esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Qualitative Analysis Results  
Qualitative feedback from Group C participants was analyzed using thematic analysis with NVivo 12 Pro, focusing on all interaction modes. Table 10 presents the identified themes. To validate the findings, hypothesis tests were replicated after excluding participants with severe usability issues, confirming consistent results.  
Analysis of Qualitative Feedback  
A. The Hybrid Mode  
The participants praised the flexibility and adaptability of the hybrid mode, highlighting its ability to let users switch between text- and menu-based interactions seamlessly. Feedback such as \"Flexibility to choose preferred method\" and \"Menu options helped guide  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Component** & **Calculation** & **CUQ** & **UES-SF** & **Effectiveness** & **Efficiency** \\ \hline Component 1 & Weight (\(W1j\)) & \(-\)0.081 & 0.039 & 0.995 & \(-\)0.054 \\ \(V1=0.6144\) & Component Weight & \(-\)0.050 & 0.024 & 0.611 & \(-\)0.033 \\  & \(V1*W1j\) & \(-\)0.050 & 0.024 & 0.611 & \(-\)0.033 \\ Component 2 & Weight (\(W2j\)) & 0.968 & \(-\)0.233 & 0.088 & 0.003 \\ \(V2=0.2752\) & Component Weight & 0.267 & \(-\)0.064 & 0.024 & 0.001 \\  & \(V2*W2j\) & 0.236 & 0.972 & \(-\)0.019 & \(-\)0.005 \\  & \(V3*W3j\) & 0.026 & 0.107 & \(-\)0.002 & \(-\)0.001 \\  & \(V1*W3j\) & \(-\)0.006 & 0.008 & 0.053 & 0.999 \\ Component 4 & Weight (\(W4j\)) & 0.000 & 0.0000 & 0.0000 & 0.0002 \\ \(V3=0.0002\) & Component Weight & 0.0000 & 0.0000 & 0.0000 & 0.0002 \\  & \(V4*W4j\) & 0.243 & 0.067 & 0.633 & \(-\)0.033 \\ Combined Weight (Equation (4)) & 0.267 & 0.073 & 0.696 & \(-\)0.036 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The PCA component loadings (weights) for each metric.  
my text questions\" reflects its effectiveness for complex tasks. However, challenges like decision-making between modes (\"Sometimes felt overwhelming with too many choices\") impacted the efficiency scores.  
The hybrid mode excelled in effectiveness (mean = 85.6) and error handling (mean = 76.9), with high CUQ scores in onboarding and learnability. However, its UES-SF scores in focused attention showed variability in complex tasks.  
The text-based mode performed well in handling complex tasks, with users praising its clarity for detailed queries. Statements like \"I had to rephrase questions often\" indicate challenges in guiding users through simpler tasks, impacting efficiency. Effectiveness scores were high in complex scenarios, reflecting its robustness in handling detailed queries. CUQ scores for personality (mean = 69.8) and onboarding (mean = 74.9) were strong, while learnability (mean = 91.2) and error handling (mean = 75.7) highlighted ease of use. Moderate efficiency scores (mean = 76.03) suggest room for improvement in response times for straightforward tasks.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Mode** & **Theme (Subtheme)** & **Examples of Statements** \\ \hline \multirow{5}{*}{Hybrid (Initial Perception)} & Flexibility & “Flexibility to choose preferred method” \\  & & “_“I shall \(\textit{size}\_\)\(\textit{
## 3 Results and Discussion
\"Dry\" substrates were obtained by drying the substrate immediately after the TM removal and the following washing, while the \"wet\" ones were left in distilled water until the analyte exposure. During the substrate drying, gap hot spots are formed in the spacing between the neighboring Ag-NWs. When the analyte is added to the \"dry\" substrate, the molecules are adsorbed along the entire Ag-NWs length, but can no longer be placed in the spacing between the neighboring Ag-NWs, since they were already learnt before the analyte exposure. In the case of the \"wet\" substrate, the analyte molecules are also adsorbed on the entire Ag-NWs length. In the drying process, the Ag-NWs are leaning, and the molecules are trapped in the nanometric gap between the neighboring Ag-NWs, that is, in the gap hot spot region. Figure 5 shows SERS-spectra of 10 \(\upmu\)g/mL 4-MPBA adsorbed on the substrate with 1.1 \(\upmu\)m Ag-NWs. All spectra were recorded six times from the different places of the substrate and then averaged, and the standard deviations are presented in Table 1. Based on the obtained spectra, we can conclude that on the \"wet\" substrates the probability that the analyte molecules will be in the gap hot spot region is greater than on the \"dry\" ones. It may be demonstrated by the spectrum intensity on the \"wet\" substrate, which is almost two times higher compared to the \"dry\" one. Moreover, the SERS-signal of 4-MPBA obtained on the substrate with the Ag-NWs array is more detailed compared to ordinary Raman of neat solid 4-MPBA presented in the paper [37]. The Raman spectrum of 10 mg/mL 4-MPBA also was recorded in our experiment and the most intense peak is presented in Figure 5. The Raman spectrum was recorded at the laser power of 120 mW and acquired in 10 s; a 785 nm laser was used. To lead the Raman spectrum to the same conditions as  
Figure 4: Schematic procedure for the gap hot spots formation on the array of Ag-NWs bundles in case of the <<dry>> and <<wet>> SERS-substrates. (**1**) The array of Ag-NWs on a metal substrate is being in the water after etching of TM. (**2a**) as distilled water was evaporated, Ag-NWs were learnt together by its tips due to the capillary force. (**3a**) An analyte was added on the already formed Ag-NWs bundles, so the analyte molecules could not be in the spacing between neighboring Ag-NWs tips and consequently in the gap hot spot region (**4a**). (**2b**) an analyte was added on wet substrates. (**3b**) Molecules of an analyte were adsorbed on the entire length of the Ag-NWs, and during the Ag-NWs leaning, the molecules were trapped in the gap hot spot region. the SERS spectra, we assume that the intensity dependence on the laser power and the accumulation time is linear, so the Raman intensity was divided by 40.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology
### DTMC Visualisation  
Once DTMC models were created, to have a better understanding of them so that we could make more sense of the gaze movement process, several attempts were made to visualize the model in different ways. One option is to use the built-in function in MATLAB, which is similar to using the R plotting function in R Studio. However, those functions generate static plots and can be overcrowded in the case of a model with many states. A more interactive plot, better to show the transition process as well, was therefore used for our study.  
Therefore, in order to enhance comprehension and readability, the DTMC model given in this work would incorporate animations, illustrating a clear graph and modelling the transition process. To do this, the \"D3.js\" JavaScript package was used to generate an interactive display of the DTMC, complete with the animation of each state transition.
# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 3 Three Versions of Running Skewness and Efficient Calculation Methods  
Given the continuous nature of online data flows, it is improper to assume online learning to be based on a complete data population. Therefore, we treat the processed data always to be samples, and correspondingly, we investigate the sample skewness instead of the population skewness of data. Furthermore, we take into account three versions of running skewness that may be the most useful ones for implementing skewness-aware online edge learning, such as the following:  
* _Running Cumulative Sample Skewness_ recursively measures the skewness of the time series data filtered from the beginning of measurement up until the current moment. This skewness version is suitable for the situation when the individual data points in a time series are all equally important (e.g., [15]).
* _Running Rolling_ (_Simple Moving_) _Sample Skewness_ recursively measures the skewness of the time series data within a fixed-size while continuously moving sample window. This skewness version is able to match the learning scenario in which each datum is valid only for a period of time (e.g., [35]).
* _Running Exponentially Weighted Sample Skewness_ also recursively and cumulatively measures the skewness of time series data. Different from the cumulative version, the data are accompanied with weighting factors that decrease exponentially as the data recede into the past. This skewness version would be more practical in the process industry [36] and in the financial sector, as the more recent data can better reflect the current system/market status.  
To facilitate explaining and distinguishing between these skewness versions, we clarify the terminology used in this research. Firstly, we follow GNU's naming convention to emphasise the recursive feature of running statistics by highlighting the term \"Running\" [13], because \"Moving\" also means the moving activity and might result in confusions from time to time (e.g., [16]). Secondly, since \"rolling window\" is widely used when describing the moving activity in the literature, we employ the term \"Rolling\" to characterize the second skewness version. Thirdly, we refer to \"exponentially weighted sample statistics\" [37] to shorten the name of the third skewness version. Overall, for both reference convenience and distinctiveness, we use Cumulative Sample Skewness **(CSS)**, Rolling Sample Skewness **(RSS)**, and Exponentially Weighted Sample Skewness **(EWSS)**, respectively, to indicate the three running skewness versions in the rest of this paper.  
Furthermore, to facilitate reading and verifying the equations presented in the rest of this paper, we summarise the relevant notations in Table 1.
## 5 Experimental Data Analysis Based on Linear Regression  
### Data Analysis of Disturbance Force and Lateral-Line System Data on Surge  
As mentioned in Section 4.1, the first set of experiments is about the relationship between disturbance force and the data of the artificial-line system in surge. The underwater robot used in this experiment is cylindrical with a large length-to-diameter ratio, so that the torque in yaw caused by the uneven force in the surge direction can be ignored.  
The data of sensor \(p_{0}\) located at the bow surface of the ROV is for the pressure data of the facing-flow surface. The mean value of \(p_{11}\), \(p_{12}\), \(p_{13}\), \(p_{14}\), \(p_{R1}\), \(p_{R2}\), \(p_{R3}\) and \(p_{R4}\) is taken as the pressure on the non-facing-flow surface.  
\[p_{surge}=p_{f}-p_{nf} \tag{11}\]  
where \(p_{f}=p_{0}\), and \(p_{nf}\) is the mean value of \(P_{L1}\), \(P_{L2}\), \(P_{L3}\), \(P_{L4}\), \(P_{R1}\), \(P_{R2}\), \(P_{R3}\) and \(P_{R4}\).  
The relationship between \(p_{surge}\) and disturbance force \(F_{surge}\) is analyzed by the multiple linear regression method, and the regression equation is derived as (12).  
\[F_{surge}=a_{1}p_{surge}-b_{1} \tag{12}\]  
where \(a_{1}\) and \(b_{1}\) are the parameters obtained by multiple linear regression [36].  
Figure 8: Frequency response curve of digital filter.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 5 Experiments for LoRa MAC
This section reports the four different experiments (from Exp.A to Exp.D) in which we consider three parameters to represent application requirements (number of nodes, data size, and data period) and three regional, regulative parameters (sensing threshold for LBT, back-off algorithm, number of sensing counts) as shown in Table 6. Each experiment is iterated ten times, and the averaged results are demonstrated in the following subsections. \begin{table}
\begin{tabular}{l|c c c c} \hline
**Parameters** & **Exp.A** & **Exp.B** & **Exp.C** & **Exp.D** \\ \hline Number of nodes & _variable_ & _variable_ & _variable_ & 3000 \\ Payload length & _variable_ & 65, 32 bytes & 32 bytes & 32 bytes \\ Data period & 15 s & _variable_ & 60 s, 180 s & 60 s \\ LBT sensing threshold & \(-130\) dBm & \(-130\) dBm & _variable_ & \(-130\) dBm \\ back-off algorithm & fixed 4 s & fixed 4 s & fixed 4 s & _variable_ \\ Number of sensing & \(\infty\) & \(\infty\) & \(\infty\) & _variable_ \\ \hline \end{tabular}
\end{table} 
Table 6: Parameters and values in four experiments. Figure 13: A gateway is at the center (0,0), and 3000 nodes are deployed according to a uniformly random distribution in a \(2690\times 2690\) m area. Nodes have different SF values according to their distance to the gateway.
## 4 Free Surface RANS Solver and Validation
### Computational Domain and Boundary Conditions  
The WAVIS solver is capable of automatic grid generation based on hull surface mesh and computational domain, thereby saving efforts required for grid generation. Details on the automatic grid generation can be found in [PERSON] et al. [44; 45]. Figure 4 displays the computational domain and the boundary conditions. The computational domain was defined for \(-1.5\leq x/LBP\leq 1.5\), \(0\leq y/LBP\leq 1.0\) and \(-1.0\leq z/LBP\leq 0.023\). Undisturbed free surface was set at \(z=0\). The FP (front perpendicular) and AP (after perpendicular) of the model were located at \(x/LBP=0.5\) and \(x/LBP=-0.5\), respectively. \(y=0\) corresponded to the centerplane around which the symmetry of the hull form allowed half domain simulation. No-slip condition was applied on the hull surface with the standard wall function being utilized to compute turbulence quantities at \(y^{+}=50\).
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 3 Results
### Performance Evaluation of the ANFIS Model  
In an ANFIS analysis, a linear output function was employed in conjunction with different types of MFs for the inputs, including triangular, trapezoidal, and Gaussian shapes. Each input variable was divided into three fuzzy classes to facilitate rule generation and to improve model resolution. As an example, the fuzzy rules generated by the ANFIS model using the triangular membership (Trimf) function and the corresponding linear model parameters are listed in Table 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rules**} & \multicolumn{3}{c}{**Input**} & \multicolumn{3}{c}{**Output**} \\ \cline{2-6}  & **Soil Depth (cm)** & **Average Temperature (\({}^{\circ}\)C)** & \multicolumn{3}{c}{**Soil Temperature (\({}^{\circ}\)C) Parameters**} \\ \hline  & & \multicolumn{2}{c}{**Trimf (3 \(\times\) 3)**} & & \\  
igh & \(120.39\) & \(-469.59\) & \(1.20\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: ANFIS rules and parameters.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Defuzzification**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} \\ \cline{2-7}  & **MAE** & **RMSE** & **R2** & **MAE** & **RMSE** & **R2** \\ \hline Centroid & 2.58 & 3.29 & 0.83 & 2.83 & 3.45 & 0.85 \\ Bisector & 2.72 & 3.46 & 0.82 & 3.06 & 3.77 & 0.83 \\ MOM & 3.63 & 4.45 & 0.76 & 3.88 & 4.80 & 0.75 \\ LOM & 4.61 & 6.01 & 0.73 & 4.85 & 6.06 & 0.70 \\ SOM & 4.42 & 5.46 & 0.76 & 4.96 & 6.07 & 0.77 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics of the MFIS models on training and testing dataset.  
Table 7 summarizes the performance metrics of the fuzzy logic models developed using different types and configurations of MFs based on input variables, including soil depth and average air temperature. For the 3 \(\times\) 3 MF configurations, all three MF types--Triangular (Trimf), Trapezoidal (Trapmf), and Gaussian (Gaussmf)--performed comparably. On the testing dataset, these models yielded consistent MAE values ranging from 1.49 to 1.55 \({}^{\circ}\)C, an RMSE of approximately 1.97 \({}^{\circ}\)C, and an R\({}^{2}\) of 0.95, reflecting a high degree of accuracy and model fit. By contrast, the 4 \(\times\) 4 MF configurations demonstrated slightly improved performance on the training set. Among them, the Trimf (4 \(\times\) 4) configuration achieved the lowest MAE (1.35 \({}^{\circ}\)C) and RMSE (1.88 \({}^{\circ}\)C), while maintaining a high R\({}^{2}\) of 0.94. In testing, this configuration continued to perform well with an MAE of 1.46 \({}^{\circ}\)C, an RMSE of 1.89 \({}^{\circ}\)C, and an R\({}^{2}\) of 0.95. Other 4 \(\times\) 4 MF types produced similar results, suggesting that increasing the number of fuzzy sets did not significantly enhance accuracy, but may improve generalization under certain conditions. In summary, the Trimf (4 \(\times\) 4) configuration provided the most favorable balance between model accuracy and complexity, and was therefore the most effective approach among the tested alternatives.
## 4 Application
### Clinical Application
#### 4.1.6 Monitoring Dialysis Patients  
[PERSON] et al. [PERSON] et al. (2019) extracted HR, RR and SpO\({}_{2}\) of patients going through haemodialysis's treatment in the Renal Unit of the Churchill Hospital in Oxford, UK, based on iPPG using a high-quality 5 megapixel camera. [PERSON] et al. [PERSON] et al. (2019) also applied iPPG to monitor HR, RR, and SpO\({}_{2}\) of hemodialysis's patients in the Oxford Kidney Unit.
## Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arabic Land Ratio  
**[PERSON], [PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth,  
Chinese Academy of Sciences, Olympic Village Science Park, W. Beichen Road, Beijing 100101,  
China; E-Mails: [EMAIL_ADDRESS] (M.Z.); [EMAIL_ADDRESS] (M.Y.); [EMAIL_ADDRESS] (W.Z.); [EMAIL_ADDRESS] (Y.Z.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-10-6485-5689; Fax: +86-10-6485-8721.  
_Received: 19 January 2014; in revised form: 31 May 2014 / Accepted: 3 June 2014 / Published: 19 June 2014_  
profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method's application in other regions and agricultural systems.  
**Keywords:** crop condition assessment; cropped and uncropped arable land; North China Plain; NDVI; linear unmixing
## 4 Discussion
### Reflection on the Results
They found that the quality and the outcome of these devices under different circumstances were very different, and that the location where these devices are worn has an impact on the results. The smallest deviation, both indoor and outdoor, was found when the device was placed on the sides of the eye. In our study, one participant placed the sensor on his glasses. Other participants wore the sensor on the chest, hence less close to the eye. The actual personal light exposure as logged by the person-worn sensors also cannot be attributed singularly to the luminaires, as they may have also varied with, for instance, changing movement patterns and weather conditions. This is why we adopted prolonged tracking within conditions and repetition within conditions. It turned out that at the group level, the introduction of the lighting system did not result in a consistent statistically significant effect across the four phases. Additional results on the subgroups showed that this absence of an effect was probably at least partially caused by the subgroup of people who started the experiment during the summer. These people even had significantly higher illuminance values during the first phase A1 than during the introduction of the lighting system in phase B1. The subgroup of people who started the experiment in the winter showed significantly higher levels of light when the lighting system was introduced during phase B1. From the royal national weather station in the Netherlands [47], it is known that the number of sun hours in the Netherlands at the end of the summer (September) of 2019 was much higher than in October 2019 (157.4 h vs. 99.8 h), the month in which the lighting system was introduced for the subgroup. The limitations of the study are fairly obvious. The participants were not blind to the exposure condition, as they knew when the lighting system was present and when it was removed. They also knew when data collection took place. Hence, we cannot attribute the participants' subjective responses and experiences singularly to either the light or the mere presence and design of the luminaires. This study was conducted with a carefully selected lighting system, but other, potentially better or worse, systems could have been selected. Lighting systems could be compared by comparing one intervention with another. In addition, the target group was very heterogeneous, as often mentioned, and there were various external influences that were hopefully all considered, but it cannot be ruled out that there were also invisible confounders. People with dementia are not always able to put themselves and what is going on inside them into words. More field studies should be performed to define the best applicable study protocol for this population.
## 3 Results
### Sample Characterization
#### 3.1.2 EDX Analysis  
The EDX analysis of the adsorbent (date seed activated carbon) before and after Cu and Pb adsorption is shown in Figures 5-7. Before adsorption, the activated carbon contains various elements, such as O, Cl, Na, Si, K, Br, and Mg, along with a major peak for C. The presence of trace elements indicates residual components originating from the seed's inherent mineral content. The EDX analyses of lead- and copper-adsorbed activated carbon confirm a high carbon content, characteristic of activated carbon. After adsorption, distinct Cu and Pb peaks appear, which directly signify metal uptake by activated carbon. At the same time, the oxygen content is decreased, which is indicative of the fact that metal ions interacted with surface functional groups by ion exchange or complexation. The removal of elements such as sodium, silicon, and potassium also provides support to the ion replacement hypothesis for heavy metals. Additionally, the significant loss of oxygen content is indicative of the participation of oxygenated functional groups such as hydroxyl (-OH) and carboxyl (-COOH) in chelating Cu\({}^{2+}\) and Pb\({}^{2+}\) ions. In the copper-adsorbed activated carbon samples, copper, oxygen, phosphorus, and silicon were detected, while the lead-adsorbed activated carbon exhibited the presence of lead, oxygen, phosphorus, and iron. These results confirm the potency of activated carbon in adsorbing both copper and lead while maintaining its carbon-rich structure.  
In the Pb- and Cu-adsorbed samples, metal ion binding may reduce or displace the various functional groups present in activated carbon, leading to a relative increase in the carbon signal due to the decrease in oxygen and other surface elements.  
Figure 4: Scanning electron micrographs (SEM) of (**a**) PAC before adsorption, (**b**) PAC after Cu adsorption, and (c) PAC after Pb adsorption.