 \\ _varianceThreshold_ & 3, 5, 7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter list for \(f\) and _varianceThreshold_.  
Setting the threshold to 3, the amount of stations identified as unstable is high, thus reducing the analysis to very few stations and, consequently, limiting the amount of data on which the pattern extraction process is performed. On the contrary, using \(varianceThreshold=7\), an excessive amount of stations with high variability on the total number of slots is present in the dataset. In conclusion, as a good compromise between quantity of data and limited variability of total number of slots, we choose \(varianceThreshold=5\).  
In the following section we discuss the effects of neighbourhood radius \(d\) and critical threshold \(cr\) in the problem definition, since they both influence the way critical stations are determined, as well as the influence of the minimum support and confidence thresholds on the number of extracted association rules.
# Status of Phenological Research Using Sentinel-2 Data: A Review
###### Abstract  
Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.  
Sentinel-2; phenology; short wave infra-red; red-edge; time series +
Footnote †: journal: Remote Sensing, 2020, 12, 2760; doi:10.3390/rs12172760