esa-sceva/satcom-chunk-collection huggingface dataset:
# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
## 4 Results and Discussion
### Comparison of LSM Results  
For analysis with LR, in order to determine the probability of landslide occurrence in a certain area, probability was calculated using Equation (1) and the file was converted to raster format to obtain an LSM. The probability of landslide occurrence is given as a value between zero (0) and one (1), and the LR map has a pixel size of 25 \(\times\) 25 m. The LR-based map classified the data into five groups, from extremely low to extremely high (Figure 7). With the AHP method, if the consistency ratio values are less than 0.1 the binary comparison matrix is acceptable, but if the values are greater than 0.1 then the comparisons must be reassessed [121]. In this study, the consistency rate was 0.03, indicating a reasonable level of consistency in pairwise comparison (Table 4). Paired comparison according to the consistency ratio is good enough to recognize the factor weights in the landslide susceptibility model. The LSM prepared using AHP method is given in Figure 8. The weighting of the parameters in the LSM via matrix was calculated using [PERSON]'s [104] weighting module and was divided into five classes, from extremely low to extremely high. Figure 7: Landslide susceptibility map prepared with LR method. In cases where these calculated FR values are less than 1, the effect of this parameter on landslides may be low. In cases where it is greater than 1, the effect of this parameter on landslides can be considered high. FR values for causative factors are given in Table 5. In this study, the LSM based on the FR method was classified using the natural breaks method and divided into five classes, from extremely low to extremely high (Figure 9). \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Parameter** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **Weight** \\ \hline (1) Slope aspect & 1 & & & & & & & & 0.1617 \\ (2) Density of discontinuity & 1/2 & 1 & & & & & & 0.1113 \\ (3) Lithology & 1 & 2 & 1 & & & & & 0.1617 \\ (4) Slope & 2 & 3 & 2 & 1 & & & & 0.2716 \\ (5) SPI & 1/4 & 1/3 & 1/4 & 1/6 & 1 & & & 0.0458 \\ (6) Slope curvature & 1/5 & 1/4 & 1/5 & 1/7 & 1/2 & 1 & & 0.0307 \\ (7) Elevation & 1/6 & 1/5 & 1/6 & 1/8 & 1/3 & 1/2 & 1 & & 0.0221 \\ (8) Land use & 1 & 1 & 1 & 1/2 & 4 & 5 & 6 & 1 & 0.1495 \\ (9) Distance from stream & 1/4 & 1/3 & 1/4 & 1/6 & 1 & 2 & 3 & 1/4 & 1 & 0.0458 \\ Consistency ratio: 0.03 & & & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Pair-wise comparison matrix, factor weights and consistency ratio of the data layers. Figure 8: Landslide susceptibility map prepared with AHP method. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{**Factor**} & \multicolumn{1}{c}{**Class**} & \multicolumn{1}{c}{**No. of Pixels**} & \multicolumn{1}{c}{**Percentage**} & \multicolumn{1}{c}{**No.
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 4 Simulation Model
### Optical Channel Model  
The transmission medium is modeled as free space without obstacles. We chose the directed LOS link configuration to model the optical signal propagation, requiring a line of sight between each device and the coordinator. We have considered the direct component of the received signal to calculate the power of the received optical signal, not considering the existence of reflections. According to the results presented in [20], at least 90% of total received optical power is direct light in VLC when using a receiver field of view (FOV) of 60\({}^{\circ}\). Therefore, to ensure the validity of our implemented model we have configured all optical receivers using the value of 60\({}^{\circ}\) for FOV. The adopted optical channel model facilitates reaching high transmission speeds, since the effects of multipath distortion on the optical signal are not considered. Among its drawbacks is the limitation in device mobility, since they must be permanently oriented to the coordinator. To meet this requirement, devices in our simulation model are able to automatically point to the coordinator. Calculation of received power considering only direct component of the signal has the additional benefit of improving the efficiency of the implemented simulation model. The computational load required to run simulations of scenarios with multiple nodes including the functionality of different layers of the architecture is significantly reduced.
## 6 Differential Evolution Algorithm and Improvements
### Conventional DE
#### 6.1.2 Mutation  
The mutation is a crucial operator in the DE algorithm, which promotes the exploration of the search space to locate the region of the global minimum and improves the quality of the solutions. In this regard, for each target vector \(\mathbf{x}_{i}^{(G)}\), the DE algorithm creates the corresponding mutant vector \(\mathbf{v}_{i}^{(G+1)}=\left[v_{i,1}^{(G+1)},v_{i,2}^{(G+1)},\ldots,v_{i,j}^{( G+1)},\ldots,v_{i,n}^{(G+1)}\right]^{T}\). The vector \(\mathbf{v}_{i}^{(G)}\) is usually created as a combination of weighted vector differences between randomly chosen vectors. Numerous mutation techniques have been presented in the literature with the intent of enhancing exploration, exploitation, or both. The most prominent mutation schemes include [73]:  
DE/rand/1  
\[\mathbf{v}_{i}^{(G+1)}=\mathbf{x}_{r_{1}}^{(G)}+F\Big{(}\mathbf{x}_{r_{2}}^{ (G)}-\mathbf{x}_{r_{3}}^{(G)}\Big{)}, \tag{28}\]  
DE/rand/2  
\[\mathbf{v}_{i}^{(G)}=\mathbf{x}_{r_{1}}^{(G)}+F\Big{(}\mathbf{x}_{r_{2}}^{(G )}-\mathbf{x}_{r_{3}}^{(G)}\Big{)}+F\Big{(}\mathbf{x}_{r_{4}}^{(G)}-\mathbf{x }_{r_{5}}^{(G)}\Big{)}, \tag{29}\]  
DE/best/1  
\[\mathbf{v}_{i}^{(G+1)}=\mathbf{x}_{best}^{(G)}+F\Big{(}\mathbf{x}_{r_{1}}^{ (G)}-\mathbf{x}_{r_{2}}^{(G)}\Big{)}, \tag{30}\]  
DE/best/2  
\[\mathbf{v}_{i}^{(G+1)}=\mathbf{x}_{best}^{(G)}+F\Big{(}\mathbf{x}_{r_{1}}^{ (G)}-\mathbf{x}_{r_{2}}^{(G)}\Big{)}+F\Big{(}\mathbf{x}_{r_{3}}^{(G)}-\mathbf{ x}_{r4}^{(G)}\Big{)}, \tag{31}\]  
DE/current-to-best/1  
\[\mathbf{v}_{i}^{(G+1)}=\mathbf{x}_{i}^{(G)}+F\Big{(}\mathbf{x}_{best}^{(G)}- \mathbf{x}_{i}^{(G)}\Big{)}+F\Big{(}\mathbf{x}_{r_{1}}^{(G)}-\mathbf{x}_{r_{2} }^{(G)}\Big{)}, \tag{32}\]  
where \(r_{1},r_{2},r_{3},r_{4}\) and \(r_{5}\) are mutually distinct integers randomly generated from the set \(\{1,2,\ldots,NP\}\setminus\{i\}\), the \(\mathbf{x}_{best}^{(G)}\) denotes the individual with the best objective function value in the entire population at the current generation \(G\) and \(F\) is the scale factor that controls the scaling ratio of the difference vector. It has been shown that besides choosing the appropriate mutation scheme the value of scale factor plays a significant role in the optimization outcome [28].
## 3 Data Analysis and Results
### The Arkalochri Aftershock Sequence  
In this section, we investigate the space-time distribution of the main event's aftershock sequence, which struck the Greek island of Crete at a depth of about 10 km on 27 September 2021 [33; 34] The earthquake's epicenter was located southeast of Heraklion. The mainshock had a magnitude \(M_{w}\)6.0. Based on a detailed examination of the aftershock sequence, as located by the Hellenic Unified Seismological Network (HUSN) station network ([[http://www.gein.noa.gr/en/networks/husn](http://www.gein.noa.gr/en/networks/husn)]([http://www.gein.noa.gr/en/networks/husn](http://www.gein.noa.gr/en/networks/husn)), accessed on 27 September 2022), the aftershock area encompasses the region between longitudes 25.17\({}^{\circ}\) E-25.40\({}^{\circ}\) E and latitudes 35.03\({}^{\circ}\) N-35.24\({}^{\circ}\) N. The aftershocks' catalogue includes events characterized by magnitudes 2.5 \(\leq M_{w}\leq\) 5.8, with a completeness magnitude of \(M_{c}\) = 2.5. According to different networks and catalogues, \(M_{c}\)-value varies systematically in space and time. However, we should be cautious because commonly this value may lead to inaccurate estimations in statistical analyses due to it being higher in the early part of an earthquake sequence.  
We study the probability distribution of interevent times in the aftershock series of the Arkalochori, 2021 event using the NESP framework, as described previously. This approach  
Figure 2: This flowchart summarizes the process behind the non-extensive statistical physics model implemented in the present work.  
results from the generalized expression of entropy (Equation (1)), which is characteristic for complex systems with finite degrees of freedom and long-range memory [5; 8; 22].  
Figure 3a shows a typical \(Q\)-exponential pattern in the log-log plot of the cumulative distribution function (CDF), \(P(>T)=N(>T)/N_{0}\) of aftershocks' interevent times. Figure 3a shows that for values of T greater than a critical interevent time \(T_{c}\) (i.e., when \(T>T_{c}\)), there is a divergence from the \(Q\)-exponential function. Furthermore, fitting the \(Q\)-exponential to the instances up to a value near to \(T_{c}\) yields \(q=1.62\), as shown by Equations (7) and (8).  
Next, we show \(ln_{Q}P(>T)\) (see Equation (10)) as a function of interevent times \(T\) for \(q=1.62\) in Figure 3b. We estimate \(T_{c}\) to be \(\approx\)7750 s based on the divergence from predicted linearity during the transition from one system to another.  
Figure 3c illustrates that the \(T\) evolves as a function of time \(t\) since the main shock. The \(T_{c}\) value indicates that the majority of interevent times have a value of \(T\) below \(T_{c}\) (Figure 3c) in the early aftershock time, supporting the idea that the NESP mechanism is predominant in the beginning of the aftershock evolution, indicating finite degrees of freedom and long-range memory effects. As time passes, these traits of the aftershock sequence are not prevalent anymore and BG statistics are restored (i.e., \(q=1\)) [14].
## 5 Performance Evaluation
### Event-Triggered vs. Time-Triggered  
In the second set of simulations, the performance of event-triggered and time-triggered CLAFS methods is compared. To facilitate comparisons with the event-triggered scheme simulated in the first set of experiments, the invocation interval for the time-triggered feedback scheduler is set as \(T_{FS}=T_{ED}\) = 500 ms.  
Fig. 11 depicts the step responses of loop 1 under both scenarios when the time-triggered CLAFS scheme is applied. The QoC is pretty good. Comparing Fig. 11 with Fig. 8, it can be seen that the time-triggered and event-triggered CLAFS achieve comparable QoC.  
The sampling periods and the DMRs for the system using time-triggered CLAFS are presented in Fig. 12. They vary in the same manner as under event-triggered CLAFS. The main difference between them is that with event-triggered feedback scheduling the sampling periods remain unchanged at some consecutive sampling instants (see Fig. 9), which implies that the feedback scheduler does not actually execute, whereas the sampling periods are updated at every sampling instant when time-triggered feedback scheduling is used (see the upper part of Fig. 12).  
To this point only the QoC is examined, which is _ideal_ in that the effect of feedback scheduling execution is not taken into account. That is, in the above simulation experiments the overhead of feedback scheduling is neglected. For the purpose of comparison, the _times of execution_ of the feedback scheduler is used as a simple criterion for measuring the feedback scheduling overhead.  
Table 1 compares the total control costs of the system (calculated by the sum of the integral of absolute error of each control loop) and the times of execution of the feedback scheduler with different invocation mechanisms. For different invocation mechanisms the overall QoC remains almost identical in both scenarios. In Scenario I, the times of execution of the feedback scheduler decreases 37.5% with event-triggered CLAFS as compared to the time-triggered case. In Scenario II it reduces from 16 to 4, with a relative reduction of 75.0%.  
The above results show that the proposed event-triggered invocation mechanism yields significant reduction in feedback scheduling overheads while providing comparable feedback scheduling performance, thus improving the efficiency of the CLAFS scheme. Furthermore, by simply selecting a smaller \(T_{ED}\) value, the event-triggered invocation mechanism can be used to achieve quicker response associated with the feedback scheduler, without introducing too large overheads.
## 4 Results
### Evaluating Indicator  
In order to ensure the objectivity of the results, this article chose to evaluate the models using two metrics: BLEU score and human evaluation. The essence of the BLEU score is to measure the similarity between two texts, so calculating the written language produced in the test set and comparing it with the original written language can reflect the performance of the model. The formula for \(BLEU\) is shown in Equation (6):  
\[BLEU=BP*\exp(\sum_{i=1}^{N}W_{n}\log P_{n}), \tag{6}\]  
In Equation (6), \(P_{n}\) represents the probability of shared subsequences, specifically, n-grams appearing in the reference sentence, with \(n\) up to 4. Thus, only the precision of up to 4 g is measured. \(W_{n}\) represents the weights of each n-gram. \(BP\) represents a penalty factor that biases BLEU towards shorter translations. When the predicted sentence is shorter than the reference sentence, it is set to 1.  
Otherwise, it is dependent on the length ratio between the predicted and reference sentence, as shown in Equation (7):  
\[BP=\left\{\begin{array}{ll}1&,\text{ if }\text{l}_{\text{c}}>\text{l}_{ \text{s}},\\ \text{e}^{1-\frac{\text{l}_{\text{c}}}{\text{c}}}&,\text{ if }\text{l}_{\text{c}} <\text{l}_{\text{s}}.\end{array}\right. \tag{7}\]  
The final BLEU score is calculated as the average BLEU score of all sentences in the test set. This article calculates BLEU scores for both word-grams and character-grams. However, BLEU is only a substitute for human evaluation, and its disadvantages include the lack of consideration of grammatical accuracy and no consideration of synonyms or  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hardware Environment** & **Software Environment** \\ \hline CPU: 12 vCPU Intel(R) Xeon(R) Platinum & \\
8255C CPU @ 2.50 GHz & Operating system: ubuntu20.04 \\ Memory: 40 GB & PyTorch 1.11.0 + Python 3.8 \\ Graphics card: RTX 3090 ti (24 GB) & CUDA: 11.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hardware and software environment.  
similar expressions. Therefore, it is necessary to add a human evaluation as an evaluation criterion for the model.  
The manual evaluation method used in this article is in the form of a questionnaire survey. Each questionnaire was composed of 500 non-colloquial sentence pairs randomly selected with a replacement from the test set. They were distributed to 20 graduate students at our university to evaluate the effectiveness of de-colloquialization for each sentence on a ten-point scale. The scores were averaged for each questionnaire, and the average scores across all questionnaires determined the final de-colloquialization score for the model.
# Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland
## 5 Results
### Validation of Optimal Band Selection Scenario for FSC Estimation  
The Blue band yielded accurate results when FSC is between 0% and 20%. Grassland presence was reflected in the low Blue band amount. Between 60% and 100%, MoNIR + shadow had higher accuracy values, but they were not as high as expected. In the algorithm, we decided to apply shadow when the FSC was greater than 60% because that was when it showed better accuracy.  
Pixel misclassification was caused by many factors. At the beginning of the season, grassland reflectance values were high in the Red and MoNIR bands. During snow presence, there was no constancy in the level of brightness. When it was bright, there were more shadow areas, and MoNIR and the Blue band had different behaviors. Sastrugi also caused misclassification because of the different shadows that it produced. Table 2 summarizes the accuracy of the 44 images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Scenarios**} \\ \hline
**Snow Cover** & **B** & **MoNIR** & **B + Shadow** &
\begin{tabular}{c} **MoNIR + Shadow** \\ \end{tabular} \\ \hline
0\% & 0.827 & 0.661 & 0.674 & 0.432 \\
10\% & 0.867 & 0.795 & 0.843 & 0.740 \\
20\% & 0.841 & 0.765 & 0.835 & 0.747 \\
30\% & 0.825 & 0.865 & 0.815 & 0.860 \\
40\% & 0.720 & 0.720 & 0.720 & 0.713 \\
50\% & 0.740 & 0.690 & 0.760 & 0.710 \\
60\% & 0.760 & 0.775 & 0.830 & 0.895 \\
70\% & 0.601 & 0.641 & 0.613 & 0.697 \\
80\% & 0.485 & 0.650 & 0.510 & 0.688 \\
90\% & 0.457 & 0.602 & 0.363 & 0.730 \\
100\% & 0.591 & 0.738 & 0.741 & 0.913 \\ Sastrugi & 0.673 & 0.876 & 0.692 & 0.916 \\ Average & 0.699 & 0.732 & 0.700 & 0.753 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy obtained in the four scenarios for different FSC values.  
Figure 11: Ratio (B/R) against FSC estimated by (**a**) Expert 1 and (**b**) Expert 2.
## 3 Method and Materials
### Preprocessing
#### 3.2.3 Principal Component Analysis (PCA)  
PCA was employed on the training dataset to reduce the dimensionality of the tabular data. This step was crucial to avoid data leakage and ensure that the model was trained only on the information available in the training set, without any influence from the test set. By focusing on the most informative features, PCA helped in enhancing the training process and the model's performance. Figure 2 shows the tabular dataset before and after PCA.
## 3 Results and Discussion
### UV-Visible Spectroscopy of the PLC Nanocomposite  
The UV-Vis absorption spectra of the unirradiated, as well as EB-irradiated, PLC nanocomposite are presented in Figure 7. The spectra reveal that the absorbed peaks at around 370 nm and 400 nm correspond to the excitation of benzene and the residual dopants, respectively [37]. It can be seen that the absorption spectra tended to shift toward higher wavelengths after irradiation; this means the samples underwent the radiation processes and formed fragments in the polymer matrix, which indicates that the number of dipoles increased.  
Figure 5: (a) TGA and (b) DTA curves of Unirr and EB-irradiated PLC nanocomposites.  
The Unirr composite exhibited a peak at 374 nm that shifted to 393, 396, and 400 nm after 40, 80, and 120 kGy EB doses, respectively [9]. The shift in the wavelength confirms that the unsaturated conjugated bonds increased in the polymer nanocomposite, leading to a gradual decrease in the optical band gap after irradiation and, hence, helping to increase the conductivity. It is also noted that the absorbance increased with the irradiation dose, mainly due to the transitions of the polaron/bipolarons, which were created by the  
Figure 6: DSC Curves of the unirr and EB-irradiated PLC nanocomposites.  
Figure 7: UV-Visible spectra of the PLC nanocomposites and EB irradiated at 40 kGy, 80 kGy, and 120 kGy doses.  
high radiation energy [38]. The observed results confirm that the optical properties of the polymer composite can be tuned by the EB irradiation.
## 4 Discussion
Regarding image quality, it has to be highlighted that the image resolution was adjusted as a compromise between image size and quality. In any case, it will be further evaluated in future versions of the application to increase image quality. Regarding audio volume, this is an issue that can be improved by modifying the characteristics of the visit (including the use of headphones, for example) instead of making changes in the AR application. The results from this study support conclusions from previous studies that indicate that AR is a technology that can provide a new and authentic learning experience in museums [37]. The technology is especially attractive to younger visitors [56; 57; 58], such as the high school students that participated in the present experience. Other studies also remarked on the additional advantages of AR experience, as it stimulates learning motivation and makes the participants more prone to visit other museums with AR [59]. It also generates more engagement during the visit [60; 61]  
Regarding the VR application, it is thought of as a complement to the AR experience. It was developed as a virtual reproduction of the museum visit that can be experienced from any location, thus contributing to a higher diffusion of the museum contents. The main limitation of the present study is that the validation of the AR system and visit was performed only with high school students. Future validations should be performed with other kinds of visitors to evaluate differences and allow the personalization of the application depending on the user's characteristics. The user experience during the VR visit should also be evaluated in future studies. Furthermore, it can be hypothesized that the users' experience associated to the AR-based visit will differ between different cultures [1], influenced by different factors, such as the sensitivity to time and space, which is affected by the cultural context. Future studies will have to evaluate if the AR-based visit generates a different experience in different cultures. This study can be easily organized in university settings, taking advantage of the mobility programs that allow the presence of different cultures in the university campus. Independently of these future studies about the cultural issues, the use of AR-based visits can be easily extrapolated to other types of technological museums, and also to ethnographic ones. AR can show additional information about the artifacts and their functionality while preserving the original artifacts, and this is especially relevant in this kind of experience. Another aspect that should be considered in future validations will be the point of view of the museum professionals. Although the museum curator participated in all the stages of the design, development, and validation of the developed applications, the opinion of independent museum professionals should also be evaluated and their recommendations should be considered when designing future applications or improving the current ones [62].