esa-sceva/satcom-chunk-collection huggingface dataset:
# Introducing Personal Teaching Environment for Nontraditional Teaching Methods
## 5 Findings: Research Questions 2 and 3
### Case Study  
The case study teaching method is one of the popular nontraditional teaching approaches that allows students to develop and present an in-depth view of a specific situation, event, or entity [33]. This allows teachers to help students to interact with real-life situations to enhance student's critical thinking, insight, problem-solving skills, analysis capabilities, and skills required for decision making [15; 66]. The authors of [67] illustrate two ways to use a case study in the classroom. In the first way, students have to read a case study and then describe the nature of the strategy, research, or intervention. Next, students analyze and evaluate the case study content in the situation. At last, students choose among the choices to be discussed in the case study. In the second way, the teacher illustrates problems and limitations in a case study. The students' role is as a consultant in this situation. A class discussion or small team discussion is then required to discuss the options available to solve the problem. The authors of [68] support the latter option, as the students have the advantage of improving their problem-solving skills. According to [69] and as an adaption of [70], a design chart of the case study methodology is presented in Figure 7. It contains four stages. First, the case study design includes the adoption of the required skills and development and review of the case. Second, conducting the case study includes data collection preparation, distributing the questionnaire, and conducting interviews. Third, analyzing the case study evidence includes the analytic strategy used to understand the issues and challenges from multiple perspectives [71]. Finally, developing the conclusions, recommendations, and implications includes evaluating the advantages and disadvantages of the proposed solutions and their impact in the short and long term [71].  
In 2013, the authors of [67] investigated students' perceptions regarding the implementation of the case study. The authors found that most students showed a positive perception of using a case study as a nontraditional teaching method, and they were willing to use it in the future. The author of [72] examined students' critical thinking abilities while using case studies. The author found that case studies help students promote active learning and play a vital role in developing critical thinking skills. The authors of [73] examined students' performance regarding conceptual understanding and perceptions while using a case study. As a post-test result of assessing student learning and engagement from cases, they found  
Figure 7: Adapted case study design chart.  
improvements in students' conceptual understanding engagement and connections to the real world, and those were significantly better than traditional teaching methods.
# Green IoT: A Review and Future Research Directions
## 6 Energy-Efficient Radio-Frequency Identification  
An RFID setup includes unique identification tags (EPC), a reader, and middleware communication (Figure 10). RFID tags, which are made of a microchip and an antenna, are affixed to objects that need to be tracked. The RFID reader does not need to have a line of sight (LoS) to the tag, so the tag can be embedded within the object. The microchip on the RFID tag holds the electronic product code (EPC) and any other necessary information for tracking. These data can be tracked and read by RFID readers from any location. An RFID reader is a device, either stationary or portable, that is connected to a network and has an antenna. It transmits power, data, and commands to the RFID tags. The RFID reader serves as a gateway for objects tagged with RFID, allowing the data stored on the tags to be accessible to business systems. RFID systems have a limited transmission distance (a few meters) and operate on various frequencies, ranging from the low frequency band of 124-135 kHz to ultra-high frequencies of 860-960 MHz [105].  
There are several different classifications of RFID tags, as given in Figure 11. However, we will focus in this study on the classifications in terms of energy.  
RFID tags can be separated based on their input power supply: Active RFID tags have an internal power source, typically a battery, and can transmit a signal over a long distance. Passive RFID tags do not have a power source and rely on energy from the RFID reader's signal to power the tag's microchip. These tags have a shorter transmission range. However, a semi-passive RFID tag has a power source but only uses it to power the microchip, not for transmitting a signal [106].
# Performance Comparison of Selected Filters in Fast Denoising of Oil Palm Hyperspectral Data
## 1 Introduction
A self-modulating convolutional neural network (SM-CNN) was introduced by [PERSON] et al. (2024) by implementing correlated spectral and spatial information. Adaptive feature transformation based on the adjacent spectral data novel block named the self-modulating residual block (SSMRB) acted as the core of the model, resulting in enhancing the network's ability to handle complex noise and adapting its predicted features while denoising every input HSI with respect to its spatial-spectral characteristics [30]. [PERSON] et al. (2016) proposed the use of spatial-spectral sparse unmixing to further enhance the unmixing and denoising performance. The method utilized a priori known spectral libraries and calculated spatially smooth abundances for the sparse number of prominent signatures detected in the image. These signatures and their respective abundances were used to reconstruct a denoised version of the original hyperspectral image [31]. In summary, denoising or noise filtering of hyperspectral data are implemented using two main approaches, and they are the spatial-spectral and the deep learning neural network approaches. In most of the studies, the approaches were tested on data that had been spiked with noise. In this work, we propose a filtering framework that is effective and fast to implement. The effectiveness of the approach is tested on some channels of real oil palm hyperspectral data that are inherently noisy.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 2 Cultivation Conditions
### Digestate as Nutrient Source  
Digestate occurs in large quantities as residue of biogas production and contains valuable nutrients. Therefore, and as proven by various publications, it is considered a potential nutrient source for microalgae cultivation (Section 5). When using digestate as nutrient source, different aspects along the whole process chain (raw digestate, digestate treatment,microalgae cultivation, downstream processing, and products) must be considered. An overview of the steps is given in Figure 1.  
Most of the variables described above are influenced when digestate is used as nutrient source instead of mineral medium [17; 46; 47; 48]. The composition of anaerobic effluents is directly connected to the feedstock used for the anaerobic digestion, which is described below in more detail (Sections 3 and 4). The components have a direct effect on the available nutrients, pH value, and salinity of the cultivation medium (Figure 2). In turn, the cultivation temperature and pH value determine the solubility of nutrients as well as potentially harmful substances present in the digestate. Additionally, solids or other components lead to a dark coloration of the cultivation medium, which means that light of certain wavelengths is absorbed. Thus, the light energy available for photosynthesis is decreased. To conclude, complex interdependencies of variables determine the organism's environment and its growth performance in every cultivation.  
Figure 1: Overview of possible process steps for obtaining microalgae products by using digestate as nutrient source. Solids separation during digestate processing and microalgae harvesting can be enhanced by using precipitating or flocculating agents.  
Figure 2: Overview of important variables for microalgae cultivation and the possible influences of digestate as nutrient source.
## 2 Materials and Methods
### Quantification of Major Aroma Compounds and Polyols  
Gas chromatography using an Agilent 6890 GC (Santa Clara, CA, USA) was employed to analyze the major volatile compounds (concentration >10 mg/L) in wine. These compounds are evaporative under room temperature and play a significant role in the organoleptic characteristics of wine. The chromatograph features a flame ionization detector (FID) and a \"CP-Wax 57 CB\" column specifically prepared for this analysis. The column dimensions are 60 m \(\times\) 0.25 \(\upmu\)m \(\times\) 0.2 \(\upmu\)m. For each replicate, 0.7 \(\upmu\)L of sample is injected, and the total elution time for the major volatiles and polyols being quantified is 80 min [21]. Before sample injection into the chromatograph, 10 mL of wine sample was treated by adding 1 mL of 1.018 g/L 4-methyl-2-pentanol (CAS 108-11-2) as an internal standard in a 14% (\(v/v\)) ethanol solution and 0.2 g of solid calcium carbonate. The prepared mixture underwent sonication in an ultrasonic bath for 30 s and was centrifuged at 5000 rpm for 10 min at 2 \({}^{\circ}\)C to remove tartaric acid from the wine. The obtained supernatant from this process was then injected for analysis. The absolute quantification of methanol, higher alcohols (1-propanol, isobutanol, isoamyl alcohol, and 2-phenylethanol), 1,1-diethoxyethane, acetaldehyde, acetoin, ethyl acetate, ethyl lactate, diethyl succinate and the polyols glycerol, and 2,3-butanoloid (levo and meso forms) was performed by a calibration table built with the standard solutions from Thermo Fisher Scientific; Waltham, MA, USA, Merck; Darmstadt, Germany, and Sigma-Aldrich; St. Louis, MO, USA, containing a known concentration of the compounds and subjected to the same treatment as the samples.
## 3 Preliminaries
### Non-Dominated Sorting Genetic (NSGA-II) Algorithm  
Genetic algorithms (GAs) are widely recognized methods used to solve optimization problems and have found successful applications across various real-world scenarios, including reliability optimization. Due to their population-based nature, GAs can yield multiple Pareto-optimal solutions, making them powerful tools for addressing multi-objective optimization problems. Among GAs, the fast non-dominated sorting genetic (NSGA-II) algorithm stands out as the most well-known one for multi-objective optimization [34]. NSGA-II is characterized by its speed and efficiency, incorporating features such as an elitist archive and an adaptation assignment rule. Extensive research confirms the effectiveness of NSGA-II, particularly in addressing reliability problems [5].
## 3 Results and Discussion
### Prediction Model Validation  
The reliability of the prediction model is verified through experimentation. Four sets of concrete blocks with different SP, PFA, and SF contents were created to measure the uniaxial compressive strength and were then compared with the prediction model. The content of SP, PFA, and SF used for the model validation experiments is shown in Table 8. The block-making process and compressive strength testing of the manufactured sand concrete blocks for the model validation experiments were the same as the methods previously mentioned in Section 2.2. The compressive strength experiments, predicted values, and relative errors are shown in Figure 5.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Source** & **Squares** & **df** & **Square** & **Value** & **Prob \textgreater{} F** \\ \hline Model & 138.43 & 9 & 15.38 & 4.59 & 0.0285 & significant \\ X1-SP & 6.12 & 1 & 6.12 & 1.83 & 0.2184 & \\ X2-PFA & 3.12 & 1 & 3.12 & 0.93 & 0.3663 & \\ X3-SF & 0.50 & 1 & 0.50 & 0.15 & 0.7107 & \\ X1 X2 & 4.00 & 1 & 4.00 & 1.19 & 0.3107 & \\ X1 X3 & 2.25 & 1 & 2.25 & 0.67 & 0.4395 & \\ X2 X3 & 2.25 & 1 & 2.25 & 0.67 & 0.4395 & \\ X1\({}^{2}\) & 0.85 & 1 & 0.85 & 0.25 & 0.6294 & \\ X2\({}^{2}\) & 53.06 & 1 & 53.06 & 15.84 & 0.0053 & \\ X3\({}^{2}\) & 60.80 & 1 & 60.80 & 18.15 & 0.0037 & \\ Residual & 23.45 & 7 & 3.35 & & & \\ Lack of Fit & 18.25 & 3 & 6.08 & 4.68 & 0.0851 & not significant \\ Pure Error & 5.20 & 4 & 1.30 & & & \\ Cor Total & 161.88 & 16 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Variance analysis of response surface experiments results.  
As can be seen from Figure 5, the relative error between the experimental and predicted values is less than 10%; therefore, the predictive model for the compressive strength of the manufactured sand concrete developed in this investigation can be considered to be credible.
# Notes on Bus User Assignment Problem Using Section Network Representation Method
## 4 Transit Network Representation  
### The CLP  
The bus station modeling problem, which involves calculating the anticipated users' times at the stations and their distribution between the attractive bus lines, is sometimes referred to as the CLP. Consider the fundamental transit network with a single O/D pair of stations connected by \(n\) lines. Now, a user at the station may select from many lines with varying \(IT\) \"in-vehicle time\". Intuitively, a traveler would select the line with the lowest \(IT\). However, the issue arises whether (s)he would alter her/his mind if the first car to arrive was from a longer \(IT\) line. Typically, it is believed that each passenger determines a group of appealing lines based on which (s)he would board the first approaching bus of this group.  
In addition to users' real-time information and choice model, we must make assumptions on users' arrival rates, lines' \(IT\) probability distributions, and lines' frequencies in order to estimate this set. The progress of the various lines is assumed to be an independent stochastic variable with an exponential distribution for the sake of the reliability of formulating the mathematical model. The users come at random according to the Poisson distribution; however, the \(IT\) timings are predictable. The answer to the following hyperbolic formulation will specify the collection of appealing lines (\(\overline{L}_{s,r}\subseteq L_{s,r}\)):  
\[argmin_{x_{l}}PC=\frac{\pi}{\sum_{l=1}^{n}\varphi_{l}x_{l}}\quad\frac{\sum_{l =1}^{n}IT_{l}\ \varphi_{l}\ x_{l}}{\sum_{l=1}^{n}\varphi_{l}\ x_{l}} \tag{8}\]  
\[s.t. \tag{9}\]  
where \(PC\) is the passenger's average cost for the group of lines with \(x_{l}=1\), which is updated to the set \(\overline{L}_{s,r}\). \(\pi\) is a factor that reflects the variation of both the bus and passenger arrival processes (e.g., \(\pi=1\) in our case) [65]. Intriguingly, Equation (8) might be solved using reliable heuristics by which the lines are arranged ascendingly relating to their \(IT\), and then each line is consecutively examined to be added to \(\overline{L}_{s,r}\) if they would not contribute to a decrease/increase in the \(PC\) value. The passengers are spread throughout the lines based on the frequency of each line:  
\[f_{l}=d_{w}\frac{\varphi_{l}}{\sum_{l\in\overline{L}_{s,r}}\varphi_{l}}\lor l \in\overline{L}_{s,r} \tag{10}\]  
Note that Equations (8)-(10) become more complicated when \(LC\) is regarded. Even if lines in \(\overline{L}_{s,r}\) are the sole desirable option, some users may be unable to board the first vehicle owing to the limited capacity. They would logically change their boarding strategies when they may find it more lucrative to wait until a bus of the set (\(\overline{L}_{s,r}\)) is empty before boarding. Consequently, lines may surpass their stated capacity (\(lc_{m}\)). This assumption is regarded as a moderate capacity limitation, and the selected congestion is to be addressed in the wider context of the assignment models.
## 3 Results  
### Characterisation of Titanium Dioxide Surfaces  
The nanotubular structures are presented in panel form in Figure 3. The different synthetic methods resulted in the complete formation of nanotubes rather than just nanopores. The nanotube arrays synthesised in ethylene glycol had the more ordered and regularly shaped tubes (Figure 3A,B). The TiO\({}_{2}\)NT-S nanostructure is the most irregular, with tubes having vastly different diameters and with tubes growing to the side rather than vertically. The TiO\({}_{2}\)NT-P array on the other hand is more regular than that of TiO\({}_{2}\)NT-S. The materials produced in the organic electrolyte, whilst having a well-defined nanotubular structure, does not have prominent voids between the tubes. The separate tubes in Figure 3C can be attributed to the over etching of the more reactive titanium grain boundaries. Similar features can be seen in Figure 3D. The TiO\({}_{2}\)NT-Ag morphology differs from that of TiO\({}_{2}\)NT in that there are spherical silver nanoparticles on the surface. This indicates the successful deposition of silver nanoparticles resulting from the simple dipping process.  
The morphological parameters of the surfaces produced are presented in Table 1. The most marked difference in morphology is in the thickness of the different nanotube arrays. The arrays produced in ethylene glycol are significantly thicker than those formed in the aqueous solutions. The difference in thickness between TiO\({}_{2}\)NT-S and TiO\({}_{2}\)NT-P indicates the effect of the longer process duration of the former. The nanotubes synthesised in the organic medium are also wider, whilst there is no appreciable difference in wall thickness.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Tube Length (\(\mu\)m)** & **Tube Diameter (nm)** & **Wall Thickness (nm)** \\ \hline TiO\({}_{2}\)NT-P & 0.65 \(\pm\) 0.11 & 70–100 & 12.00 \(\pm\) 1.54 \\ TiO\({}_{2}\)NT-S & 1.51 \(\pm\) 0.09 & 70–100 & 13.00 \(\pm\) 1.17 \\ TiO\({}_{2}\)NT & 9.76 \(\pm\) 0.18 & 90–122 & 10.15 \(\pm\) 1.41 \\ TiO\({}_{2}\)NT-Ag & 9.65 \(\pm\) 0.27 & 90–125 & 10.21 \(\pm\) 1.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphological parameters of the different materials.  
Figure 2: Diagrammatic representation of the experimental set up used for the determination of antimicrobial activity of the TiO\({}_{2}\)-NT surfaces.
# Oral Appliances for Severe Positional Obstructive Sleep Apnea Syndrome: A Case Report
## 2 Case Report  
A 63-year-old Caucasian male patient (body mass index: 26.1 kg/m\({}^{2}\); professional title: neurologist) reported repeated episodes of daytime sleepiness, heavy snoring, and suspicious sleep apnea episodes. After a drug-induced sedation endoscopy (DISE), the results showed predominantly retrolingual upper airway obstruction, raising suspicion of a severe form of OSAS.  
On polysomnographic (PSG) examination, the patient showed a higher AHI in the supine position (87.3/h) than in the lateral position (35.8/h), an oxygen desaturation index (ODI) of 20.4/h, and a snoring duration of 26.6% during the night. Evaluation of sleep positions revealed the patient spent most of the observed period (69.3%) in the non-supine position, whereas he was supine only 30% of the time. During the rest of the time (less than 1%), he constantly changed his position. Based on these observations, the diagnosis of severe OSAS was confirmed, and given the positional dependence, CPAP therapy was considered as the initial treatment, but the patient immediately refused it. Therefore, a MAD was inserted with a protruding mandibular displacement of 4.7 mm (Figure 1), and the anterior articulation system allowed the patient to open and move the teeth laterally to reduce potential discomfort.  
No short- or long-term side effects were noted at follow-up, and compliance remained high. After two years, the PSG showed a significant decrease in all measurements. Thus, AHI reached a value of 14.7/h in the lateral position and of 21.4/h in the supine position, indicating a decrease of 58.9% in the lateral position and of 75.5% in the supine position. ODI decreased from 20.4/h before treatment to 12.1/h post-treatment. The patient also changed his usual sleeping positions: time in the supine position increased to 59.6%, while time in the lateral position decreased to 40.4%. The initial symptom of discomfort disappeared completely (Figure 2), and the reevaluation of OSAS indicated a decrease in severity from severe to mild. Overall, the patient reported a significant improvement in his quality of life.  
Figure 1: The mandibular advancement device (MAD) was worn by the patient. The appliance was realized with an anterior displacement of 4.7 mm. The anterior articulation system allowed the patient to open and move the teeth laterally, reducing discomfort.  
Figure 2: Apnea/hypopnea index (AHI) before (PI) and after (PF) treatment with the mandibular advancement device (MAD). There was a significant decrease in the evaluated values (supine position and lateral position AHI). The supine position AHI was 87.3/h before treatment (PI) and 21.4/h after treatment (PF). In the lateral position AHI, a value of 35.8/h was measured in PI, which decreased to 14.7/h in PF.
## 4 Results and Discussion
### Evaluation of SPPs
#### 4.1.1 General Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Metrics**} & \multirow{3}{*}{
\begin{tabular}{c} **Time** \\ **Scale** \\ \end{tabular} } & \multirow{3}{*}{**Original**} & \multicolumn{3}{c}{**GSMaP-NRT**} & \multicolumn{3}{c}{**IMERG Early**} \\ \cline{3-13}  & & & \multicolumn{3}{c}{**SMBC**} & \multicolumn{3}{c}{**RBC**} & \multicolumn{3}{c}{**SMBC**} & \multicolumn{3}{c}{**RBC**} \\ \cline{3-13}  & & & **Month** & **10-Day** & **Month** & **10-Day** & **Criginal** & **Month** & **10-Day** & **Month** & **10-Day** & **Month** & **10-Day** \\  & & & **Factor** & **Factor** & **Factor** & **Factor** & **Factor** & **Factor** & **Factor** & **Factor** & **Factor** \\ \hline \multirow{3}{*}{CC} & Hourly & 0.76 & 0.75 & 0.77 & 0.75 & **0.77** & **0.80** & 0.80 & 0.70 & 0.77 & **0.80** \\  & Daily & 0.79 & 0.78 & 0.90 & 0.78 & **0.90** & 0.79 & 0.76 & 0.90 & 0.76 & **0.90** \\  & 10 days & 0.79 & 0.77 & 1.00 & 0.77 & **1.00** & 0.77 & 0.71 & 1.00 & 0.72 & **1.00** \\  & Monthly & 0.84 & 0.82 & 0.99 & 0.82 & **0.99** & 0.78 & 0.73 & 0.99 & 0.73 & **0.99** \\ \hline \multirow{3}{*}{BIAS} & Hourly & \(-0.03\) & \(-0.08\) & 0.03 & \(-0.08\) & **0.03** & \(-0.02\) & 0.17 & 0.10 & \(-0.11\) & **0.04** \\  & Daily & 0.04 & \(-0.05\) & \(-0.01\) & \(-0.04\) & **0.00** & 0.46 & 0.04 & 0.00 & 0.04 & **0.00** \\  & 10 days & 0.45 & \(-0.49\) & \(-0.08\) & \(-0.42\) & **0.00** & 4.58 & 0.35 & 0.00 & 0.42 & **0.00** \\  & Monthly & 1.37 & \(-1.47\) & \(-0.25\) & \(-1.28\) & **0.00** & 13.94 & 1.07 & \(-0.01\) & 1.29 & **0.00** \\ \hline \multirow{3}{*}{MAE} & Hourly & 0.39 & **0.38** & 0.42 & **0.38** & 0.42 & 0.37 & 0.46 & 0.46 & **0.37** & 0.40 \\  & Daily & 2.87 & 2.92 & **2.13** & 2.93 & 2.15 & 3.05 & 3.00 & **2.19** & 2.98 & 2.22 \\  & 10 days & 17.51 & 18.01 & 0.08 & 18.08 & **0.00** & 19.48 & 19.06 & 0.00 & 19.00 & **0.00** \\  & Monthly & 36.35 & 34.34 & 6.28 & 34.36 & **6.02** & 44.19 & 42.41 & 5.74 & 42.00 & 5.97 \\ \hline \multirow{3}{*}{RMSE} & Hourly & 1.23 & **1.21** & 1.23 & 1.22 & 1.23 & **1.08** & 1.31 & 1.51 & 1.16 & 1.14 \\  & Daily & 7.51 & 7.65 & **5.29** & 7.67 & 5.33 & 7.44 & 7.78 & **5.19** & 7.76 & 5.23 \\ \cline{1-1}  & 10 days & 27.83 & 28.76 & 0.40 & 28.87 & **0.00** & 30.22 & 32.35 & 0.04 & 32.19 & **0.00** \\ \cline{1-1}  & Monthly & 52.64 & 55.16 & 11.75 & 55.34 & **11.53** & 64.90 & 68.99 & **10.05** & 68.47 & 10.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation criteria for the NRT-SPPs over the Fuji River basin of various temporal scales. The bold values represent the highest recorded performance for each SPPs at each temporal scale. Figure 4: Inter-comparison of cumulative rainfall of different precipitation datasets in five selected months during 2002-2011.