esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Seasonal Variability in the CI  
The results of this study indicate that the seasonal variability in the CI data has the potential to reflect the change in vegetation status, which confirms the results of previous studies [30; 47; 70; 99; 100]. While the variation in the CI is closely related to the LAI, which exhibits low CI values in the growing season and high values in the dormant period, the tree cover, canopy arrangement, understory vegetation, and background reflectance all contribute to the seasonal variation in the CI [7; 30; 47]. Compared with 8-day and daily CIs, the monthly CIs reduce the influence of poor-quality data and ephemeral background reflectance changes that can provide relatively reasonable surface CIs for ecological and meteorological modeling at global and regional scales.  
Moreover, the spatial and temporal mismatch of field-measured CIs and retrieved MODIS CIs most likely lead to inconsistencies in the evaluation of seasonal variabilities. As seen in 7 sites with seasonal profiles of field-measured CIs, compared to field-measured CI data, satellite-derived CIs with larger spatial extents are more susceptible to changes in the surrounding and background reflectance in theory [47]. In addition, satellite data mainly respond to the top structure of vegetation, while the field-measured CIs are probably influenced by understory vegetation. Field-measured CIs influenced by understory vegetation may overestimate the forest CIs, as the CIs of shrubs are usually higher than those of trees [25; 99]. These issues propose a huge challenge for the validation of satellite CI retrievals using field CI measurements.  
The different temporal scales of the C5 8-day CIs, C6 daily Cis, and field-measured CIs should be considered sources of uncertainty in the comparison of seasonal variability. The temporal resolutions of the C5 8-day and C6 daily CI data are the same as those of the corresponding BRDF products. Both the C5 and C6 BRDF products are produced by using observations accumulated in a 16-day period, while the C5 algorithm retrieves data every 8 days with the first day being the day of interest, and the C6 algorithm retrieves data every day with the central day being the day of interest [35]. These temporal differences between the C5 CIs, C6 CIs, and field CIs may introduce temporal bias in the comparison of CI seasonal variations.
## 3 Cascaded Weighted Sum
### The Effect of the CWS on the Search  
The effect of the cascaded assessment on population-based search procedures like EAs, PSO or ACO is illustrated in Figure 6 for two objectives and the example of the feasible objective region used in Figure 2. Based on previous knowledge, the sources of which are discussed in Section 4.3, a region of interest is defined for every objective group and the weights are set accordingly. Additionally, threshold values \(\varepsilon_{i}\) are defined for all objectives but those of the group with the lowest priority. Care must be taken for the accessibility of the region of interest being not affected by these thresholds. In the example of Figure 6, objective two has a higher priority than objective one and a threshold value \(\varepsilon_{2}\). In the beginning of the search, a quality gain can be achieved for upward moves only. For those solutions that have surpassed \(\varepsilon_{2}\), the result of the first objective is added according to the weights changing the average movement direction towards the tangent and the region of interest. If the search runs long enough to come more or less close to convergence, most solutions will be found in the region of interest. The best of them will be at the intersection of the tangent and the Pareto front or at least close to it. Especially for EAs, which preserve genotypic diversity to some extent, good but suboptimal solutions close to the best one covering at least parts of the area of interest are very likely to be found. This means that a run is stopped when stagnation occurs over a longer period of time and not when the entire population has (nearly) converged.  
An example of a Pareto front with a non-convex section is shown in Figure 7 using the objective region and the threshold value of Figure 5. As the part between **F2** and the rightmost end of the Pareto front is quasi excluded, it is possible now to obtain solutions in the marked area of interest. This would not be the case for the original weighted sum. On the other hand, if the region of interest was located between the magenta dot and **F2**, most of the Pareto front would still be missed.  
Figure 7: Cascaded weighted sum and region of interest for the example with a non-convex Pareto front given in Figure 5.  
Figure 6: Cascaded weighted sum for \(k=2\) and objective two having a higher priority than objective one. Thus, solutions in the hatched area are bettered according to \(f_{2}\) only and will find the largest quality gain in upward moves (**red arrow**). This changes, if \(\varepsilon_{2}\) is exceeded and \(f_{1}\) starts to contribute to the resulting sum, as shown by the black arrows.  
Normalization according to Equations (3) or (4) is done linearly with the same ratio for the entire interval [min(\(f_{i}\)), max(\(f_{i}\))]. If previous knowledge is available for defining an area of interest for the Pareto front, corresponding subintervals are also known for the single objectives. This information can be used to tune the normalization function, as is shown exemplarily in Figure 8. More normalization functions can be found in [15].
# A Risk Assessment Framework for Mobile Apps in Mobile Cloud Computing Environments
## 3 Results  
### Risk Assessment Framework  
The proposed risk assessment framework is shown in Figure 1. It includes an initial preparatory stage and an operational stage. At the initial stage, the ensemble ML classifier is trained and tested on input data that includes apps' permission and intent usage indicators using a suitably constructed dataset. The trained classifier resides in the mobile cloud. The dangerous permissions used in calculating apps' risk scores are also selected at the preparatory stage, and the respective dangerous permission risk scores are calculated using a probabilistic approach (as discussed in Section 3.3). In preparation for the operational stage, the ensemble ML classifier is downloaded and installed on the user's MD along with the risk scores of the selected dangerous permissions and the software needed for the app risk score calculation and app risk evaluation.  
At the operational stage, the app risk evaluator extracts the permissions and the intents of the apps that are resident on the MD and feeds them to the trained ensemble ML classifier. The classifier checks each resident app and classifies it as either benign or malicious, while the app risk evaluator applies the risk function to calculate the app's risk score. For each app, the risk function considers the permissions that belong to the set of the already selected dangerous permissions. Finally, the app risk evaluator assigns a risk category to each app.  
The app risk evaluator aims to provide an accurate risk assessment at all times. Whenever a resident app is updated, the app risk evaluator automatically scans and re-evaluates the updated app because of the possibility of new permissions or intents to have been added to the new version. Furthermore, when the cloud ML model is retrained on a new dataset, the updated version is pushed to the MD and the app risk evaluator re-evaluates the resident apps.  
Figure 1: App risk assessment framework.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 3 Unmanned Aerial Vehicles (UAVs)
### UAV Swarms  
As illustrated in Figure 5, UAV swarms can be classified as semi- or fully autonomous. This classification can also be categorized as single-layered and multi-layered swarms. In single-layered swarms, each UAV is a leader drone. Each layer of a multi-layered UAV swarm is occupied by a dedicated layer of drones, which operate and report to specific leader drones at each layer. In every swarm, each drone has data collection and processing capabilities in real-time, while the central processing occurs at the cloud or base station. The swarms of UAVs with intelligent monitoring mechanisms can reliably and quickly cover an intended area by using different parallel operating drones. Several research studies have focused on the role of UAV swarms. For example, ref. [49] considers swarms of drones to address the charging mechanism of several drones in parallel. It features UAV swarms for smart energy management. Some studies have suggested prioritization algorithms in case of swarms of UAVs, where UAVs with high priority will be recharged first as compared to UAVs with low priority. Swarms of UAVs also impact the hovering performance. UAV flight synchronization and hovering stability are essential when we consider the mission execution of swarms of UAVs such as quadcopters. In addition, communication reliability of the quadcopter swarm is also crucial for a successful mission. The authors in ref. [50] proposed an efficient multi-robot coordination method to address the issue of real-time UAV swarm coordination on a broad area network. Swarms of UAVs have been studied in the past for surveillance purposes. For instance, the authors in ref. [51] describe quadcopter swarms for object tracking and localization.  
Figure 4: (**a**) fixed-wing, (**b**) fixed-wing hybrid, (**c**) single rotor, and (**d**) multirotor UAV.  
Figure 5: Classification of swarms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Types of UAVs** & **Key Features** \\ \hline Fixed-Wing & High speed, long endurance \\ Fixed-Wing Hybrid & Long endurance, VTOL \\ Single Rotor & Long endurance, hovering, VTOL \\ Multirotor & Short endurance, hovering, VTOL \\ \hline \hline \end{tabular}
\end{table}
Table 3: Key features of different categories of UAVs.
## 2 Methodology
### Environmental Analysis  
Several tools and calculation models to determine the environmental impact of the construction sector are in place; some are multi-variable, such as energy [36; 37], the ecological footprint [38; 39] or Eco-indicator, while others use single indicators, such as EE, WF, and CF [40; 41; 42; 43].  
The CF indicator is commonly used in construction assessment and some also employ the projects' bill of quantities [26; 38; 39]. In recent years, advances have been made in defining the ranges of CO\({}_{2}\) generated in the production of construction products [44]. The other two indicators proposed, due to their simple message, are the EE [45] and the WF [28; 46]. The EE (embodied energy) represents the energy efficiency of the production of products, which is independent from the CF, provides added information, and has commonly been used in construction assessment, for which its direct and indirect impact has been calculated [47]. The WF, disseminated by the Water Footprint Network [48], is another interesting indicator which determines the amount consumed in the production of goods by following The Standard Calculation Methodology [49] and The Water Footprint Assessment Manual [50]. The combination of the three indicators, WF, CF and EE, have given interesting comparative results in the previous work by the authors [30] by employing Building Information Modelling [31].  
The CF, WF, and EE indicators are quantified by using international LCA databases of construction products [51] and by using the EPDs available on ECO-Platform (www. eco-platform.org/, accessed on 1 February 2021), the European platform for EPD programs in the construction industry [52]. The consumption of natural resources on site is treated as an environmental cost. Direct costs in construction budgets correspond to machinery, labor, and materials, and similarly cause the direct use of resources on site through the energy expenditure of the machinery (fuel or electricity), the impact of labor (through the quantification of working hours), and the consumption of construction materials (during manufacture, transport, and commissioning).
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 4 Methods
### Feature Extraction  
Selected features were calculated to a signal for a window size of 10 signal samples with 50% overlap. The features were calculated for a pre-prepared EEG signal, as well as for the minimum-phase signal. The individual features extracted from the signal \(x(n)\) are listed in the next lines [20].  
Figure 2: Protocol for electroencephalography (EEG) signal acquisition. On-screen presentation times and average speech times. Individual colours or colour names were displayed with breaks created by black colour on screen.  
* _Mean_ value of the frame is an average value of signal parties \[\mu=\frac{1}{N}\sum_{n=1}^{N}x(n).\] (3)
* _Standard deviation_ \[\sigma=\sqrt{\frac{1}{N-1}\sum_{n=1}^{N}(x(n)-\mu)^{2}}.\] (4)
* _Skewness_ indicates the measured the asymmetry of this distribution around its mean value. The skewness is defined for a signal \(x(n)\) as: \[Skew=\frac{1}{N\sigma^{3}}\sum_{n=1}^{N}(x(n)-\mu)^{3},\] (5) where \(\mu\) and \(\sigma\) are the mean and standard deviation of the signal \(x(n)\). Distribution of EEG signal is symmetric if it looks equal on the right and left sides of the centre [21].
* _Kurtosis_ is the fourth-order central moment of distribution. EEG signals coefficients have not normal distribution and have heavy-tailed characteristic justified by kurtosis [21]. The kurtosis is defined for a signal \(x(n)\) as: \[Kurt=\frac{1}{N\sigma^{4}}\sum_{n=1}^{N}(x(n)-\mu)^{4}.\] (6) Kurtosis characterises comparison of relative peakness distribution and normal distribution. Positive kurtosis coefficients indicate peaked distribution and the low or negative coefficients mean a flat envelope [22].
* _Energy_ indicate the strength of the EEG signal. Energy used for our signal was calculated as the sum of all the squared values in the window. The average energy is define by: \[Energy=\frac{1}{N}\sum_{n=1}^{N}x(n)^{2}.\] (7)
* _Bandpower_ is a method of monitoring amplitude modulations at the frequency range [23]. \[P=\lim_{T\rightarrow\infty}\frac{1}{2T}\int_{-T}^{T}x(t)^{2}dt.\] (8)
* _Root mean square_ \[RMS=\sqrt{\frac{1}{N}\sum_{n=1}^{N}x(n)^{2}}.\] (9)
* _Shannon entropy_ of a signal is a measure of the spectral distribution of the signal. The Shannon entropy is measure of uncertainty in a random process [24]. It is defined as: \[Shannon=\sum_{n=1}^{N}x(n)log_{2}x(n).\] (10)
* _Spectral flux_ describes sudden changes in the frequency energy distribution.It indicates the rate of change of the power spectrum. Spectral flux is given by formula: \[Spectralflux=\sum_{k=1}^{M/2}(S_{t}(k)-S_{t-1}(k))^{2},\] (11)where \(S_{t}(k)\) and \(S_{t-1}(k)\) are the Fourier transforms of actual frame \(t\) and pervous frame \(t-1\) of the signal [25; 26].
## Materials and Methods
### Vacuum Impregnation (VI)  
Vacuum impregnation was performed in a prototype plant located at the Institute of Agricultural Engineering, Wroclaw University of Life Sciences (Wroclaw, Poland) [20]. The vacuum (VI) impregnation process was carried out at a reduced pressure of 6 kPa and lasted 21 min. A perforated stainless steel vessel with 100 g samples was placed in the impregnation chamber and subjected to reduced pressure for 2 min. Then 1000 mL of impregnating solution was added, and the material was kept in the chamber for 4 min. After restoring atmospheric pressure, the material was kept in the chamber for 15 min. The material was drained using filter paper. As an impregnating solution, organic, cold-pressed red beet juice was used at a concentration of 10.1\({}^{\circ}\) Brix (Haus Rabenhorst, Unkel, Germany).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Code** & **Material** & **Type of Drying** \\ \hline C0 & Courgette & - \\ C0\_FD & Courgette & Freeze drying \\ C0\_VD & Courgette & Vacuum drying \\ CB & Courgette after impregnation with beetroot juice & - \\ CB\_FD & Courgette after impregnation with beetroot juice & Freeze drying \\ CB\_VD & Courgette after impregnation with beetroot juice & Vacuum drying \\ B0 & Broccoli & - \\ B0\_FD & Broccoli & Freeze drying \\ B0\_VD & Broccoli & Vacuum drying \\ BB & Broccoli after impregnation with beetroot juice & \\ BB\_FD & Broccoli after impregnation with beetroot juice & Freeze drying \\ BB\_VD & Broccoli after impregnation with beetroot juice & Vacuum drying \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fresh and dried materials samples codes.  
Figure 1: Control sample: fresh broccoli (B), dried broccoli by vacuum drying (VD), freeze drying (FD), broccoli after VI with beetroot juice (BB).  
Figure 2: Control sample: fresh courgette (C), dried courgette by vacuum drying (VD), freeze drying (FD), courgette after VI with beetroot juice (CB).
## 4 Results
### Cognitive Load and Emotion Correlation  
We further explored the relationship between cognitive load and emotional response to understand how workload influenced users' emotions. No meaningful correlation between positive emotion and NASA-TLX scores (workload, frustration, and others) was identified (\(\rho=-0.049,p=0.267\)). Correlation estimates for individual tools also showed no significant correlation between the workload and positive emotions for Taguette (\(\rho=-0.041\), \(p>0.05\)), ChatGPT (\(\rho=-0.059\), \(p>0.05\)), and Gemini (\(\rho=-0.106,p>0.05\)). However, a statistically significant moderate to strong positive correlation existed between negative emotion and NASA-TLX (\(\rho=0.584,p<0.001\)). This result suggests that higher cognitive demands, effort, and frustration are associated with increased negative emotions. QDA tools that lead to higher cognitive demands and effort tend to evoke more negative emotions in users.  
There were significant correlations between NASA-RTLX scores and negative emotions such as nervousness (\(\rho=0.58,p<0.001\)), upset (\(\rho=0.485,p<0.001\)), hostility (\(\rho=0.420\), \(p<0.001\)), and guilt (\(\rho=0.353,p<0.001\)). These correlations suggest that as users' workload and effort increase, they tend to report higher negative emotional states, such as feeling more nervous, upset, or hostile. There were weak and primarily negative correlations between positive emotions, such as determination (\(\rho=-0.212,p<0.001\)) and inspiration (\(\rho=-0.089,p<0.05\)). These results indicate that a higher workload is associated with lower positive emotions like determination and inspiration, although the correlations are weaker than those with negative emotions. The analysis reveals a clear emotional response associated with perceived workload, particularly for negative emotions like nervousness, frustration, hostility, and guilt, which increase with higher workload scores. On the other hand, emotions like determination and inspiration decrease with increased workload, showing that cognitive and emotional load affect the user's emotional experience. While some emotions, like alertness and attention, are less affected, the overall trend suggests that workload negatively impacts users' emotional state.
## 4 Intractable Problems on a (Unit) Grid Intersection Graph
### The Graph Isomorphism Problem  
**Theorem 13**: _The graph isomorphism problem is \(GI\)-complete for grid intersection graphs._  
Proof. We show a similar reduction in [38, 54]. We start by considering the graph isomorphism problem for general graphs. Let \(G_{0}=(V_{0},E_{0})\) and \(G_{0}^{\prime}=(V_{0}^{\prime},E_{0}^{\prime})\) with \(|V_{0}|=|V_{0}^{\prime}|=n\) and \(|E_{0}|=|E_{0}^{\prime}|=m\) be an instance of the graph isomorphism problem. (We will refer the graph \(G_{0}\) in Figure 9(1) as an example). Without loss of generality, we assume that \(G_{0}\) is connected. From \(G_{0}\), we define a bipartite graph \(G_{1}=(V_{0},E_{0},E_{1})\) with two vertex sets \(V_{0}\) and \(E_{0}\) by \(E_{1}:=\{\{v,e\}\mid v\mbox{ is one endpoint of }e\}\). (Intuitively, each edge is divided into two edges joined by a new vertex; see Figure 9(2)). Then, \(e\in E_{0}\) have degree 2 by its two endpoints in \(V_{0}\). It is easy to see that \(G_{0}\sim G_{0}^{\prime}\) if and only if \(G_{1}\sim G_{1}^{\prime}\) for any graphs \(G_{0}\) and \(G_{0}^{\prime}\) with resultant graphs \(G_{1}\) and \(G_{1}^{\prime}\).  
Figure 8: Hamiltonian path problem.  
Figure 9: Reduction for \(GI\)-completeness.  
Now, we construct a grid intersection graph \(G_{2}=(V_{2},E_{2})\) from the bipartite graph \(G_{1}=(V_{0},E_{0},E_{1})\) such that \(G_{1}\sim G_{1}^{\prime}\) if and only if \(G_{2}\sim G_{2}^{\prime}\) in the same manner. The vertex set \(V_{2}\) consists of the following sets (see Figure 9(3)):  
\(V_{0}\)**, \(E_{0}\);**: we let \(V_{0}=\{v_{1},v_{2},\ldots,v_{n}\}\), \(E_{0}=\{e_{1},e_{2},\ldots,e_{m}\}\), where \(e_{i}=\{v_{i},v_{j}\}\) for some \(1\leq i,j\leq n\). \(P_{v},Q_{e}\)**:**: each vertex in \(P_{v}\cup Q_{e}\) is called _pendant_ and \(P_{v}:=\{p_{1},p_{2},\ldots,p_{n}\}\), \(Q_{e}:=\{q_{1},q_{2},\ldots,q_{m},q_{1}^{\prime},q_{2}^{\prime},\ldots,q_{m}^{ \prime}\}\). That is, we have \(|P_{v}|=n\) and \(|Q_{e}|=2m\). \(C_{a},C_{b},C_{c},C_{d}\)**:**: each vertex in \(C_{a}\cup C_{b}\cup C_{c}\cup C_{d}\) is called _connector_, and \(C_{a}:=\{a_{1},a_{2},\ldots,a_{m}\}\), \(C_{b}:=\{b_{1},b_{2},\ldots,b_{m}\}\), \(C_{c}:=\{c_{1},c_{2},\ldots,c_{m}\}\), and \(C_{d}:=\{d_{1},d_{2},\ldots,d_{m}\}\).  
The edge set \(E_{2}\) contains the following edges (Figure 9(3)):  
1. For each \(i\) with \(1\leq i\leq n\), each pendant \(p_{i}\) is joined to \(v_{i}\). That is, \(\{p_{i},v_{i}\}\in E_{2}\) for each \(i\) with \(1\leq i\leq n\).
2. For each \(j\) with \(1\leq j\leq m\), two pendants \(q_{j}\) and \(q_{j}^{\prime}\) are joined to \(e_{j}\). That is, \(\{q_{j},e_{j}\},\{q_{j}^{\prime},e_{j}\}\in E_{2}\) for each \(j\) with \(1\leq j\leq m\).
3. For each \(e_{j}\) with \(1\leq j\leq m\), we have two vertices \(v_{i}\) and \(v_{i^{\prime}}\) with \(\{v_{i},e_{j}\},\{v_{i^{\prime}},e_{j}\}\in E_{1}\). For the three vertices \(e_{j}\), \Hence the graph isomorphism problem for grid intersection graphs is as hard as the graph isomorphism problem for general graphs. Thus the graph isomorphism problem is \(GI\)-complete for grid intersection graphs.
## 3 Key Component Update
### Selection of Corrosion-Resistant Materials
SiC and WC performed exceptionally well, with WC exhibiting a corrosion rate of 0.0239 mm/a, making it suitable for long-term use in phase transition environments. However, Al\({}_{2}\)O\({}_{3}\), being an amphoteric oxide, reacts with strong acids and bases, resulting in instability in industrial applications. Although WC is an ideal benchmark material, its scarcity, high cost, and processing challenges limit its current adoption. Therefore, SiC or ceramic-lined carbon steel heat exchange tubes were temporarily chosen as materials for sulfuric acid phase transition processes.