esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Predicting the energy consumption of buildings plays a critical role in supporting utility providers, users, and facility managers in minimizing energy waste and optimizing operational efficiency. However, this prediction becomes difficult because of the limited availability of supervised labeled data to train Artificial Intelligence (AI) models. This data availability becomes either expensive or difficult due to privacy protection. To overcome the scarcity of balanced labeled data, semi-supervised learning utilizes extensive unlabeled data. Motivated by this, we propose semi-supervised learning to train AI model. For the AI model, we employ the Belief Rule-Based Expert System (BRBES) because of its domain knowledge-based prediction and uncertainty handling mechanism. For improved accuracy of the BRBES, we utilize initial labeled data to optimize BRBES' parameters and structure through evolutionary learning until its accuracy reaches the confidence threshold. As semi-supervised learning, we employ a self-training model to assign pseudo-labels, predicted by the BRBES, to unlabeled data generated through weak and strong augmentation. We reoptimize the BRBES with labeled and pseudo-labeled data, resulting in a semi-supervised BRBES. Finally, this semi-supervised BRBES explains its prediction to the end-user in nontechnical human language, resulting in a trust relationship. To validate our proposed semi-supervised explainable BRBES framework, a case study based on Skellftea, Sweden, is used to predict and explain energy consumption of buildings. Experimental results show \(20\pm 0.71\%\) higher accuracy of the semi-supervised BRBES than state-of-the-art semi-supervised machine learning models. Moreover, the semi-supervised BRBES framework turns out to be \(29\pm 0.67\%\) more explainable than these semi-supervised machine learning models.  
accuracy; augmentation; building energy; explainability; pseudo-labeled data; self-training; semi-supervised learning; uncertainties; unlabeled data +
Footnote †: journal: _Algorithms_
## 3 Space Segment
### Instrument
#### 3.1.2 Spectral and Radiometric Performance
Because of EnMAP's prism-based design, multiplicative shot-noise is the main contribution to the measurement noise at medium and high radiance levels, causing the spectral SNR to roughly scale with the square root of the incoming at-sensor radiance. This explains that the SNR in the examples in Figure 5 is highest for the brighter kaolinite spectrum and lowest for the water spectrum and that there is a drop in SNR at the spectral regions in which incoming radiance decreases because of atmospheric absorptions (see the TOA radiance spectrum in Figure 3). Regarding the EnMAP 14-bit radiometric resolution, the instrument electronics in the VNIR are designed to automatically switch the radiometric gain between high and low as a function of the incoming radiance, which is encoded in the last one of the 14 bits. This allows covering the entire dynamic range of the instrument with a linear relationship between the input radiance levels and the digital numbers registered at the detector and, at the same time, provides a sufficient radiometric sensitivity at the lowest radiance levels covered by the high gain mode. The red-green-blue (RGB) composites in Figure 6 show the effects of this dual radiometric gain system. The transitions from pinkish to yellowish patches over  
Figure 5: Simulated spectral signal-to-noise ratio (SNR) for incoming radiance levels corresponding to a Sun zenith angle (SZA) of 45\({}^{\circ}\) and surface reflectance spectra representing different EnMAP application fields. bare soil surfaces in the RGB image derived from digital number data correspond to areas in which the radiometric gain has been switched. This effect is corrected during radiometric calibration so that the effect of the two gains is no longer visible in radiometrically-calibrated radiance data.
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## 2 AODV and Modified AODV Routing Algorithms
### Modified AODV
Here, we assume that if the angle is less than 20 degrees, then the nodes are moving in the same direction [4].
## 1 Introduction  
Many countries such as Indonesia [1; 2], Malaysia [2], Bangladesh [3], and China [4] have thriving textile industries. Reactive dyes dominate the dye market because of their low cost, simple dyeing processes, and improved fastness attributable to their reaction with cellulosic fibers [5]. Textile industries generate large amounts of wastewater that contains unfixed residual dyes. Direct discharge of textile wastewater into receiving water bodies causes severe environmental pollution because of its intense color and toxicity. Several methods such as chemical adsorption [6], photodegradation [7], and ozonation [8; 9] have been assessed to address difficulties related to contamination of wastewater by effluent [6; 7; 8; 9]. Nevertheless, these methods entail high investment costs and operational difficulties. Because of the complex aromatic structure and stability of azo dyes, conventional biological treatment methods are inadequate for dye removal [2; 6].  
Recently, many studies have examined the application of constructed wetlands (CWs) for cost-effective treatment of dye wastewater [10; 11; 12; 13; 14]. Treatment in CWs specifically emphasizes dye adsorption by soil, degradation by microbial activity, and phytaccumulation by plant uptake. In particular, the role of plants in CWs has attracted great interest. [PERSON] and [PERSON] [13] reported greater than 90% color removal and cleavage of azo bonds of Yellow 2G by vertical flow CWs planted with cattails (_Typha angustifolia_ L.) and _Canna indica_. [PERSON] and [PERSON] [11] reported that Reactive Red 2 (RR2, MW = 615), with smaller molecular size, was taken up more easily than Reactive Red 120 (MW = 1469) or Reactive Red 141 (MW = 1775), with larger molecular size, by burhead (_Echindorns comidifolia_ L.) under soil-free conditions. By contrast, [PERSON] and [PERSON] [14] reported that the presence of common reed (_Phragmites australis_) did not affect removal of Acid Blue 113 or Basic Red 46.  
Our research group has surveyed CWs installed to treat Batik wastewater in Pekablongan city, Indonesia [1]. Cattail, papyrrus, and heloconia were planted in the CWs, but the role of the plant in dye removal was unclear [1]. This study specifically examines the roles of aquatic plants in CWs for removing Reactive Yellow 86 (RY86), commonly used for dyeing textiles including garments, fabrics, and home textiles, from synthetic wastewater. Reportedly, RY86 is degraded by photolysis using multi-structured Fe\({}_{2}\)O\({}_{3}\) nanoparticles as catalysts [15; 16; 17], the photo-Fenton process [18], electrochemical treatment [19], and lactose released by _Bacillus safensis_[20]. As far as we know, no report of the relevant literature describes RY86 removal by CWs. For this study, a 90 day experiment was conducted to remove RY86 from wastewater using lab-scale CWs planted with cattail and papyrus.
## 3 Results and Analysis of the Tests  
### Material Strength  
Table 3 shows the formulation of the concrete (nominal compressive strength of 40 MPa) used in the PC slab specimens. To measure the actual concrete compressive strength, three 100 mm \(\times\) 200 mm concrete cylinders were tested for each specimen on the day of the test, according to ASTM C39M-20 [35]. The average value of the three concrete cylinders is presented in Table 2 for each specimen, with a concrete compressive strength \(f_{c}\) of 43.9-59.0 MPa.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Nominal Strength** **(MPa)**} & \multirow{2}{*}{**W/C (\%)**} & \multicolumn{5}{c}{**Unit Weight (kg/m\({}^{3}\))**} \\ \cline{3-6}  & & **W 1** & **C 2** & **S 3** & **G 4** & **SP 5** \\ \hline
40 & 34.0 & 160 & 471 & 829 & 933 & 4.24 \\ \hline \hline \end{tabular} \({}^{1}\) W = water. 2 \({}^{2}\) C = concrete. 3 \({}^{3}\) S = sand. 4 \({}^{4}\) G = gravel. 5 \({}^{5}\) SP = super plasticizers.  
\end{table}
Table 3: Concrete mixture proportions.  
Table 4 shows the yield and tensile strengths of the reinforcing bars and prestressed wire strands used in the specimens. To evaluate the mechanical properties of the reinforcing bars, lattice bar, and the prestressed wire strands, three coupons were tested for each type, according to ASTM E8M-16 [36], and the average values are presented in Table 4. The yield strengths of the steel were defined using the 0.2% offset method for materials with unclear yield plateaus.
## 2 Materials and Methods
### Description of the Intelligent Irrigation Control System (IICS)  
In order to achieve precise irrigation, the IICS based on the IoT was developed (Figure 2). The IICS consists of a soil moisture monitoring system and an automatic irrigation system. It integrates sensors, controllers, actuators and communication equipment. Web-based software can monitor real-time information such as soil moisture, irrigation volume, irrigation times and valve status (open/close).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Soil Properties** & **Value** \\ \hline soil organic matter & 10.36 g/kg \\ total nitrogen & 0.95 g/kg \\ total phosphorus & 0.28 g/kg \\ clay & 14.5\% \\ silt & 40.8\% \\ sand & 44.7\% \\ field capacity & 25\% \\ soil bulk density & 1.39 g/cm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil properties in the experimental field.  
Figure 2: Design of intelligent irrigation control system (IICS). The IICS consists of a soil moisture monitoring system and an automatic irrigation system.
## 4 Experiments and Results
### Basic Settings
#### 4.1.4 Comparison Methods  
In this paper, three state-of-the-art deep learning methods for short-term OD prediction in URT including ConvLSTM [27], STResNet [6], and CASCNN [17] were selected as the comparison methods of the proposed PIANet. Compared with the original paper, the implementation details of the three comparison methods were fine-tuned to make them more suitable for the dataset in this paper. Specifically, in terms of use of input data, the inputs of ConvLSTM and STResNet are the same as that of the proposed PIANet. For CASCNN, the inflow and outflow series of the same day and the finished OD matrices of previous days were used the same as the original paper. The batch sizes of ConvLSTM, STResNet, and CASCNN were 16, 32, and 32, respectively. Moreover, for ConvLSTM, STResNet, and CASCNN, the learning rates and loss functions were set to 0.001 and \(L_{2}\) loss, respectively.
# BODIPY-Based Nanomaterials--Sensing and Biomedical Applications
## 2 Metallic Nanoparticles@BODIPY
### Cancer Diagnosis and Therapy  
#### 2.2.1 Gold Nanoparticles  
[PERSON] and co-workers obtained a nanoprobe for cancer cells based on surface-enhanced Raman scattering (SERS) [69]. It was based on synthesized amino aza-BODIPY deposited on a gold nanoparticle (Figure 15). The obtained conjugate revealed an intense Raman signal with a detection rate at the concentration of 0.4 \(\upmu\)M. To increase the selectivity, the Raman nanoprobe was functionalized with EGFR (epidermal growth factor receptor) and closed in PEG. Performed studies indicated high recognition of human fibrosarcoma cancer cells (HT1080 line), whereas simultaneously normal human cells (3T3L1) remained unaffected [69].  
[PERSON] and co-workers developed a drug delivery system based on gold monolayer nanoparticles [70]. The authors used \"hydrophobic pockets\" reported previously by [PERSON] and [PERSON] [71]. On the gold surface, zwitterionic chains were attached, and in the formed pockets, BODIPY molecules were loaded as a fluorescent probe (Figure 16). The authors concluded that dye is delivered by the monolayer-membrane transfer process. Moreover, only low uptake of the nanocar bearing zwitterionic chains into the MCF-7 cells (human breast cancer) was confirmed. Efficient delivery into the cytosol was noticed after 2 h of particles incubation with the cells. BODIPY-loaded nanoparticles sustained stability for over a month [70].  
Figure 15: The chemical structure of human fibrosarcoma cancer cells nanoprobe based on aromatic-substituted aza-BODIPY dyes and gold nanoparticle [69].  
[PERSON] and co-workers studied the usability of new gold nanoparticles coated with BODIPY and L-tryptophan in photodynamic therapy (Figure 17) [72]. They concluded that the new nanoparticle is an efficient singlet oxygen generator with a quantum yield \(\Phi\Delta\) of 0.46. Moreover, in dark conditions, the nanoparticle was not active against C6 rat glioma cells (80% viability), whereas the viability decreased to ca. 25% after excitation of the nanoagent at a concentration of 100 \(\upmu\)g/mL with light. The influence of the nanoagent on normal living cells was checked via incubation with normal fibroblasts derived from mice (L292). As a result, the viability of cells was found to be greater than 80%, pointing to high biocompatibility [72].  
Figure 16: The chemical structure of gold nanoparticle with zwitterionic chains forming pockets where BODIPY molecules were loaded [70].
## 6 Activity Against TB and Combination Study of Delpazolid with Other Anti-TB Agents
As indicated in Table 6, a checkerboard assay was performed to identify pre-existing anti-TB medications with potential synergistic effects with delpazolid. \begin{table}
\begin{tabular}{l l l l l} \hline
**Ref. Drug** & **MIC (\(\mu\)g/mL)** & **Tested TB Drugs** & **MIC (\(\mu\)g/mL)** & **FIC index** & **Activity 1 ** \\ \hline \multirow{10}{*}{Delpazolid} & \multirow{4}{*}{1} & Isoniazid & 0.13 & 1.13 & I \\  & & Rifampicin & 0.06 & 0.75 & Ad \\  & & Rifapentine & 0.01 & 0.75 & Ad \\  & & Ethambutol & 0.50 & 1.02 & I \\  & & Cycloserine & 4.0 & 1.02 & I \\  & & Amikacin & 0.04 & 1.02 & I \\  & & Streptromycin & 0.25 & 1.02 & I \\  & & Capreomycin & 0.31 & 1.02 & I \\  & & Mosifloxacin & 0.06 & 0.75 & Ad \\  & & Levoglucacin & 0.25 & 0.75 & Ad \\  & & Clofazimine & 0.25 & 0.52 & pS \\  & & Bedaquiline & 0.25 & 0.53 & pS \\  & & Delamanid & 0.02 & 0.75 & Ad \\  & & Ethionamide & 0.5 & 1.03 & I \\  & & \(p\)-aminosalicylic acid & 0.02 & 1 & I \\  & & Pyrazinamide 2 & 200 & 0.63 & pS \\ \hline \end{tabular}
\end{table} 
Table 6: MICs of selected anti-tuberculosis compounds against _M. tuberculosis_ H37 Rv and corresponding interaction profiles with delpazolid assessed by checkerboard. Figure 3: Intracellular activity of delpazolid. The activity of delpazolid on intracellular _M. tuberculosis_ was compared to linezolid in bone marrow-derived macrophages (BMDMs) at three days after infection. The experiment was performed in triplicate, and the results are shown as the mean \(\pm\) standard error of the mean (SEM). SC, solvent control. The assay revealed that delpazolid has partial synergism with clofazimine, bedaquiline, and pyrazinamide. Based on the results, in vitro time-kill kinetics tests were conducted by combining delpazolid with clofazimine and bedaquiline (Figure 4). Using the MIC against _M. tuberculosis_ H37 Rv for each drug, changes in colony-forming units (CFU) with monotherapy or combination therapy were evaluated. In addition, based on the MICs, synergistic effects between delpazolid plus bedaquiline and delpazolid plus clofazimine were evaluated at varying doses. Although the CFUs decreased at the MIC of a single drug, regrowth was observed over time. However, when delpazolid was combined with bedaquiline or clofazimine, using the 0.5\(\times\) MIC of each drug, no regrowth was observed (Figure 4). In addition, the combination of bedaquiline and clofazimine with delpazolid consistently suppressed the growth of _M. tuberculosis_ H37 Rv, exhibiting high synergistic effects with 1\(\times\) MIC delpazolid (1 \(\upmu\)g/mL) and 0.5 \(\times\) MIC clofazimine (0.25 \(\upmu\)g/mL), resulting in a 2 log CFU reduction in _M. tuberculosis_ H37 Rv. Synergy between two new antimycobacterial compounds, such as delpazolid and bedaquiline or clofazimine, offers an attractive foundation for a new tuberculosis regimen.
## 3 Results and Discussion
### Characterization and Assessment of Nanolubricants and Nanobiolubricants
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Property**} & **Lubricant (Pure)** & **Nanolubricants** & **Nanolubricants** & **Nanolubricants** & **Nanolubricants** \\  & **Mistral 15W40** & **0.25\% SiO\({}_{2}\)** & **0.50\% SiO\({}_{2}\)** & **0.75\% SiO\({}_{2}\)** & **1.00\% SiO\({}_{2}\)** \\ \hline Appearance & dark yellow & dark yellow & dark yellow & dark yellow & dark yellow \\ Small & characteristic & characteristic & characteristic & characteristic & characteristic \\ Density at 15 \({}^{\circ}\)C [g cm\({}^{-3}\)] & 0.8769 & 0.8808 & 0.8819 & 0.8831 & 0.8842 \\ Viscosity at 40 \({}^{\circ}\)C [cSt] & 97.3 & 97.6 & 98.4 & 99.0 & 99.9 \\ Viscosity at 100 \({}^{\circ}\)C [cSt] & 13.5 & 13.7 & 13.8 & 13.9 & 14.0 \\ Viscosity Index [–] & 138 & 141 & 142 & 142 & 143 \\ TAN [mg(KOH) g\({}^{-1}\)] & 2.17 & 1.9 & 1.9 & 2.1 & 2.2 \\ TBN [mg(KOH) g\({}^{-1}\)] & 10.6 & 10.7 & 10.3 & 10.3 & 10.3 \\ Pollution–silicon [ppm] & 65 & 419 & 738 & 1101 & 1365 \\ Pollution–sodium [ppm] & 0 & 0 & 0 & 0 & 2 \\ Additives–calcium [ppm] & 3045 & 3118 & 3124 & 3084 & 3043 \\ Additives–magesium [ppm] & 71 & 71 & 72 & 74 & 70 \\ Additives–zinc [ppm] & 928 & 926 & 923 & 915 & 889 \\ Additives–phosphorus [ppm] & 766 & 827 & 798 & 809 & 795 \\ Additives–boron [ppm] & 137 & 131 & 131 & 130 & 126 \\ Additives–molybdenum [ppm] & 6 & 9 & 9 & 9 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Physicochemical Properties of Nanolubricants. slight decreases are observed in the levels of zinc and boron. While these changes are minor, they should be monitored to ensure that they do not affect the performance of the lubricants over time. Additionally, molybdenum shows a slight increase in nanolubricants, which could potentially enhance certain lubricant properties such as anti-wear performance. Interestingly, in nanobiolubricants, molybdenum levels remain stable, suggesting a differential interaction between the base fluids and the nanoparticles.
## 3 Results
### Evaluation
#### 3.2.2 Usability Survey  
For the usability survey, a tailored questionnaire was put together by the authors. It contains 24 items collecting the participant's overall impression of the application and their opinion about specific aspects of its three main functions, as well as a self-assessment of their own SNOMED CT knowledge. For 20 items, an ordinal scale with five options ranging from worst (\"1\") to best (\"5\") is employed; the remaining four items are open-ended questions. None of the items are mandatory and no answer options are preselected. The complete questionnaire including answer options is available in the Appendix A.  
The survey was conducted with three local participants, all being fairly knowledgeable about SNOMED CT postcoordination but new to WASP. Each participant received the questionnaire beforehand, as well as a short introduction to WASP's functionality. Then, the application could be explored freely and without time constraints. Afterwards, the participants completed the questionnaire anonymously.  
The results were collated and aggregated as visualized in Figure 8. Both functionality as well as usability were largely considered favorable, as 78% of items (14 of 18) received only positive values (\"4\" or \"5\"). For the remaining four questions, there was one neutral answer (\"3\") in three cases and one negative answer (\"2\") in a single case. The former depicts occasional difficulties in all of WASP's three main functions, while the latter expresses a participant's wish for more information on incorrect entries. Consistent with this, information fields and tooltips were rated as very useful throughout. As for the application's principal goal, all participants confirmed that they were completely able to create PCEs and Templates according to their needs.  
Only one participant added free-text comments (see Appendix A), which include several small suggestions for optimization. Among other aspects, two bugs were reported, and an improvement for the Attribute range selection was described. Additional features were requested as well, including the reordering or deleting of Attribute relationships during Template generation, an automated example display term for the Term Template and an import/export function for PCEs and Templates.
## 2 Simulation of Blast Events
### Discussion of State of the Art on Blast Load Analyses of Tank Structures
Of course, a blast pressure load is only slightly comparable to a wind load, but analogies should still be present. Thus, without further numerical CFD or experimental investigations, no clear general statement can be made about load application on tank roofs based on the current state of the art. Therefore, in order to be able to make a reliable estimate of the performance of the tank due to a blast, two different load cases for the roof blast load are analyzed within the scope of this study: on the one hand, the load is applied as a compression load according to the relationships presented in Section 2.2. On the other, the sign of all load values is reversed while the value of the load remains identical in order to also analyze a suction load based on the recommendation from [46] and the Eurocode wind load approach on dome roofs [50]. A look at the available design criteria reveals regulatory gaps. The specification of damage ratios in [23] seems reasonable, but these must be adapted more to the consequences of damage to tank structures. Therefore, for the observation of the possible impact to a tank structure withstanding a significant blast load event, it should be considered to define the performance level of the tank structure in the way it is described in European and American standards for seismic design classification. There are two main performance levels, which can be derived from the seismic design consequence criteria:  
1. OL (operating level)/OB (operating basis): After impact by a blast load event, the tank structure will remain operational. 2. CL (contingency level)/SS (safe shut-down): After impact by a blast load event, the tank structure may be damaged but without the loss of overall integrity and containment. As the event of a blast load impact is considered a singular event in the design lifetime of a tank system, the adoption of the consequences defined for the contingency level (CL)/safe shut-down (SS) should be considered. The limit values for the ductility ratio \(\mu\) proposed by [23] can be used for this purpose. As no values are explicitly given for tank structures, the lowest suggested value (\(\mu=1\)) should be conservatively adapted.