esa-sceva/satcom-chunk-collection huggingface dataset:
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 8 Results
### Ablation Studies  
We present ablation studies in order to examine the approaches we employed for offline and online adaptation more carefully. These studies were also performed on cloud detectors with FP32 weights on a Linux desktop.
# Bread Biopreservation through the Addition of Lactic Acid Bacteria in Sourdough
## 2 Materials and Methods
### Analysis of Mycotoxins in Bread Inoculated by Fungi  
The mycotoxin content of the different types of bread inoculated by _A. flavus_ and _P. verrucosum_ was extracted with the methodology used by [PERSON] et al. [22], with a series of modifications. Once the growth of the fungus was apparent in most of the bread, they were weighed in 5 g, placed in a Falcon tube with 25 mL of MeOH, and homogenized for 5 min using an Ultra IKA T 18 basic ULTRA-TURAX (Staufen, Germany). Following this, the sample was left stirring for 24 h, after which it was centrifuged at 7700\(\times\)\(g\), at 4 \({}^{\circ}\)C for 10 min. The supernatant was evaporated to dryness with Buchi Rotavapor R-200 (Postfach, Switzerland). Finally, the dried extracts were resuspended in 1 mL of methanol, filtered with 0.22 \(\upmu\)m filters, and vitalized before the analysis with UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a Q-TOF (Agilent 6540 LC/QTOF) mass spectrometer.  
The chromatographic separation of the mycotoxins was realized in an Agilent Zorbax RRHD SB-C18 (2.1 \(\times\) 50 mm, 1.8 \(\upmu\)m) column. The mobile phase employed was composed by Milli-Q water 0.1% FA (Phase A) and ACN 0.1% FA (Phase B). The gradient used was configured as: 0 min: 2% B; 22 min: 95% B; 25 min: 5% B. The column was then equilibrated 3 min before the next injection. The flow rate was established at 0.4 mL/min, and the injection volume was 5 \(\upmu\)L. For q-TOF analysis, an Agilent Dual Jet Stream electrospray ionization (ESI) was operating in positive ionization mode. The conditions of ESI were configured as follows: gas temperature: 325 \({}^{\circ}\)C; gas flow: 10 L/min; nebulizer pressure: 40 psig; sheath gas temperature: 295 \({}^{\circ}\)C; sheath gas flow: 12 L/min; capillary voltage: 4000 V; nozzle voltage: 500 V; skimmer: 70 V; scan range: 100-1500 Da; collision energy: 10, 20, 40 eV. For quantification, aflatoxin and obratoxin calibration curves were prepared, with concentration ranging from 0.01 to 10 mg/L. The integration and data elaboration were realized using MassHunter Qualitative Analysis Software B.08.00.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Dataset Description and Preprocessing  
The dataset includes 409 records of products. Each record has 7 features: category name, area, company name, transporters name, category identifier, zone identifier, merchant identifier, and transporter identifier--four categorical and four numerical. The original aim of this dataset is to classify products by category, which are presented in Table 2 below.  
Figure 2: The system model of the proposed product flows classification.  
Effective data processing is crucial for improving the suitability of a dataset for classification models. Our suggested preprocessing module consists of two vital phases: encoding categorical attributes and normalizing numeric attributes.  
In the first phase, categorical variables undergo transformation into numerical representations through the utilization of label encoding. This step is essential to ensure the proper functioning of sklearn functions, as they often require numeric input.  
Furthermore, it is important to note that after using LabelEncoder to transform classes into integers, the use of to categorical is generally necessary. After transforming the classes into integers, to categorical converts these integers into a one-hot encoding representation, a format often required by machine learning algorithms to handle multiclass classification tasks. This step is crucial to ensure data compatibility with the requirements of machine learning algorithms when dealing with classification tasks involving multiple classes.
## 4 Materials and Methods
### Genome Assembly and Gene Annotation  
The genome was assembled de novo using the filtered long reads by NextDenovo v.2.0 ([[https://github.com/Nextomics/NextDenovo/](https://github.com/Nextomics/NextDenovo/)]([https://github.com/Nextomics/NextDenovo/](https://github.com/Nextomics/NextDenovo/)), accessed on 2 February 2024; -genome_size 50m -read_type ont). Racon v.1.4.10 ([[https://github.com/lbcb-sci/racon/](https://github.com/lbcb-sci/racon/)]([https://github.com/lbcb-sci/racon/](https://github.com/lbcb-sci/racon/)), accessed on 2 February 2024) was used for error correction. The assembly was polished using filtered short reads by NextPolish v.1.1.0 [47]. Genome completeness was assessed using Benchmarking Universal Single-Copy Ortholog (BUSCO) v.4.0.3 (-l fungi_odb10 -g genome) [48].  
GeMoMa v.1.6.1 [49], GeneMark-ET v.4.0 [50], and Evidence Modeler v.1.1.1 [51] were employed for gene prediction. ncRNAs were predicted by Infernal v.1.1.2 [52] against the RNA families (Rfam) database [53]. tRNAScan-SE v.2.0.5 (-thread 4 -E-I) [54] and RNAmmer v.1.2 (-S euk -m lsu,ssu,tsu -gff) [55] were used for rRNA prediction. The predicted genes were functionally annotated using the Basic Local Alignment Search Tool (BLAST) v.2.7.1 (-e 10\({}^{-5}\)) [56] alignment against the NCBI non-redundant protein (NR) database, the Kyoto Encyclopedia of Gene and Genomes (KEGG) database, the Eukaryotic Orthologous Groups of protein (KOG) database, the Gene Ontology (GO) database, the SWISS-PROT database, Pathogen Host Interactions (PHI-base) [57], the Carbohydrate-Active enZYmes database (CAZy) [58], and the Fungal cytochrome P450 database (P450) [59]. Annotation completeness was assessed using BUSCO v4.0.3 (-l BUSCO_db -m prot) [48]. SignalP v.5.0 [60] and TMHMM v.2.0 [61] were used to predict signal peptides and transmembrane domains to identify secreted proteins with a signal peptide but no transmembrane domain. Parameter settings were maintained at the default values. Protein conserved domain analysis was performed using NCBI CD-search [62]. The \(\beta\)-tubulin (_BenA_), calmodulin (_CaM_), and RNA polymerase II second-largest subunit regions (_RPB2_) were extracted from the genome by the BLAST algorithm against those from the closely related Penicillin-type strains [22]. For phylogenetic analysis, each set of sequences (_ITS_, _BenA_, _CaM_, and _RPB2_) was aligned using the muscle algorithm, and a phylogenetic tree was constructed using the concentrated sequences by the Maximum Likelihood method in MEGA X [63].
# Simulation and Design of Three 5G Antennas
## 4 Metal Frame Mobile Terminal Dual-Element \"Loop-Groove\" MIMO Antenna Design
### The Dual-Element \"Loop-Groove\" MIMO Antenna Design Process  
In this chapter, a dual-element \"loop-groove\" MIMO antenna structure is designed to operate within the LTE band 42 (3.4-3.6 GHz) frequency range, with a central frequency around 3.5 GHz. The design utilizes an FR4 dielectric substrate with a relative permittivity of 4.4 and a thickness of 0.8 mm. A 0.6 mm thick copper strip is placed around the perimeter of the dielectric substrate to simulate the metal frame within a mobile terminal. To create the dual-element \"loop-groove\" MIMO antenna structure, the initial step involves designing a loop antenna element and a slot antenna element, which serve as the foundational components for this structure.
## 3 Results and Discussion
### Historical CH\({}_{4}\) Emission from MSW and Industrial Waste and Predicted Future Emission
#### 3.2.1 Impact of Urbanization on the CH\({}_{4}\) Emission  
In the previous section, CH\({}_{4}\) emission from municipal solid waste was discussed for the total urban population of Saudi Arabia, which presented a general overview of the status of the historical and predicted CH\({}_{4}\) emission from the landfills. However, further investigation was conducted to estimate methane emissions in different major cities in Saudi Arabia to understand the growth dynamics of municipal solid waste in these cities.  
Figure 5: Historical and predicted CH\({}_{4}\) emission from MSW and industrial waste between 1990 and 2050 (due to population variation).  
Increasing urbanization is the major cause for GHG emission increment. According to a UN report, the total city area of the world would be three times the current values and three-quarters of the world population will be living in cities in 2030 [56]. As a result, GHG emission is expected to increase drastically. In Saudi Arabia, an exceptionally high increase in population was observed in some large cities including Riyadh, Madinah, Makkah, Jeddah, and Dammam between 1975 and 2015 [56]. These cities were mainly benefitted either from the kingdom's oil revenue or Islamic pilgrimage and attracted people from rural or small urban areas as well as from abroad by providing jobs. According to the United Nations [51], the population in the aforementioned major cities increased from none in 1975 to about 14.4 million in 2015, and by 2030, another 5.1 million people are expected to be living in these major cities. The trend of increased urbanization in major cities is reflected in producing higher methane emissions in these cities as shown in Figure 6. In a decade, methane emission in Riyadh is expected to reach about 70 Gg, followed by Jeddah, Makkah, Madinah, and Dammam. Methane emissions from other cities are expected to be below 10 Gg. The results presented in Figure 6 can be utilized by the local municipalities of these cities for taking any mitigation plan in reducing methane emission from the disposal of municipal solid waste.
# Boosted Reptile Search Algorithm for Engineering and Optimization Problems
## 4 Experimental Results and Discussion
### Series of Analysis 2: RSRFT for Security in the IoT
#### 4.2.1 Dataset Description  
The proposed algorithm was assessed using the KDDCup-99, NSL-KDD, BoT-IoT, and CICIDS-2017 datasets. The KDD-99 dataset, created by the Defense Advanced Research Project Agency (DARPA) in 1988, contains network traffic data. Another kind of dataset, called NSL-KDD, was also used, which is a derived dataset with no duplicate network traffic records and grouped into four types: Probe, U2R, R2L, and DoS, in addition to normal data. The CICIDS-2017 dataset [85] was obtained from 25 clients at the Canadian Institute for Cybersecurity (CIC) using the ClCFlowMeter tool to simulate realistic network traffic obtained using protocols such as HTTPSSH, HTTPS, FTP, and email. Additionally, the Bot-IoT dataset [86] includes 3.5 million records produced from different IoT devices and features botnet attacks.The description of all of these datasets is given in Table 7.
# Design of a CW BISOL RFQ for Three Kinds of High-Charge-State Ions Simultaneous Acceleration
## 2 Beam Dynamics Design
### Errors Analysis  
During machining and assembly, some errors are introduced into the RFQ structure. To simplify the simulation, we considered the main causes, including the vane deviation and skew, as shown in Figure 14.  
Table 9 summarizes the frequency shift and dipole field components of the RFQ caused by structural errors, where the unit of kHz/(um)\({}^{2}\) indicates that the frequency shift is proportional to the square of the vane skew (\(\Delta\)H), and the unit of kHz/um indicates that the frequency shift is proportional to the vane deviation (\(\Delta\)R), respectively. The dipole field components caused by structural errors are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **ReA**[32; 33] & **FRIB**[34] & **SPIRAL**[10; 35] & **RISP**[5] & **LEAF**[36] & **BISOL** \\ \hline frequency/MHz & 80.5 & 80.5 & 88.05 & 81.25 & 81.25 & 81.25 \\ Q/A & 1/2-1/5 & 1/3-1/7 & 1/2-1/3 & 1–1/7 & 1/2-1/7 & 1/5.74-1/6.29 \\ Input energy/(keV/u) & 12 & 12 & 20 & 10 & 14 & 3 \\ output energy/(keV/u) & 600 & 500 & 750 & 500 & 500 & 504 \\ mode separation/MHz & \textgreater{}2.4 & - & - & 1.5 & 5.54 (measured) & 6.5 \\ type & 4-rod & 4-vane & 4-vane & 4-vane & 4-vane & 4-vane \\ length/m & 3.50 & 5.04 & 5.08 & 4.95 & 5.95 & 5.60 \\ V/kV & 86.2 & 60–120 & 100–113 & 50–138 & 70 & 70 \\ power consumption/kW & 160 & 15-100 & 180 & 92.4 & 53.2 & 54 \\ Q & 4200 & 16,500 & 15,040 & 14,500 & 17,963 & 17,000 \\ transmission/\% & 82 & \textgreater{}80 & \textgreater{}98 & 98 & 97.2 & 95.2 \\ Kp & 1.6 & 1.6 & 1.65 & 1.68 & 1.55 & 1.58 \\ Specific shunt Impedance & 200 & - & - & 368 (estimated) & 551 & 511 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Main parameters of International CW heavy-ion RFQs.  
Figure 14: Vane deviation (**a**) and skew (**b**) errors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Frequency Shift** & **Dipole Component** \\ \hline \(\Delta\)H & \(-0.03214\,\text{kHz/(um)}^{2}\) & \(0.0151\%/\upmu\)m \\ \(\Delta\)R & \(-0.82722\,\text{kHz/}\upmu\)m & \(-0.0137\%/\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 9: Dipole component and frequency shift due to structurl errors.
# A Review on Potential Antimutagenic Plants of Saudi Arabia
## 7 Which Are the Medicinal Plants of Saudi Arabia That Might Demonstrate Anti-Mutagenesis?
### Euphorbia schimperiana  
The family Euphorbiaceae contains several plants that range from woody trees to simple weeds. Different species of the Euphorbia plant ([PERSON], 1844) have been known since ancient times for their medicinal activities. The plant grows well in moderate temperate areas and can be found in the western provinces of Saudi Arabia (98). The plant and its extract are reported to exhibit a range of pharmacological actions, such as analgesic, anti-ulcer, antioxidant, anti-inflammatory, antiviral, anti-spasmodic, skin allergy and anti-cancer actions. The important active ingredients that were identified for the above activities are triterpenes, flavonoids, alkaloids, tannins, and sterols. The presence of specific diterpenes such as phorbids, ingenane, tigliane, and daphnane are reported for their potential pharmacological activities [98]. In addition, the phenolic components such as quercetin-7-O-\(\beta\)-D-glucoronside and 3-methyl-quercetin-7-O-\(\beta\)-D-glucoronside were identified for their antioxidant activity by the DPPH free radical assay [99].
## 3 Results and Discussion
### Effect of Conventional Washing of Fruits on the Content of Pesticides  
Figures 15 and 16 illustrate the levels of pesticides in apples from the harvests of 2012 and 2020, respectively, subjected to conventional washing with tap water. The experiment demonstrated that conventional washing of fruits does not cause any reduction of the number of identified pesticides, i.e., 26 pesticides were detected in 2012 and 7 pesticides in samples of apples from 2020. Analysing the levels of residues of the particular pesticides, no significant differences were noted between samples of apples washed with water and those which were not subjected to the process of washing.  
[PERSON] et al. [18] also demonstrated a low effectiveness of washing apples for 2 minutes in tap water in the removal of residues of pesticides that had been introduced as standard into the analysed samples.  
In a study conducted in 2015 in Iran, it was demonstrated that 67% of the analysed samples of apples contained pesticide residues [92]. The cited report describes the effect of washing and peeling on the reduction in the levels of residues of three pesticides (diaznon, chlorpyrifos and abametcin). Washing with water for 3 minutes caused a reduction in the content of the pesticides in the apples by as little as 17%, while peeling reduced the content of diaznon and chlorpyrifos by an average of 80% [92].  
[PERSON] et al. demonstrated that washing with water, with water with a detergent, and peeling, resulted in a reduction in the level of malathion by 34.4%, 55.8%, 60.6% and 74.7%, respectively [93]. The washing of agricultural produce with water or with other disinfecting solutions causes the removal of only a part of pesticide residues from the surface, and also contributes to a reduction in further penetration of pesticides from the peel to the flesh of fruits and vegetables. Literature data on the effect of washing on the level of pesticide residues in fruits and vegetables are inconsistent, and all of them indicated an absence of correlation between solubility in water and the reduction in pesticides after washing [87].  
Figure 15: Content of pesticides in samples of apples washed with water (2012).
# Smart Grid for Industry Using Multi-Agent Reinforcement Learning
## 5 Case Study
### Results and Discussion  
The three approaches have been benchmarked on ten selected production shifts based on energy and production data from the production site. Before this, MARL was trained for 2500 episodes, which took about 5 days. The average of resulting costs of the ten shifts is displayed in Figure 9.  
It became clear that the presented MARL system outperformed the reactive control approach (PCS) on production and energy costs. In contrast, the predictive, SA-based algorithm (RCS) achieved, in total, roughly 35% better results than the MARL approach. MARL thereby achieved lower average energy costs than SA, although the production costs were up by 40%. This indicated that MARL is stuck in a local optimal, which focused on the energy cost reduction, although there is still great potential on the production cost side. However, regarding the calculation time, an important advantage of MARL became clear. After training, the MARL system was able to compute decisions within seconds, whereas it took up to 2.5 h for the SA algorithm to calculate a new solution when new stochastic events occurred. Due to this weak reactivity, the application of SA was not suitable for the case in question. In a real-life scenario with stochastic events, decisions have to be made within a few seconds. The behavior of both the system and the market and battery agent is displayed in Figure 10 for an exemplary shift. The upper part of the figure shows the initially available electricity, which consisted of the pre-purchased base load and PV power; the resulting availability and total energy consumption are also displayed. It became clear that the available electricity can be widely adapted to the energy consumption. Regarding the ad-hoc power supply, the CHP was mainly run at maximum power, while the flexibility of the battery was being exploited. On the market side, power was bought in the second half of the shift, when the battery was empty and the prices low, then sold at the end of the shift when the energy demand dropped due to the fact that most jobs had already been machined.  
In summary, the results show that the developed approach was able to control the three considered options of energy self-supply, battery, and short-term electricity market as well as a production system. Compared to a simple rule-based control strategy, all of the various cost factors could be minimized jointly while also taking battery degradation costs into account.
## 5 Vibration Signal Pre-Processing  
Each original vibration signal is subdivided into five signal fragments of two seconds each, of 100,000 samples. The MFCC features are calculated for each of the five signal fragments. The spectrogram is calculated using a Hanning window of size 40 ms for each signal fragment. This window is moved along the signal with a stride of 0.5 ms. To calculate the energy bands during calculation of the MFCC, the number of filters in the filter bank is set to \(J=64\). Such filters are calculated with filter centers linearly spaced in the range [0, 1000] Hz and filter centers logarithmically spaced in the range (1000, 25,000) Hz. After processing with the filter bank and obtaining the MFCC coefficients, an array of size \(32\times 3931\) is obtained for each signal fragment. Similarly, the \(\Delta_{m,t}\) and \(\Delta\Delta_{m,t}\) derivatives are calculated from the MFCC array previously obtained. The calculation of \(\Delta\) features in Equation (9) is performed by using a filter of size 11 to obtain a good approximation accuracy with moderate noise amplification. The coefficients correspond to 1-D higher order derivatives as detailed in [51]. The FIR derivative filter of size \(N=11\) has coefficients \(\Theta(t)=[5,4,3,2,1,0,-1,-2,-3,-4,-5]\) and the resultant \(\Delta\) array has a size of \(32\times 3931\). In addition, the \(\Delta\Delta\) is estimated from the \(\Delta\) array by using an FIR filter of size 5 with coefficients \(\Phi(t)=[2,1,0,-1,-2]\). The lower window size reduces the noise amplification of the second derivative. The \(\Delta\Delta\) array also has a dimension of \(32\times 3931\). The concatenation of the MFCC, \(\Delta_{m,t}\) and \(\Delta\Delta_{m,t}\) along the frequency axis while maintaining the temporal axis is proposed in this work. Each feature array (MFCC, \(\Delta_{m,t}\) and \(\Delta\Delta_{m,t}\))is processed with the Z-score normalization and then concatenated to obtain an array of size \(96\times 3931\).  
The subdivision of each recorded vibration signal into five segments allows for augmenting the signal dataset by a factor of five. Then, the dataset is augmented to 3900 signal fragments for the centrifugal pump and 1275 signal fragments for the reciprocating compressor. Each signal fragment is represented by a feature array of size \(96\times 3921\).  
The feature space for two vibration signals acquired from the multi-stage centrifugal pump is shown in Figure 6. The feature space for the healthy centrifugal pump is in Figure 6a while the feature space for a vibration signal with the faulty condition is shown in Figure 6b. In this representation, the presence of faults could be indicated by events with increased energy located at a certain frequency band (rectangle C) or events located at specific time locations with increased (or decreased energy (rectangles A and B). The first and second derivatives are located at the top of the figure, and a simple visual inspection cannot analyze their high-frequency content.
## Materials  
### Study Areas and Crops  
The selected study sites are located in Belgium, France and Morocco (Figure 1). Silage maize and common wheat are the most dominant crops in the selected sites.  
From Northern Europe to Northern Africa, the average daily temperature and potential evapotranspiration increases, and the average cumulative rainfall decreases. The regions were selected to capture this trend in climate regimes. Figure 2 shows the long term averages of temperature, precipitation, Penman-Monteith potential evapotranspiration (PET) and water balance computed per region based on meteorological data obtained from the JRC-MARSOP project [63]. The water balance was calculated using the Thornthwaite-Mather method as precipitation minus PET [64]. Water and heat stress are the major factors that influence arable yields in Belgium [65; 66] and France. The total annual water used for irrigation across France can amount to 80% of the crop water use during dry periods, while on average 10%-20% is used during a typical growing season [67]. In Morocco, most arable crops are rain fed, having frequent dry periods with high temperatures and irregular rainfall [68].  
Figure 1: Study areas with study crops in Belgium, France and Morocco.  
Figure 2: Cumulative monthly rainfall, PET (potential evapotranspiration), water balance (accumulated monthly water deficit/surplus) (mm) and average monthly temperature (\({}^{\circ}\)C) during the 1999–2012 period for study sites in Belgium (**a**); France (**b**) and Morocco (**c**). Error bars show the standard deviation for the meteorological indicators.