esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Experimental Setup and Data Collection
This variation can be accounted for by using data pre-processing techniques such as baseline manipulation. Baseline manipulation is the solution to the problem and the correct way of representing the signal when the analysis deals with sensor values from different conversion units. Baseline manipulation helps to pre-process the sensor output to free itself from the drift effect, the intensity dependence and, possibly, from non-linearity [40; 41]. The details about other type of data pre-processing techniques will be described in the next section. The process of data collection began with the first condition, which was the ambient air environment. The purpose of this experiment was to collect the data of clean air for the room with the assumption that the ambient air was not contaminated. For the first environment, the data collection process took about two days. Thus, at the end of day 2, there were 960 samples collected for ambient air. The second environment was the environment with the presence of chemical substance. In this experiment, a cleaning agent was used as a proxy of the chemical substance. About 100 mL of chemical was put in a beaker and placed inside the room--at the centre of the room. The experiment was repeated for two days and 960 samples were collected during that period. For the third environment, an air freshener was used as a proxy for the fragrance. An automatic air freshener which released  
Figure 3: Room setup for the experiment. fragrance every 15 min was placed inside the room. It was hung on the wall adjacent to the wall where the sensing node was placed, about 2 m from the floor and about 2 m from the sensing node. The air flow from the air conditioner would accumulate the fragrance in the room. The data was collected for two days with 960 samples. For the fourth condition of the room environment with combustion activity, a person smoking a cigarette was chosen as the proxy. A person was asked to smoke in the room so that the real data of a person smoking a cigarette in a room was collected. That person smoked one cigarette at the centre of the room. Every cigarette produced data for approximately 30 min. The experiment was repeated four times a day for eight days. The amount of data collected for the environment with combustion activity was 960 samples. Lastly, for the room environment with the presence of food and beverages, rotten cooked fish had been selected to represent this category. A bowl of rotten cooked fish was placed in the middle of the room. The experiment was repeated for two days and 960 samples were collected during that period.
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 3 Proposed Deep Learning-Based MIMO Receiver
### Deep Learning-Based Operation
 \tag{21}\]  
where \(f_{R}\Big{(}\mathbf{p}_{\text{sample}}\Big{)}\) can be obtained by (14). With \(\mathbf{p}_{\text{sample}}\), we have \(\mathbf{q}\Big{(}\mathbf{p}_{\text{sample}}\Big{)}=\mathbf{r}_{L+1}\). A well-trained DNN indicates that the difference between \(\mathbf{r}_{L+1}\) and \(\mathbf{q}_{\text{sample}}\) is sufficiently small. Therefore, we update the weight matrices and bias vectors to decrease the loss function \(\mathcal{L}\). As the loss function \(\mathcal{L}\) for the proposed receiver, we use the cross-entropy between \(\mathbf{q}_{\text{sample}}\) and \(\mathbf{q}\Big{(}\mathbf{p}_{\text{sample}}\Big{)}\) as follows [17]:  
\[\mathcal{L}=-\frac{1}{2}\sum_{i=1}^{2}\Big{\{}\mathbf{q}_{\text{sample}}\Big{\}} _{i}\log_{2}\Big{(}\Big{\{}\mathbf{q}_{\text{sample}}\Big{\}}_{i}\Big{)}. \tag{22}\]  
Since the proposed receiver operates based on the DNN, it is important to sufficiently train the DNN using a large number of labeled data to ensure accurate prediction. Based on  
Figure 1: A DNN architecture for the proposed receiver. the input-output relationship above, as depicted in Figure 2, we proposed that the receiver operates in two phases: a training phase where the DNN is trained, and an inference phase where the receiver type is determined with well-trained DNN. In the training phase, for the given channel \(\mathbf{H}\), \(\mathbf{p}_{\text{sample}}\) is generated at the receiver by (20). Moreover, to obtain \(\mathbf{q}_{\text{sample}}\), the proposed receiver calculates \(P_{S}^{ZF}(\mathbf{H})\) to determine \(f_{R}\) using (14), assuming that the receiver has empirically \(\mathbb{E}_{\mathbf{H}}\big{[}\hat{P}_{S}^{ML}(\mathbf{H})\big{]}\) in advance. The receiver constructs the \(\mathbf{q}_{\text{sample}}\) based on (21) and then updates the DNN parameters. Note that the proposed receiver repeats this operation for numerous channel realizations in the training phase to train the DNN sufficiently. In the inference phase, with well-trained DNN, the proposed receiver generates \(\mathbf{p}_{\text{sample}}\) for the given MIMO channel \(\mathbf{H}\) and achieves \(\mathbf{q}\big{(}\mathbf{p}_{\text{sample}}\big{)}\). Since the \(\mathbf{q}\big{(}\mathbf{p}_{\text{sample}}\big{)}\) is obtained by applying the softmax function and each element of \(\mathbf{q}\big{(}\mathbf{p}_{\text{sample}}\big{)}\) indicates its probability, the proposed receiver determines the receiver type in the inference phase \(\hat{f}_{R}\) as follows:  
\[\hat{f}_{R}= \begin{cases}0;&\quad\Big{\{}\mathbf{q}\big{(}\mathbf{p}_{\text{ sample}}\big{)}\Big{\}}_{1}>\Big{\{}\mathbf{q}\big{(}\mathbf{p}_{\text{sample}} \big{)}\Big{\}}_{0},\\ 1;&\quad\Big{\{}\mathbf{q}\big{(}\mathbf{p}_{\text{sample}}\big{)}\Big{\}}_{1} \leq\Big{\{}\mathbf{q}\big{(}\mathbf{p}_{\text{sample}}\big{)}\Big{\}}_{0}. \end{cases}  \tag{23}\]  
Consequently, the ML receiver is adopted when \(\hat{f}_{R}=0\) and the ZF receiver is used when \(\hat{f}_{R}=1\).
## 3 Results and Discussion
### Analysis of Energy Performance of Heat Pump Systems
#### 3.2.1 Comparison of ENERGY Performance of Heat Pumps  
Table 5 lists the field test energy performance of heat pump systems. The median of \(COP\) in SD-GHPs is 3.59, with a maximum of 5.15 in SG-1 and minimum of 2.32 in SG-5. Besides, the median of \(COP\) in MD-GHPs is 5.20, with a maximum of 7.80 in MG-7 and minimum of 4.15 in MG-3. It can be seen that the \(COP\) in MD-GHPs is generally higher than the \(COP\) in SD-GHPs. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & **Depth** & \begin{tabular}{c} **Inlet Water** \\ **Temperature** \\ **(\(\mathbf{{}^{\circ}C}\))** \\ \end{tabular} & \begin{tabular}{c} **Outlet Water** \\ **Temperature** \\ **(\(\mathbf{{}^{\circ}C}\))** \\ \end{tabular} & \begin{tabular}{c} **Q\({}_{g}\) per DBHE (kW)** \\ **(\(\mathbf{{}^{\circ}C}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Running** \\ **(\(\mathbf{{}^{\circ}B}\))** \\ \end{tabular} &
\begin{tabular}{c} **Q\({}_{g}\) per DBHE (kW)** \\ **(\(\mathbf{{}^{\circ}B}\))** \\ \end{tabular} \\ \hline MG-1 & 2000 & 26.9 & 18.9 & 257.6 & 24 & 22.3 \\ MG-2 & 2000 & 29.8 & 19.3 & 151.9 & 24 & 13.1 \\ MG-3 & 2000 & 20.0 & 9.7 & 294.4 & 24 & 25.4 \\ MG-4 & 2000 & 23.3 & 19.3 & 122.7 & 24 & 10.6 \\ MG-5 & 2500 & 29.1 & 18.4 & 247.2 & 24 & 21.4 \\ MG-6 & 2500 & 28.9 & 20.0 & 195.2 & 24 & 16.9 \\ MG-7 & 2500 & 34.0 & 20.2 & 302.7 & 24 & 26.2 \\ MG-8 & 2800 & 41.0 & 23.0 & 672.0 & 11 & 26.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accumulated heat extraction per DBHE in 24 h (\(Q_{g,a}\)). In order to make clear the difference, the \(T_{c}\) and \(T_{e}\) are also analyzed then the \(COP_{t}\) and \(DCOP\) are calculated. As shown in Figure 8, the median of \(T_{c}\) in SD-GHPs and MD-GHPs are 44.8 \({}^{\circ}\)C and 42.8 \({}^{\circ}\)C separately. While the median of \(T_{e}\) in SD-GHPs and MD-GHPs are 6.2 \({}^{\circ}\)C and 17 \({}^{\circ}\)C separately. It can be seen that, there is nearly no difference in \(T_{c}\) between SD-GHPs and MD-GHPs, since they operate in similar conditions in the user side. However, benefitting from the high-temperature heat source, the \(T_{e}\) of MD-GHPs is nearly 10 \({}^{\circ}\)C higher than \(T_{e}\) of SD-GHPs, which contributes much to the improvement of \(COP_{t}\). Figure 9 then compares the \(COP_{t}\) of SD-GHPs and MD-GHPs. Benefiting from the high \(T_{e}\), the \(COP_{t}\) of MD-GHPs varies from 9.62 to 14.37 with a median of 12.30. While the \(COP_{t}\) of SD-GHPs varies from 7.75 to 10.46 with a median of 8.41, which are generally lower than that of MD-GHPs. However, as shown in Figure 10, there is no obvious difference in \(DCOP\) between SD-GHPs and MD-GHPs. Where the \(DCOP\) of MD-GHPs varies from 0.34 to 0.70 with a median of 0.45 and the \(DCOP\) of SD-GHPs varies from 0.30 to 0.52 with a median of 0.42.
## 3 Results and Discussion
In fact, a distance from the determined points to the reference trajectory (test A) up to 0.221 m was recorded in tests B and E. Therefore, the S7 and S5 receivers can be used in RTK mode on agricultural farms where row crops, e.g., tree and horticultural crops, are cultivated: these receivers allow the spatially variable rate application of crop inputs (e.g., fertilisers, herbicides, pesticides) by respecting the distance between the rows and without causing any damage to the crop. A distance from the determined points to the reference trajectory up to 0.532 m was recorded in tests F, D, C and M. Therefore, the S7 receiver, with or without external antenna and without differential correction, the S5 \begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Eigen Value** & **Per Cent** & **Cumulative** \\
**Number** & **Eigen Value** & **Variance** & **Percentage** \\ \hline
1 & 4.09872 & 40.987 & 40.987 \\
2 & 2.90292 & 29.029 & 70.016 \\
3 & 1.13744 & 11.374 & 81.391 \\
4 & 0.899032 & 8.990 & 90.381 \\
5 & 0.375134 & 3.751 & 94.132 \\
6 & 0.183559 & 1.836 & 95.968 \\
7 & 0.148244 & 1.482 & 97.451 \\
8 & 0.127786 & 1.278 & 98.728 \\
9 & 0.0857155 & 0.857 & 99.586 \\
10 & 0.0414487 & 0.414 & 100.000 \\ \hline \end{tabular}
\end{table} 
Table 6: Principal Components Analysis. Figure 6: Principal Components Analysis 2D plot of Component Weights. receiver, in RTK mode and without external antenna, and the low-cost QLV2 receiver can be used on all farms, for mapping crop yield, soil nutrients and humidity. A distance from the determined points to the reference trajectory up to 0.962 m was recorded in tests I and G. As a consequence, the TMMP and S5 receivers, with external antenna and without differential correction, can be used for topographic surveys of areas larger than 50 hectares in order to quantify the Utilised Agricultural Area (UAA) and farm uncultivated areas. Finally, distance from the determined points to the reference trajectory up to 1.439 m was recorded in tests H and L. Therefore, the S5 and TMMP receivers, with internal antenna and without differential correction, can be used only in aided guidance systems of agricultural machines, both for traceability of crop operations and operator safety, according to [28]. Generally, the use of low-cost receivers such as TMMP and QLV2 can allow location of the work site and, then, ensure prompt rescue interventions for operator safety.
## 3 Electric Field Detection Method of Degraded Insulators Based on Sensitive Insulators  
The space electric field detection method of insulators can be applicable to different surface states; however, due to the attitude disturbance, path inclination, and other complex working conditions, if the traditional contactless electric field detection method is used for electric field detection, it will affect detection results. Therefore, it is necessary to improve the traditional detection method.
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 2 Related Work
### AR Benefit Analysis  
Several research studies have validated the potential of AR technology, not only in capturing the attention of exhibition attendees but also in fostering educational environments [1]. It is crucial for AR technology to be thoughtfully integrated to align with the exhibition's objectives. Put differently, visitors should not be so engrossed in the AR effects that they overlook exploring the physical space around them [13]. When the technology becomes the main focus, visitors can become detached from the intended content [14].  
Researchers have utilized questionnaire surveys, observation, semi-structured interviews, and video analysis to analyze the benefits of AR [5]. Nevertheless, the initial three methods solely gather data from the user's viewpoint, posing challenges in conducting an objective analysis of user behavior. Video analysis, on the other hand, enables the examination of user trajectories [15]. Walking directions, trajectories, and distances can also be measured by placing sensors in participants' pockets [3] or on their shoes [4]. However, these approaches necessitate the installation or positioning of devices, presenting significant inconveniences for research analyses.  
In recent times, AR technology has been utilized as a supplementary tool for both users and researchers. For example, [PERSON] et al. [6] created the AR behavior analysis system called navAR to investigate the influence of AR on the book-finding behavior of library users. They collected user behavior data using mobile devices and the internet. With the assistance of this system, it has also been applied in research on art gallery navigation. [PERSON] et al. [8] aimed to understand viewer behavior in \"game mode\" and \"free mode\", and to do so without disrupting the exhibition experience, they designed an experiment where the app was made available for public download. This allowed viewers to choose between the two different viewing modes voluntarily. Viewer behavior data were uploaded to the backend, enabling researchers to analyze differences in audience behaviors. Both of these studies utilized experimental designs, operating in the same space but with different modes.
## 3 Results and Discussion  
### Zeta Potential and Stability  
The zeta potential measurements of \(\upalpha\)-Al\({}_{2}\)O\({}_{3}\) nanoparticles in the nanofluid are displayed in Figure 4a. According to the DLVO theory, higher zeta potential results in higher repulsion between nanoparticles. Therefore, the nanofluid exhibits high stability. The dimensionless total potential energy of the interaction between the nanoparticles was calculated using Equations (1)-(4) [2; 29], and the results are presented in Figure 4b.  
\[V_{T}=\ V_{VDW}+V_{EDL} \tag{1}\]  
\[V_{VDW}=\ -\frac{A}{6h}\bigg{[}\frac{a_{p}^{2}}{2a_{p}}\bigg{]} \tag{2}\]  
\[V_{EDL}=\ 4\pi\varepsilon_{r}\varepsilon_{0}a_{p}\varepsilon_{p}^{2}\bigg{(} \frac{a_{p}+h}{2a_{p}+h}\bigg{)}ln\bigg{[}1+\frac{a_{p}}{a_{p}+h}exp(-\kappa h )\bigg{]} \tag{3}\]  
\[V_{\text{TD}}=\frac{V_{T}}{k_{B}T} \tag{4}\]  
where \(V_{T}\), \(V_{VDW}\), \(V_{EDL}\), and \(V_{\text{TD}}\) are the total energy of interaction, Van der Waals attractive force, electrical double layer repulsive force, and the dimensionless total energy of interaction, respectively. \(A\) is the Hamaker constant for the Al\({}_{2}\)O\({}_{3}\) nanoparticle-nanoparticle interaction (5.3 \(\times\) 10\({}^{-20}\)) [17], \(h\) is the separation distance between nanoparticles (\(m\)), \(a_{p}\) is the nanoparticle's radius (\(m\)), \(\varepsilon_{r}\) is the relative dielectric constant of the base fluid (78.5 for water) [17], \(\varepsilon_{0}\) is the permittivity of the free space (8.85 \(\times\) 10\({}^{-12}\) C/V/m), \(\zeta_{p}\) is the zeta potential of nanoparticles (mV), \(k_{B}\) is the Boltzmann constant (1.3805 \(\times\) 10\({}^{-23}\) J/\({}^{\circ}\)K) [17], \(T\) is the temperature of the system (295 \({}^{\circ}\)K), and \(\kappa\) is the Debye reciprocal length (m\({}^{-1}\)).  
The nanoparticles had the highest zeta potential at a pH value of 4.5. DLVO calculations revealed that the interaction between nanoparticles was repulsive in all cases. However, a notable increase in the nanoparticles' repulsion occurred when the pH was decreased and zeta potential increased. These results are reflected in Figure 5, where a sedimentation test at 4.5, 6.7, and 7.5 pH conditions revealed the same trend. The highest stability was achieved at a pH of 4.5.  
Because of the relation between the Van der Waals attractive force and the distance between the nanoparticles, when nanoparticles collide with each other, they form clusters that are inseparable unless subjected to strong agitation. This clustering behavior is detrimental to nanofluid stability. According to DLVO theory, the electrical double layer force is the only force acting against the clustering of nanoparticles. The electrical double layer force had a positive correlation with zeta potential, which increased at lower pH conditions, explaining the stability results.
## 2 Modeling Approach  
### Classical Thunderstorm Wind Model  
In the classical thunderstorm wind model, the horizontal wind speed of thunderstorm wind is composed of the time-varying mean wind and a uniformly modulated nonstationary random process. As shown in Equation (1), the horizontal resultant wind speed \(U\) is expressed as the sum of the slowly-varying mean wind speed and the residual fluctuation. The extraction of \(\overline{U}\) from \(U\) may be carried out by wavelet and Hilbert transforms, empirical mode decomposition [18, 22; 23; 24] or, more classically as in this paper, by a moving average filter or running mean [17, 19]. There is a wide body of literature on the most suitable choice of the moving average period T: by a moving average filter with a period of 30 s [25]. It is worth noting that the turbulence intensity for thunderstorm events is generally smaller than the classical one for synoptic winds, due to the removal of low-frequency harmonic turbulence components related to the moving average operation [1, 5]. The residual turbulent fluctuations can be further expressed as the product of the slowly-varying standard deviation and the reduced turbulent fluctuation. In the modelling process, the reduced turbulent fluctuation is usually taken as a stationary Gaussian random process with zero mean and unit standard deviation.  
\[U(t)=\overline{U}(t)+U^{\prime}(t)=\overline{U}(t)+\sigma_{U}(t)\widehat{U}^{ \prime}(t) \tag{1}\]  
By introducing the turbulence intensity \(I_{U}(t)=\sigma_{U}(t)/\overline{U}(t)\) into Equation (1), the wind speed model can be obtained as shown below:  
\[U(t)=\overline{U}(t)\Big{[}1+I_{U}(t)\widehat{U}^{\prime}(t)\Big{]} \tag{2}\]  
The gust factor is:  
\[G_{U}=\widehat{U}/\overline{U}_{\max} \tag{3}\]  
where \(\widehat{U}\) is the 1-s peak wind speed [1], and \(\overline{U}_{\max}\) is the maximum value of the slowly varying mean wind speed \(\overline{U}\).  
Figure 1 shows the decomposition results of thunderstorm wind speed signal at the height of 64 m, based on the classical decomposition model. The maximum value of slowly-varying mean wind speed \(\overline{U}\) is 18.14 m/s. The mean value of slowly-varying turbulence intensity is \(\overline{I}_{U}=0.159\). As noted earlier, the relatively important parameter of wind direction is not considered quantitatively in the classical wind velocity modeling method, so it is impossible to achieve the information decomposition of the longitudinal and lateral fluctuation wind speed. Therefore, optimization needs to be conducted.
## 3 System Design with Simulation and Experimental Results
### System Simulation and Experimentation  
#### 3.3.1 Communication System Simulation  
Based on the above simulation framework, computer-based simulations of the communication system were performed. In the simulations, the 4 FSK modulation scheme was configured with a bit rate R = 2.5 kbps, where the carrier frequencies corresponding to symbols 00, 01, 11, and 10 were set to \(f_{1}\) = 40 kHz, \(f_{2}\) = 45 kHz, \(f_{3}\) = 50 kHz, \(f_{4}\) = 55 kHz, respectively. The selection of this frequency band is based on bandwidth requirements and interference control considerations in practical communication environments, with the rationale behind choosing this band and its relationship to communication rates being thoroughly analyzed in our prior research [26].  
Figure 7 demonstrates the relationship between \(\lambda_{s}\) and the BER of the simulated system at different ambient noise levels when the symbol rate is 2.5 kbps. The results show that BER increases with rising noise levels, indicating a direct correlation between noise intensity and error probability. Under identical noise conditions, BER decreases as the parameter \(\lambda_{s}\) increases. At the three noise levels shown in the figure, when \(\lambda_{s}\) approaches \(2.5\times 10^{-3}\), the BER can be reduced to below \(10^{-5}\) due to the increased number of effective UV photons, which enhances frequency discriminability and enables the receiver to distinguish carrier frequencies more accurately.  
To further reduce the BER, considering the error correction mechanism of the channel coding, RS code and LDPC code are introduced into this system. Computer simulations were conducted to evaluate the impact of both codes on system BER performance under varying SNR. The RS and LDPC codes were configured with identical code rates of \(1/2\). Figure 8 demonstrates the relationship between BER and \(\lambda_{b}\) for the three systems under different noise levels. 4 FSK denotes the uncoded system, while 4 FSK-RS and 4 FSK-LDPC represent the original system with RS code and LDPC code incorporated, respectively.  
Figure 7: BER under different noise levels (uncoded system).  
As shown in Figure 8, under identical noise levels, both the 4 FSK-RS and 4 FSK-LDPC systems exhibit significantly lower BER compared to the 4 FSK system. A performance comparison between these two coded schemes reveals that the 4 FSK-LDPC system outperforms the 4 FSK-RS configuration across all tested noise levels, specifically at higher noise levels (e.g., noise power level \(\lambda_{b}=0.0003\)). This observation highlights the inherent advantages of LDPC codes in mitigating noise impairments under adverse channel conditions, thereby ensuring reliable information transmission in UV scattering communication systems.