esa-sceva/satcom-chunk-collection huggingface dataset:
# From 5G to 6G--Challenges, Technologies, and Applications
## 3 Sixth-Generation Network's Aspects
### Sixth-Generation Network's Footprint
#### 3.1.2 Technical Impact  
The future of the digital world looks brighter than ever, thanks to the technical advancements that have increased exponentially over the last 30 years and are not showing any signs of slowing down. The sixth-generation network is expected to offer the most sophisticated technologies up to date. We highlight the most prominent emerging technologies in the subsequent parts of the paper. However, we demonstrate here a few examples of fundamental changes in the digital world. The introduction of the first binary-based computer in 1938 [32] started a line of technology that continues to this day with integrated circuits (ICs) able to perform billions of tasks in less than a second. In the near future, a new concept of computer computations will emerge, i.e., the Q-bit, based on quantum mechanics. In very simple terms, this concept suggests looking into the electron's state in wires to determine the encoded data by the transmitter [33]. This type of computation is expected to revolutionize the digital world and open the door to achieving unprecedented performance metrics, which can enable new services on the network. Another example worth looking at is integrating AI into the global network. AI is changing how end devices perceive communication networks by introducing many concepts, such as network self-sustainability/management and autonomous systems in factories, vehicles, and many other setups. AI is expected to be at the heart of many 6G services and technologies, which are expected to be so advanced that humans will not have to intervene with the work of the network at all. AI, at the highest levels, will be able to analyze human sentiments for various purposes, such as better selection of online content and advertisements to be delivered to the individual users based on their facial feedback [34] and offer a better user experience during human-bot chats, which are becoming more common. Similar to the impact of AI on future networks, VR is the cornerstone of many current and future network services that is foreseen to change in principle how humans perceive their surroundings and interact with each other. For instance, VR has been used in medical staff training and treatment of patients remotely during the COVID-19 pandemic, such as by performing VR-based physical or cognitive rehabilitation and telehealth services [35; 36].
# Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
## 5 Results
### Large-Scale Classification  
In this first analysis we concentrate on the results obtained by applying the proposed algorithm to the large-scale area shown in Figure 4, where a comparison between the REF reference map and case (_SADH_) for the four swaths acquired with orbit number 83 is presented. The performance analysis is carried out using both the overall and average accuracy parameters denoted as OA and AA, respectively, and Table 4 summarizes the two metrics for each of the four stacks, considering the set of inputs in case (_ORIG_) and in case (_SADH_).  
Figure 4: Comparison between the REF reference (**left**), with in yellow the clearcuts (CUT) marked by PRODES between 2017 and 2019, and the final result of the Random Forests algorithm using SADH textures (**right**). The white square identifies a region of interest in which the maps are clearly different. Such an area corresponds to the Pacaás Novos National Park and is separately analyzed in Figure 9.
## 2 Materials and Methods
### Data Collection, Connection, Filtering, and Pre-Processing  
After the data collection, the data were filtered. Only data with a collection date after the year 2000 positioned in the contiguous United States (Figure 2) were included. The cut-off date was selected to ensure that the periods of the different selected datasets would coincide. In addition, only samples that were collected from streams were included. This resulted in differing numbers of samples per metal. The highest number of samples was obtained for Al, with 2927 data entries.  
Utilizing the PyGeoHydro library in Python, the closest upstream stream station for each sediment sample was selected from the NHDPlus, and selected attributes for the associated basin above the station were extracted. The minimum, maximum, mean, and median statistics for the NDVI and EVI data and the SRTM digital elevation data were extracted and stored for the same basin. The number of dams of different construction types  
Figure 1: The workflow of the presented study begins with the several data input sets and moves along the spiral until reaching the results.  
Figure 2: The basin areas for which factor data were collected and the positions of the sediment samples in the contiguous United States.  
and attributes, such as length and height of the dam and area and volume of the reservoir, were accumulated per dam type for each basin. The whole data acquisition process resulted in 1684 attributes per sediment sample dataset, describing unique combinations of geology, land cover, soil, climate, and human impact. The Al samples, for example, were associated with 2692 different drainage areas of different sizes and different physical setups. The subsequent statistical analysis was performed in the statistical programming language R (version 4.0.5, R Core Team, Vienna, Austria) in the RStudio environment (version 1.4.1106, RStudio, Boston, MA, USA).
## 4 Conclusions  
The gluten-free composite flour consisting of a mixture of rice and quinoa flour was used as the basis for supplementation with a 10% soy protein isolate of hydrolysate obtained with bromelain, Neutrase or trypsin. Empirical rheological measurements were employed to determine the thermo-mechanical behavior of the gluten-free doughs. The flour mixtures enriched with soluble soy peptide fractions, with a molecular weight lower than 20 kDa, exhibited delayed starch gelatinization. The addition of the insoluble soy peptide fraction resulted on the other hand in a dough consistency increase. Based on the  
Figure 5: The antioxidant activity of the muffin samples (M—control muffin; M-SPI—muffins enriched with soy protein hydrolase produced with bromelain; M-hSPI-N—muffins enriched with soy protein hydrolysate produced with Neutrase; M-hSPI-T—muffins enriched with soy protein hydrolysate produced with trypsin). The results of the DPPH- and ABTS-based assays are represented with light and dark gray, respectively. Different superscript letters associated with mean antioxidant activity values determined with the same method indicate significant differences at \(p<0.05\).  
thermo-mechanical behavior, it was concluded that, among all tested formulations, the most important improvements in the dough behavior were observed in the case of supplementing the gluten-free flour with 10% soy protein hydrolysates obtained with bromelain and trypsin. The gluten-free muffins' supplementation with soy protein hydrolysate resulted in a more vivid color of the crumb and no important changes in terms of texture, but the specific volume was significantly lower compared to the sample with soy protein isolate. The antioxidant activity of the gluten-free muffins enriched with soy protein hydrolysate was significantly higher compared to the control. In conclusion, soy protein hydrolysate can be successfully used to obtain baked gluten-free products with an improved nutritional and bioactive profile.  
Conceptualization, LA; methodology, I.B., I.V. and L.G.-G.; software, L.A., L.D. and I.B.; validation, M.B., L.G.-G., L.D. and I.V.; formal analysis, LA.; investigation, M.B., L.G.-G., I.V., I.B., L.D. and I.A.; resources, LA. and L.G.-G.; data curation, L.G.-G., I.V. and LA; writing--original draft preparation, I.B., L.G.-G., I.V., M.B. and I.A.; writing--review and editing, L.D. and I.A.; visualization, I.A. and L.G.-G.; supervision, I.A.; project administration, I.A.; funding acquisition, I.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
[PERSON] from Merck Romania is acknowledged for technical support regarding the protein electrophoresis analysis using the mPage Lux system. The Integrated Center for Research, Expertise and Technological Transfer in Food Industry is acknowledged for providing technical support.  
The authors declare no conflicts of interest.
## 3 Results  
### Structural Properties and Stability  
The calculated ground-state structural configuration of pristine MXene MnCrNO\({}_{2}\) with fully relaxed geometry is discovered to have hexagonal symmetry and to belong to the \(P3m1\) space group (No. 156). The side and top views of the structure are shown in Figure 1a, in which the MnCrNO\({}_{2}\) monolayer takes a fivefold layered structure, i.e., O-Mn-N-Cr-O. The primitive cell contains one Mn, one Cr, one N, and two O atoms, where the Mn atoms, the Cr atoms, and the sandwiched N atoms form a trigonal lattice, as shown in the top view. Structurally, the MnCrNO\({}_{2}\) monolayer is thus similar to the Mn\({}_{2}\)NO\({}_{2}\) or Cr\({}_{2}\)NO\({}_{2}\) monolayer [6; 46]. The optimized lattice parameter of the monolayer MnCrNO\({}_{2}\) is \(a\) = 2.97 A. The other relevant parameters with regard to the bond angles and bond lengths are listed in Table 1.  
To inspect its stability, we also calculate the phonon spectrum of the monolayer MnCrNO\({}_{2}\), as shown in Figure 1b. The phonon dispersion contains tiny imaginary frequencies resulting from common numerical simulation instabilities, but these imaginary frequencies only appear in a small region around the \(\Gamma\) point; they are related to long-wave fluctuations, occupying 0.4% of the total phonon density of states (DOS), and can be reasonably ignored. Such cases were also reported in other 2D materials [47; 48]. Therefore, the MnCrNO\({}_{2}\) monolayer is thermally stable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bond** & **Length (Å)** & **Bond** & **Angle (\({}^{\circ}\))** \\ \hline O-Cr & 1.932 & O-Cr-N & 87.004 \\ Cr-N & 2.228 & Cr-N-Mn & 91.610 \\ N-Mn & 2.058 & N-Mn-Mn & 84.047 \\ Mn-O & 1.956 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The fully optimized bond angles and bond lengths for the Janus monolayer MnCrNO\({}_{2}\).  
Figure 1: (**a**) Top and side view of the Janus monolayer MnCrNO\({}_{2}\). Purple, blue, grey, and red spheres represent Mn, Cr, N, and O atoms, respectively. (**b**) Phonon spectrum of the monolayer MnCrNO\({}_{2}\).
## 2 System Structure and Problem Formulation
### Problem Formulation  
Different from relevant relay selection schemes, this paper considers the switching loss when switching between the different relay nodes, which is a realistic consideration. For convenience of representation, we assume that the duration of each time slot was normalized to 1. We define \(\tau\) as the switching loss when switching between neighboring relays. It is also assumed that the switching process is operated at a constant angular speed. That is to say, the switching loss \(\tau_{i,j}\) between the \(i\)-th relay node and the \(j\)-th relay node \((1\leq i,j\leq N)\) is calculated by \(\tau_{i,j}=|i-j|\cdot\tau\). Apparently, the switching loss from the \(i\)-th relay to the \(j\)-th relay will be equal to the switching loss from the \(j\)-th relay to the \(i\)-th relay. In addition, the switching time is equal to zero when the same relay node is selected in both current and previous TS.  
The S-R link and the R-D link cannot transmit data before the switching process is completed. In other words, the switching process will reduce the available time of communication, which limits the channel capacity. As a result, considering the switching loss, the channel capacity of the \(k\)-th TS was modified in Equation (3).  
Figure 2: System structure of a DF-FSO system.  
\[\mathbb{C}_{k}=\big{(}1-\tau_{N_{k},N_{k-1}}\big{)}\cdot\min\bigg{\{}\log_{2} \bigg{(}1+\Big{(}\eta Ph_{k}^{N_{k}}\Big{)}^{2}/\sigma_{r}^{2}\bigg{)},\log_{2} \bigg{(}1+\Big{(}\eta Pg_{k}^{N_{k}}\Big{)}^{2}/\sigma_{d}^{2}\bigg{)}\bigg{\}} \tag{3}\]  
This paper focused on maximizing the average channel capacity over \(M\) time slots, as shown in Equation (4):  
\[\begin{array}{l}\max_{\{\Omega_{M}\}}\frac{1}{M}\sum\limits_{k=0}^{M-1} \mathbb{C}_{k}=\max\bigg{\{}\big{(}1-\tau_{N_{k},N_{k-1}}\big{)}\cdot\min\bigg{\{} \log_{2}\bigg{(}1+\Big{(}\eta Ph_{k}^{N_{k}}\Big{)}^{2}/\sigma_{r}^{2}\bigg{)},\log_{2}\bigg{(}1+\Big{(}\eta Pg_{k}^{N_{k}}\Big{)}^{2}/\sigma_{d}^{2}\bigg{)} \bigg{\}}\bigg{\}}\\ \text{s.t. }1\leq N_{0},N_{1},\cdots,N_{M-1}\leq N\end{array} \tag{4}\]  
where \(\Omega_{M}\) stands for the sets of the selected relays, i.e., \(\Omega_{M}=\{N_{0},N_{1},\cdots,N_{M-1}\}\).  
Obviously, this exhaustive method can solve this problem if the CSI of all slots are available. However, this is unachievable, since the future CSI could not be known. In addition, the complexity of the exhaustive method grows exponentially as the number of TS increases, which is unacceptable. Regarding Equation (4), the system needed to select the current relay cautiously, since every choice might change the future channel capacity. As a result, we have to choose every relay by taking into account the possible impact on the future channel capacity. Fortunately, we could employ RL method, which aims at maximizing the long-term cumulative rewards.
## 3 Research Results
### Spatial Dependence Mechanism Underlying CS  
#### 3.3.1 Gradient Effect of Terrain Driving CS  
BRB is located in the area of rapid deformation; the topography is diverse, showing obvious features of a valley, loess plateau, and alpine landform (as shown in Figure 7). Figure 8 shows the distribution of CS under different elevations (DEM), gradients, topographic relief, and topographic niche index gradients.  
The variation in CS with altitudes presents a significant gradient (Figure 8): under different DEM gradients, the cumulative value of the ninth gradient (about 3000 m) is the highest, and that of the third gradient (below 2000 m) is the lowest. The CS decreases first and then increases with altitude, which is related to the frequent human activities, and the high incidence of landslide and debris flow in the low-altitude area. The forest cover area is the main area at 2130-3362 m (grade IV-IX), and the alpine meadow, bare land, and snow cover area are the main areas at grade X, and the CS is low.  
There are also significant differences in CS among different slope zones. Areas with gentle slopes are predominantly found in the northeastern, central, and southern regions of the basin. These areas are mainly used for agriculture and construction, and have been significantly impacted by human activities. When the slope increases, the suitable hydrothermal conditions and the decrease in anthropogenic influence are conducive to vegetation growth, the increase in mountain forest and grassland cover, and the corresponding increase in CS. However, when the slope reaches grade VI, vegetation decreases, and soil and water conservation function declines, resulting in a slight decline in CS. The spatial distribution of topographic relief and slope is similar, so the variation characteristics of CS on topographic relief gradient are basically the same as that of the slope gradient.  
Figure 7: Terrain feature map of BRB.  
The variation in the topographic niche index further revealed the distribution characteristics of CS. The regions with a high topographic niche index (grade X) are mainly concentrated in the south of Diepe county, the east of Zhouqu, and the west of Wenxian. The low-value areas (grade I-III) are primarily found in the southern part of Wenxian County, as well as in the northern regions of Dangchang and Wudu District, mostly agricultural and urban areas. The median area (grade IV-IX) is dominated by woodland with less human disturbance, and the fluctuation of CS increased with the topographical niche index.  
Overall, with the increase in altitude and slope, forest and grassland gradually become the main types of LU, and CS peaks between 2500 and 3300 m above sea level, while areas with a slope of 27-40\({}^{\circ}\) and relief degree of 700-1000 are the most suitable for vegetation growth, which contributes to the accumulation of soil organic carbon.
## 2 Objective Evaluation Test
### Phase Test  
Criterion: The surround channels of the stereo-to-multichannel upmix should have a certain correlation to prevent uncomfortable perception. If the surround channels would be completely correlated, a mono sound source would be created, which could be perceived as uncomfortable. If the surround channels would be completely decorrelated, two independent sound sources would be created, which could be perceived as uncomfortable, too [15; 16; 17; 18].  
The procedure of the phase test is shown in Figure 10. The test signal is identically equal to the test signal of the volume test and is used as input signal for the tested upmix.  
The correlation degree \(\Upsilon_{SLSR}\) results from the normalized cross-correlation of the generated upmix output signals \(x_{SL}\) and \(x_{SR}\) for \(\tau=0\), and leads with  
\[\Upsilon_{\varepsilon}(n)=\left\{\begin{array}{l}\dfrac{|\Upsilon_{SLSR}(n) |-\varepsilon_{o}}{1-\varepsilon_{o}},|\Upsilon_{SLSR}(n)|>\varepsilon_{o}\\ \varepsilon_{u}-|\Upsilon_{SLSR}(n)|_{\tau}|\Upsilon_{SLSR}(n)|<\varepsilon_{u }\\ 0,\varepsilon_{u}\leq|\Upsilon_{SLSR}(n)|\leq\varepsilon_{o}\end{array}\right. \tag{11}\]  
to the evaluation score of the phase test \(score_{PhT}\) according to  
\[score_{PhT}=1-\dfrac{1}{N}\sum_{n=1}^{N}\Upsilon_{\varepsilon}(n) \tag{12}\]  
With the requirement that the surround channels \(x_{SL}\) and \(x_{SR}\) should not be either completely correlated or completely decorrelated, two evaluation limits, \(\varepsilon_{o}=0.5\) and \(\varepsilon_{u}=0.2\), were defined within which a certain correlation is supposed to be optimal. The tendency for complete correlation and thus the creation of a mono sound source is higher weighted in the evaluation score than the tendency for complete decorrelation. The evaluation allows the comparison of the correlation degrees of the surround channels (see Figure 11).  
Upmix 1: In mode (a), the correlation degree of the surround channels is basically negative, in mode (b), basically positive. It is notable that the surround signals of the one mode are a phase-inverted version of the surround signals of the other mode.  
Upmix 2: In both modes, the correlation degree of the surround channels is basically approximately one. So, there is the danger that the correlated surround signals are decomposed into a mono signal.  
Figure 11: Correlation degree of surround channels: (**a**) Upmix 1(a) and 1(b); (**b**) upmix 2(a) and 2(b).  
Figure 10: Block diagram: Phase test. Correlation degree is calculated in the block “KOR”.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 4 Preliminaries
### Multi-tier Framework  
Figure 1 represents the multi-tier dependent clustering architecture. The nodes present in this structure are either mobile or static, which depends upon the necessity of the application. The concept of node mobility was conducted by developing the random walk (RW) and random waypoint (RWP) model.  
In this architecture, the quadrangular profile system zone is deliberated. The upper level is constructed as the BS, and the system zone is separated into several regions. Each region is distributed into sub-regions, then it is represented as a cluster. The regions are condensed in the direction of the BS, and cluster counts are enlarged in the direction of BS to steady the system load equivalently [37]. The lower level has the determined extent through the lowest clusters count, and the upper level has the lowest extent through the higher cluster count. The motive enumerated in this research is that the upper region not only transfers collected information toward the BS but also relays the traffic of lower regions. In additional confrontations, the intra-cluster distance is condensed as moving on the road to BS via cluster count through the diminishing region. The system model of WSN-IoT is explained in the following section.
## 4 Project Examples
### Experimental Analysis
#### 4.3.3 Model Comparison Experiment  
In order to verify the effectiveness of the method proposed in this paper for predicting settlement caused by underpass construction, four networks, iTransformer [22], LSTM [27], Tide [28], and DLinear [29], were selected to compare the performance of the proposed Bi-Mamba model. The input parameters and training times of iTransformer, LSTM, Tide, and DLinear are consistent with those of Bi-Mamba. The performance of the models was tested using the same test set. The comparison between the model prediction results and the actual measurement results is shown in Figures 11 and 12. The MAE and MSE comparison of each model is shown in Table 2.  
As shown in Table 2, the MSE and MAE values of Bi-Mamba at different prediction lengths are lower than those of other models. In particular, for high-precision long sequence prediction (such as 96 steps), the MSE value of Bi-Mamba is 0.279, which is 0.474, 0.327, 0.396, and 0.751 lower than LSTM, iTransformer, Tide, and DLinear, respectively; the MAE of Bi-Mamba is 0.276, which is 0.249, 0.222, 0.118, and 0.284 lower than LSTM, iTransformer, Tide, and DLinear, respectively. It can be seen that the accuracy of Bi-Mamba is significantly better than that of other models, indicating that the Mamba model can more accurately predict the MSE and MAE values of Bi-Mamba.  
Figure 12: (**a**) Comparison of Bi-Mamba predicted and measured values; (**b**) Comparison results between iTransformer predicted values and measured values; (**c**) Comparison results of LSTM predicted values and measured values; (**d**) Comparison of Tide predicted values and measured values; (**e**) Comparison results between DLinear predicted values and measured values. Comparative scatter plots of predicted versus observed values across models at a forecast length of 96.  
mine the laws of mutual influence of time-series data during the construction process in long time-series prediction, indicating that the model proposed in this paper has better fitting accuracy and prediction performance, and can more accurately predict the subgrade settlement caused by the construction of the underpass.