esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Damage Morphology  
Figure 10 shows the damage comparison diagram for different laminates from the experimental and numerical results for the case of 32-J of impact energy. As seen from Figure 10, the damage diagrams of all of the composite laminates obtained by numerical simulations match well with those  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stacking Sequence**} & \multicolumn{2}{c}{**Experiments**} & \multicolumn{2}{c}{**Simulations**} \\  & **Peak Force** & **Absorbed** & **Peak Force** & **Absorbed** \\  & **(kN)** & **Energy (J)** & **(kN)** & **Energy (J)** \\ \hline \([0/90]_{8}\) & 6.4438 & 45.3494 & 6.7991 & 45.1237 \\ \([(0/90)_{4}/\text{SMA}/(0/90)_{4}]\) & 6.5688 & 43.8837 & 6.7771 & 44.9752 \\ \([0/\text{SMA}/90/(0/90)_{7}]\) & 6.3605 & 43.3652 & 6.3418 & 41.9943 \\ \([0/\text{SMA}/90/(0/90)_{3}/\text{SMA}/(0/90)_{4}]\) & 6.5549 & 43.0796 & 6.7991 & 52.7428 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The impact parameters of various laminates for experiment and simulation under 64-J impact energy.  
Figure 8: The comparison plots of energy–time from experimental and numerical results for the case of 64-J impact energy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stacking Sequence**} & \multicolumn{2}{c}{**Experiments**} & \multicolumn{2}{c}{**Simulations**} \\  & **Peak Force** & **Absorbed** & **Peak Force** & **Absorbed** \\  & **(kN)** & **Energy (J)** & **(kN)** & **Energy (J)** \\ \hline \([0/90]_{8}\) & 6.1012 & 26.2039 & 6.2480 & 29.0969 \\ \([(0/90)_{4}/\text{SMA}/(0/90)_{4}]\) & 6.4666 & 25.0616 & 6.7845 & 20.9080 \\ \([0/\text{SMA}/90/(0/90)_{7}]\) & 6.9370 & 24.0008 & 7.2166 & 29.0764 \\ \([0/\text{SMA}/90/(0/90)_{3}/\text{SMA}/(0/90)_{4}]\) & 6.8569 & 23.2786 & 7.1506 & 20.9550 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The impact parameters of various laminates for experiment and simulation under 32-J impact energy.  
Figure 9: The comparison plots of energy–time from experimental and numerical results for the case of 64-J impact energy.  
obtained by experiments. Besides, in terms of damage morphology, the laminate-stitched SMAs have smaller damage than the laminate-unstitched SMAs in the 32-J perforation condition.  
Figure 11 shows the damage comparison diagram for different laminates from experimental and numerical results for the case of 64-J impact energy. As seen from Figure 11, the damage diagrams of all of the composite laminates obtained by numerical simulations match well with those obtained by experiments.
## 5 Discussion and Future Research Trends  
Current studies demonstrate that scientists show much concern for the modern use of DL approaches to address problems in target detection through UAVs, for instance, small-size objects, target occlusion, complex backgrounds, and joint issues. However, to overcome these problems, there are even now obvious questions for scientists. The data in this section reflect DL-existing conditions for target detection and indicate future fact-finding directions.
# Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
## 3 Results
### The Effects of the VFS on Nutrient Loss
On a slope of 20\({}^{\circ}\) and rainfall intensity of 30 mm/h, in terms of reducing the loss of TP in the sediment, VFS2 was better than VFS1 because of an additional 13% reduction, and an additional 7% reduction in TP loss under the condition of rainfall intensity of 60 mm/h. However, when the rainfall increased to 90 mm/h, it was more appropriate to set VFS1 on the slope of 20, which can reduce TP loss by 22%, while VFS2 can only reduce TP loss by 16. When the slope was 35 and the rain intensity was 30 or 90 mm/h, VFS2 plays a more significant role in reducing total phosphorus loss in sediment. However, for the 35\({}^{\circ}\) slope with rainfall intensity of 60 mm/h, setting VFS1 could reduce the TP loss more.
## 2 Materials and Methods
### MAGDA Demonstrator Sites
#### 2.1.3 Romania-Cereal and Oilseed Farming  
The third demonstrator site is located in Braila County, Romania, within the Danube River floodplain and the Baragan Plain, which is the most fertile agricultural area in Eastern Europe. This region supports large-scale industrial farming of summer crops, including corn, sunflower, soybean, wheat, and barley. Farmers in Braila face significant challenges related to water availability, soil moisture variability, and extreme weather events such as droughts and floods.  
The MAGDA test site in Braila consists of three sensor deployment areas: one within the Embanked Great Island of the Danube River and two further inland in the Baragan Plain. These locations have been selected to assess spatial variability in soil moisture and crop water requirements.  
MAGDA's real-time data assimilation system aims to improve irrigation planning by integrating satellite-derived soil moisture data, in situ GNSS reflections, and atmospheric observations from Meteorrones. By correlating historical precipitation data with current field conditions, the system helps agricultural stakeholders optimize their irrigation schedules and prepare for extreme weather events, ensuring better crop resilience and resource efficiency.
# Olfactory Impact of Terpene Alcohol on Terpenes Aroma Expression in Chrysanthemum Essential Oils
## 3 Results and Discussion
### Organoleptic Effect of Terpene Alcohols on Quantitative Perception
#### 3.2.2 Olfactory Threshold of Terpene Alcohols in Different Matrixes  
These olfactory thresholds of terpene alcohols in alkane solution were shown in Table 5. The findings concerning the olfactory threshold of borneol in water reported in the literature: 0.14 mg/L [33]. In our work, the olfactory threshold of borneol in alkane solution is 2.03 mg/L. The olfactory threshold of lanlool in water reported 0.001 mg/L [34]; the olfactory threshold of lanlool in alkane solution is 3.85 mg/L. While the evaluation of olfactory threshold could be affected by lots of factors [34], the olfactory threshold determination in the literature mostly uses water as the solvent, ignores the function of non-volatile compounds on the thresholds of aromatic compounds [35]. The differences in olfactory threshold between the obtained results and literature data proofed the matrix affected the intensity of volatile aroma compounds. It's obvious that the olfactory threshold of terpene alcohols in alkanes solution was higher than the thresholds in water. Similarly, comparing the thresholds of terpene alcohols in different matrices between alkane solution and TR, the olfactory threshold of isoborneol was 0.61 mg/L in alkane solution and 3.22 mg/L in TR, the threshold is increased by a factor of about five; the olfactory threshold of BEL was 0.87 mg/L in alkane solution and 1.80 mg/L in TR, the threshold is increased by a factor of about two. The threshold concentration in different matrixes is different, and the threshold concentration of compounds in a complex metric is higher.  
Figure 2: Volatile loss of terpene alcohols over time in sensory analysis in Terpenes Recombination.  
The olfactory threshold of borneol and linalool in alkane solution is 2.03 mg/L and 3.85 mg/L, respectively; the olfactory threshold of the mixture (BL) in alkane solution is 0.05 mg/L. In alkanes solution, D-fenchyl alcohol added to TR led to a decrease in the \"olfactory threshold\" of the mixture (from 30.69 to 12.74 mg/L). The addition of d-fenchyl alcohol led to a 2.41-fold (\(p<0.001\)) decrease. That interesting finding has also revealed the aromatic interaction mechanism. Therefore, in order to study these effects, further experiments were carried out by adding three different concentrations of aromatic compounds to the terpen recombination.
## 7 Conclusions and Future Work  
This paper presented a distributed network management scheme to address the real-time, reliability and throughput requirements of monitoring and process control applications in industrial automation. The resource reservation technique is used in D-MSR for allocating and reserving the communication resources along the path between two end-points (sensors and actuators/gateway). Channel hopping technique is used in D-MSR to prevent external interference and multipath fading in order to provide a reliable communication. This paper showed that D-MSR is more efficient than WirelessHART in managing the network when it comes to node joining, reserving the communication resources (either to exchange management packets or sensor data packets), and coping with network dynamicity (e.g., node or edge failures) in terms of latency and overhead. Furthermore, in case of extensive external interference, D-MSR requires less time to reach the stable data delivery ratio value in comparison with WirelessHART.  
The spatial reuse of communication resources in D-MSR improves the throughput in the large-scale network at the potential cost of reduced reliability due to internal interference. That is because concurrent transmissions in the same cell may cause transmission failure even when the edges are two hops away from each other, since in a realistic setting the interference and transmission range may not be equal. On the other hand, by avoiding the spatial reuse of communication resources in WirelessHART, the throughput is reduced. This makes WirelessHART less suitable for large scale networks.  
Control in the field is not recommended by WirelessHART network. The network manager in WirelessHART supports peer-to-peer sessions between sensors and actuators if the resulting communications are routed via the gateway. This results in more energy consumption by the nodes close to the access points. D-MSR, On the other hand solves that problem by enabling peer-to-peer sessions communication in the field without involving the gateway or access points. This also results in lower energy consumption over the whole network.  
The end-to-end delay in D-MSR is close to that of WirelessHART. This result shows that D-MSR can address the real-time requirements, while also achieving a higher efficiency in the network management than WirelessHART, in terms of delay and overhead. Even though the results are promising already, the following points are expected to improve the capabilities of D-MSR.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 3 Connectivity Requirements for Drones
### Remote and Real-Time Control (RRC)  
RRC depends on real-time flight progress reports from drones, including geo-coordinates and devices status. RRC enables a remote controller to release real-time command and control instructions. To allow remote control and tracking of the drones, specific data rates and latency criteria must be satisfied. With 6G, several drones can operate autonomously (i.e., autonomously in beyond-visible LoS). Sixth Generation connections integrated with satellites can provide communication over unlimited distances and provide near-instant control with a latency of less than 1 millisecond. If drones have 6G connectivity, they can be controlled from anywhere in the world using the Drones Traffic Management (UTM) system.
## 2 Methodology
### Goal and Scope
#### 2.1.2 System Description and Boundaries  
The limits of the systems are defined in Figures 1 and 2, where the flow diagrams for the APG and COG processes are represented, respectively. Depending on the waste stream, different unit processes were considered. In general terms, the system boundaries include H\({}_{2}\) separation and purification, methane separation (only for APG), a torch, a combustion engine, and the manufacture and use of the HT PEMFC. The initial stage, for both APG and COG streams, was the separation of hydrogen for its recovery, which required an adequate separation technique according to the feed concentration, required purity, and conditions of the flow of entry. The use of pressure swing adsorption (PSA) was considered the optimal option for this case. PSA can separate some gas species from a mixture of gases under pressure according to the molecular characteristics of the species and their affinity for an adsorbent material at near-ambient temperatures [47]. Two streams were obtained after implementation of PSA: On the one hand, one stream was rich in hydrogen (99.97% purity), which can be used to feed a PEM fuel cell for energy production; on the other hand, the remaining gas stream, with considerable methane content. Consequently, this second stream was targeted for energy recovery. In the case of COG, the resulting CH\({}_{4}\) concentration was high enough to be able to be directly introduced into a biogas combustion engine to produce electricity (solid blue line). For the APG, since the molar concentration of methane does not reach 50%, another separation was required to obtain once again two streams: The first one achieved sufficiently concentrated CH\({}_{4}\) levels to be introduced into the engine, while the other stream was burnt in a torch to decrease the harmful effects of these emissions (solid blue line). Nevertheless, in both cases, if methane recovery was not possible, the combustion of the gases in a torch (dashed blue lines) was proposed.
# An Automatic Navigation System for Unmanned Surface Vehicles in Realistic Sea Environments
## 5 Dynamic Environment Modeling and Path Re-Planning
### The Process of Path Re-Planning  
The process of path re-planning with the DTFMS method is described in Figure 15. First, the dynamic domain models need to be added to the potential map \(V\). For example, Figure 15a adds one own vessel and two target vessels, and each addition time is of 0.08 s. The updated \(V\) is acquired by the TFMS method within 0.20 s in Figure 15b. Then the arrival time matrix \(T\) and path are generated within 0.20 s, as shown in Figure 15c,e. Figure 15d,f shows the impact of removing a target vessel on the path. As we can see, the path is updated according to the changes of the environment. Affected by the domain model, the new path can follow the current course and stay away from target vessels.  
Figure 14: The outputs of target vessel domain model for increasing speed \(U\) with \(T_{upd}=4\) s, \(r_{min}=20\) m, and \(ULim=5\) m/s. The dotted line represents the output for the bow section, and the solid line represents the stern section of the target vehicle. \(r_{min}\): minimum radius of the domain model; \(ULim\): the boundary value of speed; _Rbow_: the semi-major axis of the bow part; _Rstern_: the radius of stern part.  
Figure 13: Target vessel dynamic domain model. (**a**) Schematic of the target vessel dynamic domain model; (**b**) The pixel image of domain model with speed 2.5 m/s; (**c**) Speed 5 m/s; (**d**) Speed 6 m/s; (**e**) Speed 8 m/s; (**f**) Speed 10 m/s.  
Similarly, an actual environment example of path re-planning using the DTFMS method is shown in Figure 16.  
In terms of computation time, the simulation environment (463 pixels \(\times\) 613 pixels) by the DTFMS method requires 0.50 s to explore the space, whereas the realistic environment takes 3.80 s with dimensions  
Figure 16: Path re-planning in an actual environment. (**a**) Addition of domain models to potential map \(V\) (time 0.30 s); (**b**) New potential map \(V^{\prime}\) by tunable fast marching square (TFMS; time 1.80 s); (**c**) Generated path and arrival time matrix \(T\) (time 1.80 s).  
Figure 15: The process of path re-planning. (**a**) Addition of domain models to potential map \(V\) (time 0.08 s); (**b**) New potential map \(V^{\prime}\) by tunable fast marching square (TFMS; time 0.20 s); (**c**) Generated path and arrival time matrix \(T\) (time 0.22 s); (**d**) Generated path and arrival time matrix \(T\) with a single target vessel domain model; (**e**) Generated path and arrival time matrix \(T\) in three dimensions; (**f**) Single target vehicle domain model in three dimensions.  
of 1253 \(\times\) 1794 pixels. Because the realistic environment needs to explore more space, it takes more time. Therefore, the update time \(T_{upd}\) we choose should be longer than the computation time of the algorithm.
# Enhancing Sequence Movie Recommendation System Using Deep Learning and KMeans
###### Abstract  
A flood of information has occurred, making it challenging for people to find and filter their favorite items. Recommendation systems (RSs) have emerged as a solution to this problem; however, traditional Appenrecommendation systems, including collaborative filtering, and content-based filtering, face significant challenges such as data scalability, data scarcity, and the cold-start problem, all of which require advanced solutions. Therefore, we propose a ranking and enhancing sequence movie recommendation system that utilizes the combination model of deep learning to resolve the existing issues. To mitigate these challenges, we design an RSs model that utilizes user information (age, gender, occupation) to analyze new users and match them with others who have similar preferences. Initially, we construct sequences of user behavior to effectively predict the potential next target movie of users. We then incorporate user information and movie sequence embeddings as input features to reduce the dimensionality, before feeding them into a transformer architecture and multilayer perceptron (MLP). Our model integrates a transformer layer with positional encoding for user behavior sequences and multi-head attention mechanisms to enhance prediction accuracy. Furthermore, the system applies KMeans clustering to movie genre embeddings, grouping similar movies and integrating this clustering information with predicted ratings to ensure diversity in the personalized recommendations for target users. Evaluating our model on two MovieLens datasets (100 Kand 1 M) demonstrated significant improvements, achieving RMSE, MAE, precision, recall, and F1 scores of 1.0756, 0.8741, 0.5516, 0.3260, and 0.4098 for the 100 K dataset, and 0.9927, 0.8007, 0.5838, 0.4723, and 0.5222 for the 1 M dataset, respectively. This approach not only effectively mitigates cold-start and scalability issues but also surpasses baseline techniques in Top-N item recommendations, highlighting its efficacy in the contemporary environment of abundant data.  
recommender systems; collaborative filtering; enhancing sequence movie recommendation system; deep learning; transformer architecture; KMeans +
Footnote †: journal: Applied sciences
# The Impacts of Human Activities on Ecosystems within China's Nature Reserves
###### Abstract  
Protected areas (PAs) provide refuges for threatened species and are considered to be the most important approach to biodiversity conservation. Besides climate change, increasing human population is the biggest threat to biodiversity and habitats in PAs. In this paper, the temporal and spatial variations of land cover changes (LCC), vegetation fraction (VFC), and net primary productivity (NPP) were studied to present the ecosystem dynamics of habitats in 6 different types of national nature reserves (NNRs) in 8 climate zones in China. Furthermore, we used Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light datasets and the human disturbance (HD) index estimated from LCC to quantify the living and developing human pressures within the NNRs in the period 2000-2013. The results showed that (1) the living human activities of NNRs increased apparently in the humid warm-temperate zone, Qinghai-Tibet Plateau, mid-temperate semi-arid zone, and mid-temperate humid zone, with the highest increase of nighttime light observed in inland wetlands; (2) the developing human activities in NNRs indicated by the HD index were higher in the humid warm-temperate zone and mid-temperate semi-arid zone as a result of increasing areas of agricultural and built activities, and lower in the sub-tropics due to improved conservation of forest ecosystems; (3) the relationship between HD and VFC suggests that ecosystems in most NNRs of south-subtropics, mid-temperate arid zone and Qinghai-Tibet Plateau were predominantly impacted by climate change. However, HDs were the prevalent factor of ecosystem dynamics in most NNRs of north-subtropics, mid-temperate semi-arid and humid zones.  
national nature reserves; human activities; land cover changes; nighttime light; vegetation fraction; net primary productivity; China +
Footnote †: journal: Physics Letters B
## 4 Results and Discussion
### Deep Learning Approach vs. Statistical Analytical Approach
* Our algorithm does not use a voxel representation of the object; this representation may result in the removal of the fine details of the object and would make the surface rough. The proposed approach to finding extrema statistically by summating random values is not among classical techniques or modification of the known approaches; to the best of our knowledge, it has not been used before by anyone. Summarizing the abovementioned, we can conclude the following. The data preparation for neural network processing requires either voxelization of the object inside the box with a limited number of overall voxels or choosing a limited subset of points to represent the object (usually around 1 K points only). We work directly with the mesh of thousands, and even millions of vertices, and subsampling of this mesh would affect the accuracy of mapping objects with fine details and complex internal structures. Additionally, the comparison result would significantly depend on the choice of these 1 K points. That is why our approach cannot be directly compared to the neural network approaches.
## 3 Results
### Adjustment Results Using the EAM and UAM for Five and Six Baseline Vectors
The 2 DRMS for the UAM adjustment with five baseline vectors was 0.015 to 0.028 m and, for six vectors, 0.015 to 0.029 m, whereas the 2 DRMS, in the case of the EAM adjustment with five baseline vectors, was 0.021 to 0.061 m and, for six vectors, 0.029 to 0.055 m.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \cline{5-8} \multirow{3}{*}{**Station**} & \multirow{3}{*}{**Solution**} & \multirow{3}{*}{**Statistics**} & \multicolumn{4}{c}{**Number of Baseline Vectors**} \\ \cline{5-8}  & & & & **6** & & & & & \\ \cline{5-8}  & & & & **Coordinate** & & & & & \\ \cline{5-8}  & & & **N** & **E** & **U** & **N** & **E** & **U** \\ \hline \multirow{5}{*}{**OPOC**} & \multirow{5}{*}{EAM} & Mean (m) & 0.000 & 0.008 & 0.001 & 0.001 & 0.000 & 0.000 \\  & & Standard deviation (m) & 0.008 & 0.012 & 0.017 & 0.006 & 0.008 & 0.014 \\  & & Max. (m) & 0.172 & 0.112 & 0.256 & 0.103 & 0.116 & 0.162 \\  & & Mean (m) & 0.001 & 0.003 & 0.001 & 0.001 & 0.000 & 0.001 \\  &When six and five baseline vectors were used with the proposed UAM adjustment, we noticed a decrease in outliers for all stations and elimination of linear discrepancy outliers for coordinate U (Figure 6), which were recorded for the EAM adjustment. In most cases, the values of mean discrepancies of coordinates N, E, and U were within \(\pm 0.002\) m regardless of the adjustment method. Nevertheless, when the UAM method was employed, the standard deviation of coordinates evidently improved. The largest values of the maximum discrepancies were recorded for EAM adjustments. The UAM adjustment significantly reduced the maximum linear discrepancies of each coordinate. Available case studies also confirmed the stability and accuracy of the solution with five baseline vectors [15, 16]. Figure 5: The spatial distribution of positions in the horizontal plane in relation to reference positions of the analysed points for the EAM (black) and UAM (cyan) using baseline vectors to the six and five nearest ASG-EUPOS stations.