esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology (Research Area and Data Collection)
### Research Sample  
Students were members of faculties that are located in the city center or nearby. The sampling (we used a non-probability sampling) was linked to the information about the number of enrolled students that we had received from the Department of Education and Students' Affairs at the University of Maribor. We conducted a random sample of courses at both levels, undergraduate and graduate. Course tutors and vice deans for student affairs provided information about the attendance of selected courses. We asked for a confirmation letter via email to conduct surveys among students. The data were collected with self-administered questionnaires in the lecture halls (the questionnaire used is presented in Supplementary materials Table S1). The sample consisted of 382 respondents from 11 faculties located in the city center or nearby (67.2% women and 32.8% men, M\({}_{\text{age}}\) = 22); however, the sample is not representative. In Table 1 some demographic characteristics of our sample are presented.  
\begin{table}
\begin{tabular}{c c c} \hline
**Demographic Characteristics** & \multicolumn{2}{c}{**\% (\(n\))**} \\ \hline \multirow{2}{*}{Gender} & Male & 32.8\% (124) \\  & Female & 67.2\% (254) \\ \hline \multirow{4}{*}{Age} & 19–21 & 48.4\% (181) \\  & 22–25 & 48.4\% (181) \\  & 26–43 & 3.2\% (12) \\ \hline \multirow{4}{*}{Faculty} & FF & 16\% (61) \\  & EPF & 6.8\% (26) \\  & FERI & 12.6\% (48) \\  & PeF & 8.9\% (34) \\  & FGPA & 7.9\% (30) \\  & FKKT & 3.1\% (12) \\  & FZV & 10.8\% (41) \\  & MF & 9.7\% (37) \\  & FNM & 5\% (19) \\  & FS & 9.7\% (37) \\  & PF & 9.4\% (36) \\ \hline \multirow{4}{*}{Distance of living} & Less than 3 km & 52.9\% (202) \\  & 3–5 km & 11\% (42) \\ \cline{1-1}  & 5–10 km & 7.6\% (29) \\ \cline{1-1}  & More than 5 km & 28.5\% (109) \\ \hline \end{tabular} Legend: Faculty of Medicine (MF), Faculty of Natural Sciences and Mathematics (FNM), Faculty of Education (PeF), Faculty of Arts (FF), Faculty of Law (PF), Faculty of Civil Engineering, Transportation Engineering and Architecture (FGPA), Faculty of Chemistry and Chemical Engineering (FKKT), Faculty of Mechanical Engineering (FS), Faculty of Electrical Engineering and Computer Science (FERI), Faculty of Economics and Business (EFF), Faculty of Health Sciences (FZV).  
\end{table}
Table 1: Demographic characteristics of the sample.
# A Computer-Vision Based Application for Student Behavior Monitoring in Classroom
## 2 Proposed System
### Summarize Analyzed Data
#### 2.6.1. Uniting the Appearances into a Sequence  
The method to unite appearances was based on the kernel method of object tracking [46; 47]. Consider that frame \(t_{i}\) is the image frame at time \(t_{i}\). It contains \(N_{i}\) appearances. Frame \(t_{i}\) at time \(t_{j}\) contains \(N_{i}\) entities and \(t_{i}\) is close to \(t_{j}\). Consider an appearance \(\ell_{t_{i}}^{k_{0}}\) from frame \(t_{i}\) (\(0\ <=\ k_{1}\ <\ N_{j}\)), and \(\ell_{t_{j}}^{k_{1}}\) from frame \(t_{j}\) (\(0\ <=\ k_{1}\ <\ N_{j}\)). \(\ell_{t_{i}}^{k_{0}}\) and \(\ell_{t_{j}}^{k_{1}}\) are united if:  
* \(0\ <\ t_{i}\ -\ t_{j}\ <\ t_{interval}\), where \(t_{interval}\) is a pre-defined hyper-parameter which defines the time (in milliseconds) to search the suitable appearance to unite with.
* \(k_{0}=argmax\Big{(}\text{IoU}\Big{(}\ell_{t_{i}}^{k_{0}}\text{-}bounding_{ box},\ell_{t_{j}}^{k_{1}}\text{-}bounding_{box}\Big{)}\Big{)}\) with \(\text{IoU}(box_{1},box_{2})\ =\ \frac{\text{intersection},\text{area}(box_{1},box_{2})}{ \text{union}_{\text{area}}(box_{1},box_{2})}\).  
All appearances will be united and now they become \(N\) separated sequences of entities. Figure 4 shows an example of a sequence of appearances of three consecutive image frames. These appearances are united by the algorithm that determines them as the same person.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 3 Results and Discussion
### Kinetic
#### 3.5.2 PFO and PSO  
The PFO and PSO models were used to calculate the variables. Equations (8) and (9) were used for the modeling of the nonlinear PFO and PSO shown in Figures 7 and 8, and the calculated variables are listed in Table 4.  
\[q_{t}=q_{e}\left(1-e^{-K_{1}t}\right) \tag{8}\]  
\[q_{t}=\frac{K_{2}q_{e}^{2}t}{1+K_{2}q_{e}t}. \tag{9}\]  
Based on Table 4 and Figures 7 and 8, the mean of R\({}^{2}\) obtains 0.941, ranging from 0.863-0.995 for the GAC and 0.980, ranging from 0.940-0.996 for the GACA. This indicates that the PSO accomplishes a higher R\({}^{2}\) than the PFO in all the experimental conditions except for the GACA-w at pH 9. It should be considered that the PFO does not express the time of 3-9 h in Figure 7, whereas the mild curve line is included at the PSO. In general, the PFO and PSO were related to physisorption and chemisorption, respectively [16; 36].  
The rate constants, \(K_{1}\) and \(K_{2}\), can be compared to the reaction rate. Due to the following observation that a higher K was associated with a higher \(q_{e}\) at pH 7 and 9, the relationship was questionable at pH 4. To determine the value of K clearly, an attempt was made to linearize Equations (8) and (9) to the following Equations (10) and (11), respectively:  
\[\ln(q_{e}-q_{t})=\ln q_{e}-\ K_{1}t \tag{10}\]  
\[\frac{t}{q_{t}}=\ \frac{1}{K_{2}q_{e}^{2}}+\ \frac{1}{q_{e}}t. \tag{11}\]  
Figure 7: Nonlinear pseudo-first-order (PFO) kinetic for GAC and GACA at initial pH of 4 (a), 7 (b), and 9 (c) (red lines indicate the phosphate desorption experiment).  
The linear PFO and PSO are drawn in Figures S1 and S2. As shown in Figure S1, it would be difficult to prepare one linear line. Therefore, the linear regression was separated into two parts [16]. Table S2 lists the parameters calculated from the linear PFO at 0-96 h and 0-6 h, in addition to those from the linear PSO. Although the separation of the PFO improves the R\({}^{2}\), the values of \(q_{e}\) appear to be inexplicable, and R\({}^{2}\) is still poor. By contrast, the linearization of the PSO accomplishes at least 0.998 up to 1.00 (0.99996) of R\({}^{2}\), which is higher than that of the nonlinear PSO. Furthermore, the desorption process was obviously distinguished with the negative \(K_{2}\) for the GAC-w at pH 4 and the GACA-w at pH 9. Additionally, the order of \(K_{2}\) is very similar to the 0.9 ratios of \(R_{t}\)/\(R_{e}\) in Figure 6. Based on \(K_{2}\), it could be concluded that the acid condition (pH 4) achieves an increased adsorption rate, and the pH adjustment (decreasing solution pH) assists in accelerating the adsorption reaction.  
\begin{table}
\begin{tabular}{c} **GACA-** \\ **wo** \\
## 3 Hybrid Model Based on CAE and CNN
### Architecture of the Designed Hybrid Model  
The proposed hybrid deep neural network model based on CAE and CNNs in object-oriented remote sensing classification (referred to as the CAE_CNN model) includes two steps. The first step requires training of the object-oriented remote sensing dataset without class information supervision based on the designed CAE model to extract feature maps and reduce the dimensionality of the data. The second step involves using the extracted feature maps as the inputs for the designed CNN model, which can then be classified.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Object** & **Point 1** & **Point 2** & **Point 3** & **Point 4** & **Point 5** & **Point 6** \\ \hline Band1 & 2908 & 4022 & 3080 & 3957 & 4330 & 3112 & 3665 \\ Band4 & 2560 & 3075 & 2176 & 2999 & 3535 & 2342 & 2606 \\ Band8 & 3629 & 2695 & 2053 & 2767 & 4405 & 3026 & 3286 \\ \hline \end{tabular}
\end{table}
Table 1: Spectral values of object and pixels (three bands presented for example). The second column represents a single statistical value based on the multiple distinct spectral values of an object. The third to eighth columns represent spectral values of point1 to point6, respectively. The object and points refer to Figure 1.  
Figure 1: An example of an image object: (**a**) the original image object; (**b**) the post-processing image object.  
The designed CAE model includes an encoder part and a decoder part. The encoder part includes a convolutional layer and a max pooling layer. The decoder part contains a deconvolution layer and an up-max pooling layer (layers are ascertained after repeated experiments).  
The designed CNN model contains four layers with weights, including three convolutional layers and a fully-connected layer. The max pooling layer follows each convolutional layer.  
The architecture of the CAE_CNN model can be seen in Figure 2 below.
## 3 Results and Discussion
### Evaluation of Different Extraction Techniques on the Yield and Purity of Obtained Extracts
#### 3.1.3 Phycobiliproteins Extraction by Freeze-Thaw Cycles  
Freeze-thaw extraction is commonly used to disrupt cyanobacteria cells. The freezing of the sample and formation of the ice crystals helps to increase the volume of the cell, and once it is thawed, the cell is disrupted. The influence of the number of freeze-thaw cycles on extraction yield and purity was investigated. It can be seen from the results in Figure 4 that the extraction yield gradually increased with the increasing number of cycles. After two cycles, the yield was 29.2 \(\pm\) 0.15 mg/g of biomass and 9.9 \(\pm\) 0.5 mg/g of biomass of PC and APC, respectively. In comparison, three cycles of freeze-thaw obtained the highest extraction yield with 31.3 \(\pm\) 0.7 mg/g of biomass and 11.2 \(\pm\) 0.6 mg/g of biomass of PC and APC, which corresponds to a 7% and 10.6% increase in PC and ACP between the two and three cycle extraction yields, respectively. All extracts obtained had food grade purity ranging from 1.09 \(\pm\) 0.02 to 1.10 \(\pm\) 0.01 with no significant difference (\(p\) > 0.05). The collected data are in close agreement with those of [PERSON] et al., who reported that four cycles are the optimal conditions to achieve the highest yield, but a higher number of cycles can also lead to decreased purity due to the release of contaminant proteins [39].  
Figure 3: Homogenisation extraction yield of phycocyanin and allophyocyanin from _A. platensis_ using different extraction times (**A**). Yield values are expressed as mg/g of _A. platensis_ biomass. Purity of the crude extracts obtained from different extraction times (**B**). Green-coloured extract pictured after 30 min of homogenisation (**C**). Values are expressed as mean \(\pm\) standard deviation, n = 3. Different lowercase letters indicate significant differences (\(p\) < 0.05).
# Optimal Arrangement Strategy of IoT Sensors in Urban Drainage Networks: A Review
## 3 Arrangement of IoT Sensors in the Drainage Network
### Classification of Monitoring Objectives
#### 3.1.2 Identification of Abnormal States in Pipeline Networks  
(1) Tracking and Source Tracing of Target Pollutants  
By monitoring the composition of sewage such as pH, conductivity [2; 4], and other water quality parameters in the drainage network, the tracking and source tracing of illegal discharge in the network can be realized. Based on reverse modeling [45] and the inversion [46] of monitoring data on the transfer and diffusion process of target pollutants, or the establishment of transfer and transport models for these pollutants [20], tracking and source tracing can be achieved. Wastewater treatment plants can be instructed to implement emergency measures in advance to minimize the impact on the treatment process owing to fluctuations in sewage quality in the network.  
The drainage network is an important type of infrastructure in cities. Downstream inspection wells collect and reflect the upstream sewage composition information. By monitoring the specific chemical components of wastewater, it is possible to locate and trace target pollutants. In recent years, wastewater-based epidemiology (WBE) has facilitated the tracking and localization of mass epidemic outbreak hotspots [47; 48] and illicit drug abuse [49; 50]. It has achieved reliable monitoring results in populated areas such as communities [51] and universities [52]. In particular, this method is low cost and allows for wide coverage during the initial screening of large populations compared to the sampling and testing of individuals. An example of this is during the large-scale COVID-19 outbreak [53]. When information about the target is detected in the drainage network, progressive screening of the upstream area can facilitate the localization of infected individuals. Appropriate isolation measures can then be implemented.  
(2) Hotspot Monitoring of Floods and Overflows  
The monitoring of water levels in municipal drainage networks via the placement of IoT sensors in flood-prone areas can guide flood emergency response and management, such as in unattended overflow sections of the pipeline network [45], flooding hotspots in the combined drainage network [22], and areas where backflow occurs in rainwater networks during high tides [54]. Through the appropriate positioning of sensors, the scope of investigation can be narrowed for the required measurement. The development of AI technology enables the efficient application of the massive amount of data generation from IoT sensors to flood modeling and prediction. [PERSON] et al. [55] constructed different neural network models to simulate and predict CSO water levels based on IoT-monitored CSO water-level datasets and rainfall intensity data. [PERSON] et al. [9] considered the three factors of CSO frequency, volume of water, and the amount of pollution, to monitor CSO events in a combined drainage network to minimize their impacts.
# Priority-Based Content Delivery in the Internet of Vehicles through Named Data Networking +
## 2 Traffic Prioritization in IoV: What and Why?
### Heterogeneous Networking for IoV  
The growing complexity and pervasiveness of envisioned IoV applications seriously challenge the capacity of 802.11 and pave the way for resorting to the usage of different wireless communication technologies. Indeed, recently manufactured vehicles can be provided with multihomed communication devices that enable different technologies to play alternative roles or be combined to boost vehicular connectivity depending on application requirements, content availability and network performance [20].  
Basically, three different types of connectivity can be established by vehicles: (i) Vehicle-to-Infrastructure (V2I), i.e., communications over 3G/LTE to reach remote nodes; (ii) Vehicle-to-Roadside (V2R), i.e., interactions with the local roadside infrastructure (Road-Side Units, RSUs) set-up over 802.11 OCB links; (iii) Vehicle-to-Vehicle (V2V), e.g., interactions with other vehicles. V2V communications can be established both over 802.11 OCB links and, as more recently debated in the research community, through LTE Device-to-Device (D2D) communications, also known as Proximity Services (ProSe) in Third Generation Partnership Project (3 GPP) Release 12 [21].  
The aforementioned kinds of communications typically exhibit different performance features. For instance, V2I communications over LTE may have poor quality under high speeds, but are able to provide ubiquitous coverage. 802.11 OCB, instead, provides high-throughput for localized V2V and V2R interactions, with performance getting worsened in the case of multi-hop communications. However, it may suffer from intermittent connectivity, either due to sparsely-deployed RSUs in the case of V2R communications, or fragmented topologies, in the case of V2V interactions. Unlike 802.11 OCB, for which standardization, testing and experimentation are at a mature stage worldwide [20], with the consensus of many stakeholders, the design of the D2D technology for V2V interactions is still at its infancy and specifically under study in Release 14 [22].  
Heterogeneous networking can be particularly helpful to accommodate the different delivery requirements of IoV traffic. In particular, prioritization of traffic can highly benefit from the availability of multiple interfaces. Low-priority traffic tolerating delays can be delivered over V2V/V2R links when available, by offloading the cellular interface, strained by the explosive growth of mobile data traffic [23]. On the other hand, bulky high-priority data transfers, especially if they have a tight deadline, may benefit from the usage of multiple interfaces in parallel.
## 4 Simulation Results
### Comparisons between Traditional Method and Proposed Method  
The traditional GA (TGA) incorporating with the interior point method and the point-estimation method was used to study the problem for comparisons.  
The case studied in Section 4.1 serves as an example here. Table 10 shows the performances of TGA considering different population sizes (50, 75 and 100). The crossover and mutation rates of TGA are 0.8 and 0.02, respectively. The simulation results imply that the TGA yields a feasible solution only because a long binary bit string (24 \(\times\) 9 = 216 bits) is needed for encoding \(u_{i}(t)\) and \(v_{\ell}(t)\) and the number of real variables (encoded by 144 \(\times\) 32 bits) is also large. The TGA requires much shorter CPU times and less number of iterations than those needed by the proposed method. Obviously, the TGA  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**t** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8–16** & **17** & **18** & **19** & **20** & **21** & **22-24** \\ \hline \(P_{el}^{6}(t)\) & 7.5 & 7.5 & 7.5 & 7.5 & 0.0 & 0.0 & 0.0 & 7.5 & 7.5 & 7.5 & 7.5 & 7.5 & 0.0 \\ \(P_{el}^{16}(t)\) & 2.0 & 2.0 & 2.0 & 2.0 & 2.0 & 2.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \(P_{el}^{18}(t)\) & 2.5 & 2.5 & 2.5 & 2.5 & 2.5 & 2.5 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \(P_{el}^{20}(t)\) & 2.5 & 2.5 & 2.5 & 2.5 & 2.5 & 2.5 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \(P_{el}^{26}(t)\) & 2.5 & 2.5 & 2.5 & 2.5 & 2.5 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal demand response (MW) in a day.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Impact Factors** & **Descriptions** & **Expected Values of Total Cost (S)** \\ \hline \multirow{3}{*}{Electricity Tariffs} & Peak hour: 1315/MWh; off-peak hour: 305/MWh & 19,028.80 \\  & As shown in Table 5 & 23,737.68 \\  & Peak hour: 1315/MWh; off-peak hour: 1005/MWh & 25,637.07 \\ \hline Minimum Up/Minimum & All are 1 h at all buses & 21,879.39 \\ Down Times & As shown in Table 2 & 23,737.68 \\ \((M_{i}^{en},\ M_{i}^{off})\) & (8,4), (8,3), (7,3), (7,4) hours at buses 3, 4, 5, 6. & 25,845.03 \\ \hline Ramp Rates & 0.5, 0.6, 0.7, 0.8 MW/h at buses 3, 4, 5, 6. & 23,781.71 \\ \((R_{i}^{up}=R_{i}^{down},\text{MW/h})\) & As shown in Table 2 & 23,737.68 \\  & 4, 3.5, 4.5, 3 MW/h at buses 3, 4, 5, 6. & 23,346.11 \\ \hline \multirow{3}{*}{Must-run Hours in Production Lines} & 7, 3, 4, 3, 2 h at buses 6, 16, 18, 20, 26 & 18,254.53 \\  & As shown in Table 6 & 23,737.68 \\ 15, 11, 12, 11, 10 h at buses 6, 16, 18, 20, 26 & 33,448.68 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Impacts of different factors on results.  
converges prematurely. The expected values of final cost obtained by TGA are $31,300.69-31,345.96, which are much greater than $23,737.68 obtained by the proposed method.
## 5 Simulation Results and Discussion
### Effect of Time Delay without Minimization Methods  
The modeling tests showed how much communication latency was accepted by the system in order to maintain the system stability. The impact of time delay was assessed from the following perspectives: without communication delay, as well as with the lowest and maximum communication delays that the system could tolerate in order to maintain acceptable performance. When there was a delay of 900 ms in the generator, the system responded to the failure scenario (G1-G6). As seen in the graph, due to the delay, it took the controller longer to detect the defect and respond to it. The controller could still provide a strong damping performance and efficiently compensate for the delay.  
When there was a delay of 900 ms in the generator (G1-G6), it took longer for the controller to respond to the issue. The controller still provided sufficient damping performance. The entire Kigali National Grid (KNG) system, however, grew unstable. The simulation findings from the study implied that low latency communication was required to preserve the system's stability. The longer the communication latency, the slower the control operations, which could lead to power system instability and oscillation. The control system experienced increased system overshoots and longer settling times when network delays grew from 100 ms to 900 ms.  
Figure 13 of the active power and reactive power shows how different changes occurred on the grid when the shunt reactor and the two capacitance banks started to work from 10 s when there was a 7 s inundation of the PV system. At 18 s, there was an impact when the Jali PV plant was disconnected.
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 4 Experimental Procedure
### Results and Discussion
Regarding the classifiers, it is observed that SVM performs slightly better than kNN. This result is because the SVM approach is able to separate the classes more efficiently using the Gaussian kernel. However, as a consequence of the small corpus, the difference is not significant. In fact, for MFCC features, kNN outperforms SVM by 0.12%. However, it is expected that the difference will increase when new species are added to the corpus. The feature fusion technique (MFCC/LFCC) exhibits high classification results in both algorithms, outperforming each of the individual features. In most cases, the resultant accuracy per class is equal to or greater than those achieved by single features. This confirms that this method provides a further characterization of the reptile calls by appending information of high and low frequency regions, which leads to a higher accuracy in classification. Furthermore, 13 classes were identified with a success rate of 100% when this approach was applied. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multicolumn{3}{c}{**kNN**} & \multicolumn{3}{c}{**SVM**} \\ \cline{2-9}  & **MFCC** & **LFCC** & **MFCC/LFCC** & **MFCC** & **LFCC** & **MFCC/LFCC** \\ \hline
1 & 98.67\% \(\pm\) 0.11 & 97.00\% \(\pm\) 0.17 & 98.67\% \(\pm\) 0.11 & 99.67\% \(\pm\) 0.06 & 96.00\% \(\pm\) 0.20 & 98.33\% \(\pm\) 0.13 \\
2 & 100.00\% \(\pm\) 0.00 & 100.00\% \(\pm\) 0.00 & 100.00\% \(\pm\) 0.00 & 100.00\% \(\pm\) 0.00 & 100.00\% \(\pm\) 0.00 & 100.00\% \(\pm\) 0.00 \\
3 & 99.53\% \(\pm\) 0.07 & 96.82\% \(\pm\) 0.18 & 97.65\% \(\pm\) 0.15 & 99.41\% \(\pm\) 0.08 & 99.06\% \(\pm\) 0.10 & 99.65\% \(\pm\) 0.06 \\
4 & 98.00\% \(\pm\) 0.14 & 98.55\% \(\pm\) 0.12 & 96.00\% \(\pm\) 0.20 & 100.00\% \(\pm\) 0.00 & 97.09\% \(\pm\) 0.17 & 99.45\% \(\pm\)  
In the second experiment, the training samples were reduced from 50% to 5% to validate the robustness of the method using the best model found in the first experiment (MFCC/LFCC fusion + SVM). Table 3 shows that the fusion approach is able to deal with the low number of training samples, keeping accuracy and F-Measure values above 90% in almost all cases. This proves that the fusion of both cepstral coefficients is effective for modeling the discriminant information in the reptile calls. Moreover, the reduction in training set size offers savings in time needed to calculate the support vectors. However, when 5% of training is reached, the system clearly declines in effectiveness because most of the reptile species are only characterized by one syllable; thus, SVM has serious difficulties finding discriminant information to identify the classes. Nevertheless, this approach is able to maintain the classification results above 85%, confirming the robustness of the feature fusion method.