esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Controlling Factors of RCPs
#### 5.3.3 Terrigenous Debris Input  
Under stable conditions, the accumulation of carbonate plants exceeds the rate of relative sea level rise, which is the seemingly irreconcilable contradiction between carbonate platform submerging and relative sea level. Based on the analysis of a large number of outcrops, sedimentary microfacies, geochemistry and the seismic reflection structure of carbonate platforms, it is concluded that the inundation of carbonate platform was mainly the result of terrestrial debris input and water eutrophication [10; 14; 15; 37; 38]. Therefore, in order to elucidate the submerged mechanism of RCPs in Wan'an Basin, it is necessary to discuss the paleomarine environment of RCPs except water depth.  
The main part of the Wan'an Basin in the southern South China Sea is located on the shelf of the Indo-China Peninsula, and the deposition is mainly controlled by the Mekong River. At the end of the Late Miocene, terrigenous debris began to accumulate on a large scale from west to east in Figures 6 and 7. A large amount of sediment input makes the seawater turbid and reduces its transparency, causing the RCPs that require clean water to suffocate, leading to the inundation of the platforms [14]. This explanation seems reasonable. However, the information from nine wells showed that no terrigenous particles were found on the top interface of the RCPs, and there was no obvious abnormality in the GR curve from bottom to top, which also showed that the lithology of the RCPs was relatively pure in Figures 4 and 5. This is obviously inconsistent with the inundation of the platform caused by the input of a large amount of terrestrial debris. In fact, a large number of studies have shown that in the process of importing terrestrial debris, the transportation speed of nutrients carried by it far exceeds the transportation speed of sediment, and the scope of its impact is also wider [10; 14; 37]. The results of research on indicators such as clay minerals/feldspar, kaolinite/chlorite ratio and bio-silica content accumulation rate from ODP1143 station showed that the sedimentary minerals at ODP1143 station mainly came from the input of the Mekong River, and it became obvious at 8.5 Ma; this could have been related to the increase in surface runoff caused by the uplift of the Indo-Chinese Peninsula and the strengthening of the East Asian summer monsoon over the same period. In the process of importing terrestrial debris, the nutrients such as phosphate and nitrate carried by it first reach the development areas of RCPs, causing the eutrophication of the water body, reducing the growth rate of RCPs [10; 14; 15; 37]. The rapid subsidence of the basement and the relative stability of the global sea level further accelerated the inundation of RCPs. The intensification of the East Asian summer monsoon lasted 2.3 Ma (8.5 Ma-6.2 Ma), which was consistent with the fact that the organic RCPs began to be covered by terrigenous debris after 5.5 Ma.
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 2 Related Works
### LIDAR-Based 3D Object Detection  
LiDAR sensors can directly capture high-precision 3D point cloud data, eliminating the depth estimation errors inherent in camera-based approaches. As a result, LiDAR-based 3D object detection has garnered significant attention. In two-stage point cloud-based detection methods, PointRCNN [23] directly generates candidate bounding boxes from raw point clouds and refines them through classification and regression. Point-GNN [24] leverages Graph Neural Networks (GNNs) to model the topological structure of point clouds, utilizing message passing mechanisms to enhance 3D object detection performance. However, these methods often struggle with sparse and noisy point cloud data, making them susceptible to local information loss and reducing their robustness. To improve efficiency, researchers have proposed single-stage point cloud detection methods. YOLO3D [25] adopts a single-stage detection strategy based on the YOLO [26] framework, directly regressing the pose and size of 3D objects from LiDAR point cloud data to improve inference speed and reduce computational cost. 3 DSSD [27] integrates a fusion-based farthest point sampling strategy with a boundary-aware module to enhance detection speed and accuracy. CIA-SSD [28] employs voxel feature extraction and a center IoU-aware strategy to optimize bounding box predictions and improve localization precision. Despite these advancements, single-stage methods still face challenges such as sparse point cloud data and information loss during voxelization, which particularly affect small object detection and dynamic scene adaptability. In contrast, multimodal fusion approaches combine RGB visual information with LiDAR's geometric features, effectively enhancing detection accuracy and robustness, and demonstrating superior performance in complex environments.
###### Abstract
(2005) verified the relationship between marine environmental factors and the distribution of albacore fishing grounds in the Indian Ocean, and the results showed that SST, Chl-a and SSS had significant effects on the distribution of immature albacore, with SST having a particularly prominent effect on the distribution of mature albacore fishing grounds [31]. [PERSON] et al. (2008) analyzed the relationship between albacore fishing grounds in the western North Pacific Ocean and sea surface height anomaly (SSHA), SST and Chl-a, and showed that the optimal range corresponding to the highest catch per unit effort (CPUE) was 18.5-21.5 \({}^{\circ}\)C for SST, 0.2\(\sim\)0.4 mg m\({}^{-3}\) for Chl-a and \(-\)5.0-32.2 cm for SSHA [32]. However, due to the complexity of the marine environment itself, there are differences in the habitat environment of fish in different seas [33]. [PERSON] et al. (2019) showed that Chl-a was the most important marine environmental factor affecting the potential habitat of fish in the Saudi Arabian Red Sea, and the number of potential fishing zones (PFZs) was positively correlated with Chl-a and negatively correlated with SST and SSS [34]. [PERSON] et al. (2021) showed that SST and Chl-a influence the distribution of marine fish resources [35]. Therefore, a quantitative assessment of the influence of marine environmental factors on the forecast accuracy of South Pacific albacore fishing grounds based on RF feature importance analysis is particularly critical to achieve efficient fishing of South Pacific albacore. To make up for the above research deficiencies, this paper took the South Pacific waters as the research area and constructed an albacore fishing grounds forecasting model based on the ensemble learning of five machine learning algorithms, namely, RF, SVM, KNN, XGBoost and GP, to realize the high-accuracy forecasting of different grades of albacore fishing grounds in the South Pacific. The main objectives were as follows: (1) to compare the forecast accuracy of different machine learning algorithms and the ELM for different grades of South Pacific albacore fishing grounds; (2) to explore the application effect of the ELM on albacore fishing grounds with different grades; (3) to quantitatively assess the effects of different marine environmental factors on the forecast of different grades of albacore fishing grounds in the South Pacific Ocean.
## 3 Influence Analysis of Stiffeners on Lamb Wave Propagation Law
### Effect of Stiffeners on Lamb Wave Velocity and Signal Head  
The time-domain waveform of the z-axis displacement obtained at each receiving point of 230 mm from the origin is shown in Figure 7. As can be seen from the figure, the arrival time of the first trough of point 1 to 7 is about 42.2 \(\upmu\)s, and the calculated wave velocity is 5.45 km/s. The first wave trough of the Lamb wave after the same number of stiffeners is roughly equal. The first wave trough of the Lamb wave passing through the two stiffeners is significantly higher than passing three stiffeners, which is also applicable to the first wave crest. It can be seen that the number of stiffeners has almost no effect on the Lamb wave velocity. The more stiffeners the signal passes through, the lower the first crest and trough of the signal is.
# Strategies for Reducing Sodium Intake in Bakery Products, a Review
## 4 Methods for Sodium Content Reduction in Bakery Products
### The Use of Different Types of Mineral Salts as Substitutes for Sodium Chloride in Bakery Products
Total KCl replacement is not recommended due to the unpleasant bitter and metallic taste, which limits consumer acceptability. For this reason, for a higher reduction of sodium in bakery products it is recommended to combine it with other food ingredients [12]. Therefore, the advantage of using KCl in the bread making recipe is a technological and healthy one due to the fact that it increases the potassium intake from the diet which is associated with a very low risk of hypertension, an opposite effect to sodium consumption [66]. A recent study estimated that potassium intake for the United States, Mexico, France, and the United Kingdom was 80%, 95%, 77%, and respectively 95%, under the recommendations of the World Health Organization on potassium intake [111].
# Inter-UAV Routing Scheme Testbeds
## 2 Ad Hoc Routing  
This section is dedicated to the analysis of the fundamental principles governing ad hoc networking, with an emphasis on FANETs. Additionally, some of the most important ad hoc routing protocols are explained and examined, in the context of evaluating them on a physical level, along with mobility models commonly used either to emulate high-mobility FANETs or as a route computation metric. The way in which mobility models affect route selection and are thus considered a route computation metric is link quality estimation (highly dependent on node mobility) accompanied by node position-awareness, which directly affects next hop selection in geographic or movement-predictive routing protocols (e.g., Greedy Perimeter Stateless Routing (GPSR) [32], Greedy Load Share Routing (GLSR) [33], Mobility Prediction based Geographic Routing (MPGR) [34], Directional Optimized Link State Routing (D-OLSR) [35], Geographic Contention-based Forwarding and Cooperative Communications (CoopGeo) [36] and their derived schemes).
## 5 Results
### In-Orbit Results
#### 5.2.2 Measurements over Populated Areas
Figure 10 shows the results of the same procedure applied to the measurements carried out over America, redefining the areas of interest. The coastal regions (\(R4\), \(R6\) and \(R7\)) cover \(40^{\circ}\) of longitude, while the middle region (\(R5\)) covers \(30^{\circ}\). The latitude limits are \(0^{\circ}\), \(35^{\circ}\), and \(70^{\circ}\), as shown in the map. The interference levels are lower than in Figure 9. The regions that are more affected by the interference are \(R4\) and \(R6\). Around \(20\%\) of the points in these regions experience an interference level higher than \(-36\) dB. Furthermore, two different behaviours can be observed in region \(R6\): \(57\%\) of the points experience interference levels above \(-38\) dB, while the rest of the points follow a different trend with lower power. The regions \(R5\) and \(R7\) follow a distribution that is not so different from the noise (dotted black line). Figure 10: Power distribution over America divided in four regions. The colour blue is for western, green for central, and orange for eastern areas. (**a**) Measurement locations over several orbits. The regions \(R4\), \(R6\) and \(R7\) cover \(40^{\circ}\) of longitude; and the middle region \(R5\) covers \(30^{\circ}\). \(R4\) and \(R5\) cover \(70^{\circ}\) of latitude; and \(R6\) and \(R7\), \(35^{\circ}\). (**b**) ECDF of received power over the four regions, including the noise floor measurements (black dotted line). The yellow continuous line is linked to the circles in the map and dashed lines to crosses. The red line is the ECDF of all the points in the map. Figure 9: Power distribution over Europe, northern Africa, and the Middle East. (**a**) Measurement locations over several orbits. The northern regions cover \(35^{\circ}\) of longitude and latitude; and the southern region, \(70^{\circ}\) of longitude and \(35^{\circ}\) of latitude. (**b**) Empirical Cumulative Density Function (ECDF) of received power over the three regions and all frequency bins within the 3 dB bandwidth, including the noise floor measurements (black dotted line). The blue continuous line is linked to the blue circles in the map and dashed lines to crosses. Red line is the ECDF of all the points in the map. One representative example of a measurement where the band-limited signal can be seen in the average power spectrum is shown in Figure 11a, obtained close to Greenland. Two types of interference signals can be distinguished: one narrow-band at 434.74 MHz and the band-limited signal with a bandwidth around 300 kHz, which is about 7 dB higher than the rest of the spectrum. At the same time, the CV indicates that the band-limited signal has a higher spread as CV increases up to 2.6. The temporal behaviour of the frequency bins differs (Figure 11b,c). When analysing both figures, we can see the main behaviour of the band-limited signal converging to about 0.25. The edges of the spectrum are a combination of noise and the sidelobes of the band-limited signal and converge to around 0.6.
# A Two-Phase Approach for Predicting Highway Passenger Volume
## 6 Phase II: Model Prediction and Evaluation  
### Model Prediction  
With the significant impact factors selected by Phase I as input variables, Phase II developed the DFNN to predict highway passenger volume. The primary concern of developing DFNN is to determine the appropriate quantity of hidden layers and neurons in each hidden layer. In this study, the grid search method was adopted, whose initial range for the number of hidden layers was set from 1 to 10 and that for the number of neurons was set from 1 to 140. Taking MAE as an evaluating index, the result of the grid search method is shown in Figure 5.  
The quantity of hidden layers and neurons with the minimum MAE is selected. Finally, the quantity of hidden layers is set to 9, and the quantity of neurons in each hidden layer is set to 120 in the DFNN of this study. Moreover, the quantity of neurons in the input layer and the output layer is set to 30 and 1, respectively, because there are 30 independent variables and 1 dependent variable.  
Additionally, multiple epochs are needed for improving the predicted accuracy of the DFNN. Consequently, we continuously increased the epoch and calculated the loss of training set and verification set. When the loss of four consecutive epochs is less than 0.0001, it is considered that the training process has reached convergence and can be stopped. The loss of the training process is shown in Figure 6. Finally, the epoch of the DFNN in this study was set to 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Included Impact Factors** \\ \hline Urban economic level & GDP, RSC, RT, GIO \\ Urban population size and structure & TP, NSC, WPI, WSI, WTI, PD, PLPG \\ Per-capita income and consumption & AWW, DB, HD, WCS, HEC \\ Urban industrial structure & PL, SI, TI \\ Resource and environment & DLA, LC, NOB, APR, APGL, GCAP, NBH, \\  & NTM, CPL, VDWW, VSDE \\ \hline \hline \end{tabular}
\end{table}
Table 2: The extraction result of significant impact factors.  
Figure 5: The result of grid search method for determining hidden layers and neurons.  
Afterward, the significant impact factors were input in the developed DFNN, and the highway passenger volume was predicted. Then, evaluating indicators were calculated, showing that the MAE and RMSE of predicted highway volume from the DFNN are 2066.31 persons per day and 4176.37 persons per day, respectively.
# Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications
## 4 Results and Discussion
### Shielding Analysis of IPT Systems
The enhanced shielding capabilities, coupled with improved durability and efficiency, support the potential of magnetic concrete as an alternative to traditional ferrite cores.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 3 Implementation Results and Analysis
### Write Access Time  
The WAT is defined as the time interval between the moment the WL voltage reaches 50% of its full swing and the point at which the voltages of the storage node and its complementary node intersect. A shorter WAT means a faster write operation. Figure 8 shows the WAT and WWTV. The WAT is primarily influenced by the internal feedback structure, the number and type of access transistors, and the ease with which stored charges can be flipped during a write operation. First, WRTU-16T has a cross-coupled inverter pair that forms a feedback loop, accelerating the state transition by reinforcing voltage changes between Q and QB. Second, WRTU-16T has four access transistors--two PMOSs and two NMOSs--that simultaneously connect the storage nodes and internal nodes to the bit lines. This parallel access provides multiple discharge paths, enhancing the speed of write operations. Third, the storage nodes are surrounded by NMOS transistors, which tend to weakly hold a logic '1', allowing for faster discharge through the bit lines during a write, thereby further reducing the WAT. The three architectural advantages reduce the unnecessary contending current and enable fast and reliable write performance. As shown in Figure 9, the WRTU-16T exhibits an improved WAT.  
Figure 8: WAT and WWTV measurement method.  
Figure 7: Comparison of RSNM with other SRAMs: (**a**) at VDD = 1 V, (**b**) RSNM Measurement.
## 3 Results and Discussion
This effect on \(S^{\text{2D}}\) could be only numerical taking into account that \(S^{\text{2D}}\) is approximated because its calculation is based on the skyrmion energy harmonic approximation. Therefore, it could depend on some numerical errors arising from the approximation of the real skyrmion energy that are enhanced with increasing the amplitude of the external bias field and that can result more effective in the calculation of \(S^{\text{2D}}\). Actually, a simple physical consideration can be done. For this type of thermodynamic systems the entropic contribution \(TS\) to the Helmholtz free energy \(F=\textless E>-\,T\,S\), with \(\textless E>\approx a\textless D_{\text{sky}}\)2\(>\) the average skyrmion energy, should be of the same order of \(\textless E>\) (on average about \(10^{-20}\) J) resulting in \(F\) at least one order of magnitude less than each of the energetic and entropic contributions. Nevertheless, this occurs only within the 3D description. Finally, note that the 3D description was also supported by micromagnetic simulations that accounted for the thickness \(t\) of the ferromagnetic material [13]. According to those considerations, one concludes that, albeit skyrmions are planar structures, a 3D theoretical description to discuss the statistical thermodynamic properties including also a dependence on the thickness \(t\) of the magnetic skyrmion is preferable. A final confirmation of which of the two descriptions is the more realistic one could come, for example, from calorimetric measurements able to determine in an accurate way the entropy of a magnetic skyrmions population. Figure 4: **(a)** 2D (solid lines) and 3D (from Table 1 of [14]) (dashed lines) average skyrmion diameters as a function of \(T\) for \(\mu_{0}H_{\text{ext}}\) = 0 mT (blue lines); \(\mu_{0}H_{\text{ext}}\) = 25 mT (green lines) and \(\mu_{0}H_{\text{ext}}\) = 50 mT (red lines); **(b)** standard deviation of the 2D (continuous lines) and 3D (dashed lines) distribution (from Figure 4 of [13]) as a function of \(T\) for \(\mu_{0}H_{\text{ext}}\) = 0 mT (black lines); \(\mu_{0}H_{\text{ext}}\) = 25 mT (red lines) and \(\mu_{0}H_{\text{ext}}\) = 50 mT (blue lines).
# Study on Disintegration and Infiltration Characteristics of Nanjing Jiangxinzhou Embankment Soil
## 3 Results  
### Disintegration Test  
#### 3.1.1 Mass Time Variation Curves  
When the water content was low, the remolded sample absorbed water and gained weight first due to the suction of the matrix. The destruction was not strictly one-way disintegration. The destruction included water absorption, weight gain, and disintegration. Therefore, the percentage change in mass was used instead of the actual disintegration amount. The results are shown in Figure 5.  
Throughout the test, the samples for all working conditions eventually disintegrated completely. Both water content and dry density significantly affected the soil samples' disintegration characteristics. First, the rate of change of sample mass slowed with the increase in dry density degree.  
Secondly, when the water content was low (5%-15%), sample mass change had the following stages: absorption, weight gain, and disintegration. In the initial stage, the water absorption of the sample was more significant than the disintegration amount, and the macroscopic expression was an increase in the total mass. In the later stage, the increase in the disintegration amount showed a continuous decrease in mass.  
When the initial water content was significant (>15%), the continuous and rapid mass loss occurred after the soil was immersed in water until the disintegration was complete. This phenomenon was inconsistent with the conclusion of many scholars in previous studies wherein the degree of soil disintegration intensity was inversely proportional to the water content [11; 25]. Since there is a threshold water content of around 20% and the optimum water content of the tested soil is 19.86%, it is assumed that the disintegration pattern of the soil may be significantly different depending on the optimum moisture content.  
Figure 5: \(Cont.\)
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 2 Intrusion Detection in the Internet of Things
### Detection Method  
This refers to how the IDS detects potential threats [40].  
* Signature-based: uses predefined patterns or signatures of known threats.
* Anomaly-based: establishes a \"normal\" behavior baseline and detects deviations from this baseline.
* Specification-based: defines a set of rules or specifications that determine valid behavior.
* Hybrid: combines multiple methods for more comprehensive detection.