esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modulation-Based Adaptive Power Allocation
Because of the relationship between the EVM and the SNR, required SNRs are about a minimum of 15 dB for quadrature phase-shift keying (QPSK) and 28 dB for 256 quadrature amplitude modulation (QAM) in the transmission system. The higher the modulation order, the more signal power is required. In MFN, the signal is transmitted through the fiber from DU to RU, amplified at the RU, and then sent to each device or user over the wireless channel. The dominant noise which degrades SNR occurs mainly in the nonlinear device or wireless channel. When the device receives a signal, the device periodically measures the channel. Then, the device sends a channel quality indicator (CQI) containing the measured SNR from the device. Since the modulation format of the signal is determined by the SNR from CQI[21], that is, the transmitted signal from the DU to the RU needs only an SNR that is enough to satisfy the EVM requirements until the antenna where the signal is amplified. Therefore, in the IFoF transmission, the spectral powers of the bands are allocated based on the required SNRs according to the modulation format of each band, and the allocated signal is transmitted. If the signal power per IF is allocated by the SNR required by each modulation format, the total power of the signal can be reduced, and the power of the nonlinear component is also reduced. When the SNR required by each modulation format is allocated to each IF band, the power of the signal is adjusted, and the IMD power can also be decreased, as shown in Figure 6.  
The operating principle of the proposed modulation based adaptive power allocation (MBAPA) technique is as follows: When the initial power of all bands has the same power, the power of the signal band of 256 QAM, the highest order, is maintained. Based on the 256 QAM signal power, the excess power in each band is reduced by the required SNR difference for each order. In other words, the power of the QPSK signal is reduced by 13 dB, which is the difference from the required SNR of 256 QAM. Even if the surplus power of the signal is reduced, the required EVM can be satisfied so that the signal to which the MBAPA technology to control surplus power is applied can be received. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Format** & **QPSK** & **16 QAM** & **64 QAM** & **256 QAM** \\ \hline EVM (\%) & 17.5 & 12.5 & 8 & 4 \\ SNR (dB) & 15 & 18 & 22 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Modulation format dependent error vector magnitude (EVM) and signal-to-noise power ratio (SNR) requirements in long term evolution (LTE) standard [20]. Figure 6: Signal spectrum with the proposed technique of (**a**) transmitted signal and (**b**) received signal.
## 2 Study Area: Environmental Settings and Archaeological Background  
### Corsica  
The climate of Corsica is characterized by a steep latitudinal gradient, from the warm and dry lower elevations (average annual temperature of \(\sim\)14 to 17 \({}^{\circ}\)C) to the cold and humid higher altitudes (9-13 \({}^{\circ}\)C). The climate of North Corsica is cooler and wetter, with a mean annual temperature of 15.5 \({}^{\circ}\)C and mean annual precipitation771 mm in Bastia (North Corsica); while the climate of South Corsica iswarmer and drier (16.4 \({}^{\circ}\)C and 542 mm in Bonifacio).The broad altitudinal range of the island results in several forest zones. The lowest elevationsare characterized by the predominance of sclerophyllous evergreen oak forest (_Quercus ilex_, _Quercus suber_) and Mediterranean scrubland or maquia (_Erica arborea_, _Pistacia lenticus_, _Cistus monspeliensis_ and _Juniperus phoenicea_). At intermediate elevations, mesophylllous pine forest (_Pinus pinaster_) is widespread, and mixed deciduous forest (_Quercus pubescens_, _Quercus petraen_, _Ostya carpinifolia_, _Antus cordata_, _Castanea sativa_) is locally abundant. In North Corsica, various types of maquia occur in the surroundings of Saint Florent, including stands of wild olive (_Olea europaea_) and lentisk (_Pistacia lenticus_) in the warm coastal areas; tall maquis with _Arbutus_; evergreen oak and heather (_Calluna vulgaris_) in the cooler areas; and low maquis dominated by _Cistus_, _Rosmarinus officinalis_, and _Lavandula stoeachs_ on poor soils (sands) and in rocky environments. In South Corsica, the area of I'etang de Piantarella is occupied by Mediterranean maquis (_Cistus monspeliensis_,Juniperus phohenica, Pistacia lentiiscus, Olea europaea, Arbuttus unedo) and marshland in a more open landscape.  
The first human settlement in Corsica is marked by a sharp discontinuity between the Mesolithic and Neolithic [19]. The first attested human presence is at the onset of the Holocene, consisting of hunter-gatherer communities that did not establish permanent settlements but rather occupied caves and other shelters. Then, a gap in the population is documented during the transition from the 9 th to the 8 th millennium cal BP, suggesting that agropastoral colonisation occurred in depopulated areas, as confirmed by genetic discontinuity between these two populations [19]. The first Neolithic evidence is documented at ~7.8 cal kyr BP, followed by the rapid colonisation of the island by the first farming societies. However, there is little evidence of the early development of agriculture in archaeobotanical records in Corsica, strongly supporting, thus, the need for archaeoecological or geoarchaeological research to detect the earliest evidence of agriculture on the island [24]. From the Middle Neolithic (6.8-5.9 cal kyr BP, [25]), human settlements were concentrated in coastal regions and the nearby plains.
## 3 Results and Discussion
### Effect of Amount Deposited  
The effect of spotting detection points (in this case repeated depositions of a concentration of 5 mg/L in 0.3 \(\upmu\)L aliquots) was assessed to determine if this would improve the outcome of the method. Table 2 shows that going beyond one deposit resulted in deviation from linear regression, signified by decreasing R\({}^{2}\) values. The single, double  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Box Enclosed**} & \multicolumn{2}{c}{**Tabletop Scan**} \\ \hline \multirow{2}{*}{**Mean Gray**} & **Integrated Density** & **Mean Gray** & **Integrated Density** \\ \hline
0.772 & 0.733 & 0.967 & 0.981 \\
0.822 & 0.731 & 0.963 & 0.992 \\
0.777 & 0.772 & 0.947 & 0.982 \\
0.864 & 0.815 & 0.982 & 0.987 \\
0.787  
and triple deposits of the Fe standards per detection point yielded R2 values of 0.9984, 0.9576 and 0.8829, respectively. For a concentration of 5 mg/L, the amount of Fe in mass per deposit was 1.5 ng. Therefore, a second deposit would raise the amount of Fe to 3.0 ng and a third deposit to 4.5 ng. This explains the increase in color intensity with each additional deposit. Since the maximum of the linear range was determined as 15 mg/L (4.5 ng), adding more of this standard to the detection points increased the amount of Fe on the detection points. In atomic absorption, limitation to Beer's Law (relationship between absorbance and concentration) can be caused by high concentrations as crowded atoms/molecules/ions diminish the absorptivity of one another [6]. In this study, it is suspected that the crowding of atoms/ions diminished the color intensification.  
According to the stoichiometry of the reaction (Equation (1)) between Fe and 1,10-phenanthroline, a higher Fe amount meant more Fe moles for limited binding sides on 1,10-phenanthroline. Consequently, at some point, increased Fe amounts did not increase the ferroin complex. It is therefore not only convenient to deposit once but yields better results.  
\[\text{Fe}^{2+}(\text{aq})+3\text{phen}\rightarrow[\text{Fe(phen)}3]^{2+}( \text{aq}) \tag{1}\]
# A Fast Algorithm for VVC Intra Coding Based on the Most Probable Partition Pattern List
## 3 Proposed Method  
### Principle  
Figure 2 shows schematic diagrams of the CU partition results of the QTMT structure of the coding sequence in VVC. Upon examining Figure 2, it becomes evident that as the  
partition depth increases, the CU progressively becomes smoother, resulting in a gradual reduction in texture complexity. Additionally, the partition mode decision tends to favor partition modes that minimize the texture complexity of the sub-CUs.  
We selected five video sequences with different resolutions and texture complexities under JVET general test conditions [24], including FoodMarket4, Kimono1, BasketballPass, BQMall, and BQSquare. The relationship between the decision of the CU partition mode in the coding process and the texture complexity of the sub-CUs after different coding sequences was counted. The experimental results are shown in Figure 3. The partition mode with the largest average texture complexity of the sub-CU was recorded as Modemax, and the partition mode with the smallest average texture complexity of the sub-CU was recorded as Modemin. Texture complexity was used to describe the local texture features of the image, and the more complex the texture of the local image, the higher the texture complexity.  
Figure 3: The relationship between the optimal partition mode of VVC and the average value of the sub-CU texture complexity.  
Figure 2: The partition results under QP = 32: (**a**) BQSquare, and (**b**) BlowingBubbles.  
Based on the statistical data in Figure 3, it is not difficult to find that the VVC partition mode decision has a strong correlation with the average texture complexity of the partitioned sub-CUs. The smaller the average texture complexity of the divided sub-CUs, the greater the possibility of the partition mode being selected as the optimal partition mode, accounting for an average of 75%. The average texture complexity of the divided sub-CUs reflects the probability of the current partition mode being selected as the optimal partitioning mode, but it cannot be used as the basis for the final partition mode decision because there are still some partition modes that are selected as the optimal partition mode, even though their sub-CU average texture complexity is not the lowest.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 3 Method
### The Framework of Flame-SeaFormer  
Squeeze-Enhanced Axial Transformer (SeaFormer) [26] is a lightweight semantic segmentation algorithm for general domains. This model incorporates a new attention mechanism characterized by the squeeze axial and detail enhancement features, making it suitable for crafting cost-effective semantic segmentation architectures. In the context of flame segmentation, SeaFormer establishes self-adaptive global dependencies among flame pixels in the horizontal and vertical directions. This capability enables the accurate determination of flame pixels' presence within their respective neighborhoods. This paper introduces Flame-SeaFormer, a flame situation detection model based on the pixel-level segmentation of visual images. It modifies specific parameters (such as the number of segmentation categories) of the SeaFormer model to suit the research task. The difference between Flame-SeaFormer and SeaFormer is shown in Table 1. SeaFormer focuses on the design of a semantic segmentation model, while Flame-SeaFormer places a greater emphasis on the further application of the model to analyze flame situations and explore flame combustion patterns. The overall framework of Flame-SeaFormer is illustrated in Figure 5. Firstly, for input fire video sequences containing complete combustion processes (top-left of Figure 5), the SeaFormer model (middle-left of Figure 5) extracts prominent fire features from the images and outputs pixel-level segmentation results for each image (bottom-left of Figure 5). Secondly, following the calculation of the flame parameters, static and dynamic flame parameters at different time points are obtained. Finally, based on the predicted flame parameters, flame parameter trend curves are plotted (right side of Figure 5; for spatial constraints, only static flame parameters are displayed). Then, a flame situation analysis is conducted. It consists of three main parts: the flame segmentation model SeaFormer, the inversion of static flame parameters, and the inversion of dynamic flame parameters. Subsequently, three parts of the model are introduced.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **SeaFormer** & **Flame-SeaFormer (Ours)** \\ \hline Application domain & General domain & Fire domain \\ \hline Trend analysis & - & Flame situation analysis \\ \hline Static flame parameter & Flame height & - & ✓ \\ parameter & Flame width & - & ✓ \\ mining & Flame area & - & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 1: The differences between Flame-SeaFormer and SeaFormer.
###### Abstract  
In the current work, the non-invasive approach of breath analysis is implemented for the first time in an eccentric exercise protocol that investigated the effect of spirulina supplementation on exercise-induced oxidative stress. We assessed whether volatile alkanes in exhaled breath can serve as alternative biomarkers of oxidative stress. A randomized, double-blinded, placebo-controlled, crossover supplementation study was carried out enrolling 14 participants. The volunteers consumed 42 mg-kg\({}^{-1}\) body weight of either _Spirulina Nigrita_ or maltodextrin, as a placebo, daily for 15 days. Afterward, they followed a damaging eccentric exercise protocol of the upper limbs. Expired breath samples were collected from then just before supplementation (baseline measurement), prior to exercise, and 1 h, 24 h, 48 h, and 72 h after exercise. The samples were analyzed by Gas Chromatography/Mass Spectrometry (GC-MS) coupled with a thermal desorption unit (TDU) to determine the alveolar gradient (AG) of several alkanes, C5-C14, that are known to be related to oxidative stress. Apart from breath analysis, TBARSs were also determined as a crude marker of lipid peroxidation. Two-way repeated measures ANOVA tests were applied to the alkanes' AGs between the spirulina (SPI) and placebo (PL) groups across time. In the PL group, a trend of increasing almost all alkanes immediately after exercise, with a gradual return to pre-exercise levels up to 72 h later was revealed. A statistically significant time effect was observed for 2-methylhexane, 3-methylhexane, heptane, octane, and undecane. The administration of spirulina appeared to reduce the increases in alkanes after exercise, and a statistically significant attenuation was observed for 2-methylpentane and 2-methylhexane. An examination of TBARSs confirmed that the reduced increases observed in the SPI group were due to changes in lipid peroxidation, while a positive correlation between the iAUC of TBARSs and that of 2-methylhexane and 3-methylhexane was revealed. In conclusion, the analysis of volatile alkanes in exhaled breath may serve as an attractive alternative for assessing redox changes after eccentric exercise compared to traditional blood biomarkers.  
breath analysis; exercise-induced oxidative stress; food supplements; spirulina Article
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
### RTK versus NRTK  
It is of practical interest to highlight whether RTK positioning from a local master station and NRTK positioning provide the same accuracy. NRTK offers savings and operational advantages, as it does not need a local GNSS master station; in practice, this is not advisable unless a good Global System for Mobile communication (GSM) network coverage is guaranteed. In fact, as the differential corrections from the CORS network are accessed through the internet via a GSM modem, poor GSM signal quality at the drone base station would impede the NRTK survey.  
If the CORS network management allows for the generation of Virtual Rinex files, and if on-board GNSS raw data are recorded, the trajectory can also be recovered in case of a lack of GSM signal. However, as Virtual Rinex files are generated at a later time, there is no guarantee at the survey time that a high-quality navigation solution was achieved. Moreover, it should not be forgotten that CORS stations cannot predict accurate tropospheric corrections at altitudes that are very different from those of the network stations. Therefore, a bias can be expected for surveys in mountain areas. On the other hand, whenever access to the site by ground vehicles is difficult, if a NRTK service is available, only the lightweight drone needs to be carried to the survey site.
## 4 Parameter Optimization
### Point Selection
#### 4.1.1 Target Buildings  
To ensure that the altitude accuracy is meaningful, it is important to have the control points aligned in the latitude and longitude directions. The role of the target buildings is to align the two sets of point clouds in the horizontal direction.  
In the inspection area, the buildings suitable for a parametric check have a regular geometry and are at a certain height above the ground. A typical building suitable for checking schools is shown in Figure 6b.  
In order to choose the target building points, a rectangle should be boxed around the building. Then, the points higher than the altitude threshold in the rectangular area should be selected. It must be ensured that the rectangular selection box is flat except for the target building.  
Target buildings are used to match the points from different air lines, so the strip splicing error should be eliminated through point-selection work. We have to select buildings that stand on one strip completely, which are always small, as shown in Figure 6.  
There are three sets of targets: the first contains points generated from east to west, the second from north to south, and the third from west to east. The three airlines constitute two control groups: white numbers on the first picture are compared against the second, and black numbers are subtracted by the third.  
Figure 6: Target buildings: (**a**) top view of target buildings; (**b**) side view of the building in red in (**a**); (**c**–**e**) selected buildings, with (**c**) showing the airline from east to west, (**d**) from north to south, and (**e**) from west to east.  
We checked the accuracy of the latitude and longitude directions using two groups, one of which is reverse-checking and the other is crosschecking. Both of them need four vertexes selected from imprecise point cloud manually.  
Depending on the point of the target building, it is possible to make a judgment about the error in the horizontal direction as  
\[\overline{e}_{b}=\frac{1}{18}{\sum\limits_{i=1}^{9}{\sqrt{\left({x_{i}^{c1}-x_{ i}^{d}}\right)^{2}+\left({y_{i}^{c1}-y_{i}^{d}}\right)^{2}}}}+\sqrt{\left({x_{i}^{ c2}-x_{i}^{e}}\right)^{2}+\left({y_{i}^{c2}-y_{i}^{e}}\right)^{2}}, \tag{15}\]  
where the subscript \(i\) denotes the average value of the \(i\)th set of point clouds, \(c1,c2,d,e\) correspond to the four different labeled builds in the three figures; x is the relative distance in longitude; and y is the latitude.
# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 2 Data and Methodology
### Relative Importance Analysis and Ranking  
Using the connection weight matrix method, the relative significance of the predictive variables for the ANN model was calculated [44]. The concept of using the weight connection to determine the relevance of predicting variables in an ANN model was initially proposed by [PERSON] [45]. The overall absolute weight contribution fraction of the variables with regard to each neuron was calculated using the Garson algorithm to determine the relative importance of the predicting variable, as indicated in Equation (7). [PERSON] and colleagues later claimed that the net sum of the weight contribution for each predicting variable across the neurons yielded more accurate rankings [44], as given in Equation (8). Both approaches were utilized in this study to rank the relative relevance of the predicting variables from the ANN model.  
\[\begin{split} Garson\ Relative\ Importance(x_{i})=\frac{\sum_{j=1}^{n} \left|W_{ij}^{h}*W_{j}^{o}\right|}{\sum_{i=1}^{n}\left\{\sum_{j=1}^{n}\left|W_ {ij}^{h}*W_{j}^{o}\right|\right.}}\end{split} \tag{7}\]\[\text{{Olden Relative Importance}}(x_{i})=\frac{\left|\sum_{j=1}^{n}W_{ij}^{n}*W_{j}^{o} \right|}{\sum_{i=1}^{m}\{\left|\sum_{j=1}^{N}W_{ij}^{n}*W_{j}^{o}\right|\}} \tag{8}\]  
where \(x_{i}\) is the \(i^{th}\) predicting variable, \(W_{ij}^{n}\) is the weight matrix of the hidden layer of the ANN model, \(W_{j}^{o}\) is the weight matrix of the output layer, \(n\) is the maximum number of neurons in the hidden layer, and \(m\) is the number of predicting variables.  
The relative importance of the variables from the regression models was also estimated using relative weight analysis [46; 47]. The traditional use of normalized coefficients was found to yield misleading results due to some level of correlation between the predicting variables [47]. The relative weight analysis eliminates the effect of variable inter-correlation by transforming the predictors into sets of orthogonal variables. The end result is a decomposed contribution of each variable to the \(\mathbb{R}^{2}\) of the model in explaining the dependent variable. The analysis was run using R code web-based software with all variables at the 5% significant level and with 10,000 bootstrapping iterations [48].
###### Abstract  
Cyclic steam stimulation (CSS) is one of the main offshore heavy oil recovery methods used. Predicting the production of horizontal CSS wells is significant for developing offshore heavy oil reservoirs. Currently, the existing reservoir numerical simulation and analytical models are the two major methods to predict the production of horizontal CSS wells. The reservoir numerical simulation method is tedious and time-consuming, while the analytical models need many assumptions, decreasing models' accuracy. Therefore, in this study, a novel methodology combining the particle swarm optimization algorithm (PA) and long short-term memory (LM) model was developed to predict the production of horizontal CSS wells. First, a simulation model was established to calculate the cumulative oil production (COP) of horizontal CSS wells under different well, geological, and operational parameters, and then the correlations between the calculated COP and parameters were analyzed by Pearson correlation coefficient to select the input variables and to generate the initial data set. Then, a PA-LM model for the COP of horizontal CSS wells was developed by utilizing the PA to determine the optimal hyperparameters of the LM model. Finally, the accuracy of the PA-LM model was validated by the initial data set and actual production data. The results showed that, compared with the LM model, the mean absolute percentage error (MAPE) of the testing set for the PA-LM model decreased by 4.27%, and the percentage of the paired points in zone A increased by 2.8% in the Clarke error grids. In addition, the MAPEs of the training set for the PA-LM and LM models stabilized at 267 and 304 epochs, respectively. Therefore, the proposed PA-LM model had a higher accuracy, a stronger generalization ability, and a faster convergence rate. The MAPEs of the actual and predicted COP of the wells B1H and B5H by the optimized PA-LM model were 8.66% and 5.93%, respectively, satisfying the requirements in field applications.  
Keywords:offshore heavy oil reservoirs; cyclic steam stimulation; particle swarm optimization algorithm; long short-term memory model; cumulative oil production +
Footnote †: journal: Applied Sciences