esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Drone Cell Placement: Drone Placement for Optimal Coverage
###### Abstract  
Using drone cells to optimize Radio Access Networks is an exemplary way to enhance the capabilities of terrestrial Radio Access Networks. Drones fitted with communication and relay modules can act as drone cells to provide an unobtrusive network connection. The multi-drone-cell placement problem is solved using adapted Dispersive Flies Optimization alongside other meta-heuristic algorithms such as Particle Swarm Optimization and differential evolution. A home-brewed simulator has been used to test the effectiveness of the different implemented algorithms. Specific environment respective parameter tuning has been explored to better highlight the possible advantages of one algorithm over the other in any particular environment. Algorithmic diversity has been explored, leading to several modifications and improvements in the implemented models. The results show that by using tuned parameters, there is a performance uplift in coverage probability when compared to the default meta-heuristic parameters while still remaining within the constraints implied by the problem's requirements and resource limitation. This paper concludes by offering a study and comparison between multiple meta-heuristic approaches, investigating the impact of parameter tuning as well as analyzing the impact of intermittent restarts for the algorithms' persistent diversity.  
drone-assisted radio access network; drone cell placement; hyper-heuristic; parameter tuning; dispersive flies optimization; particle swarm optimization; differential evolution +
Footnote †: journal:  
Article
###### Abstract  
The impact of atmospheric nitrogen deposition on carbon exchange between forest and atmosphere is one of the research hotspots of global change ecology, past researchers have extensively studied the impacts on leaf level, while the impacts on crown CO\({}_{2}\) exchange are still unclear. Therefore, we explored the impacts of different nitrogen addition levels on crown CO\({}_{2}\) exchange of _Fraximus mandshurica_ saplings and their responses to the changes of major meteorological factors (photosynthetically active radiation, _PAR_; vapor pressure deficiency, _VPD_; and air temperature, _T\({}_{\text{air}}\)_) with a novel automated chamber system. There are four levels of nitrogen addition treatments: control (no nitrogen addition, CK), 23 (low nitrogen addition, LN), 46 (medium nitrogen addition, MN), and 69 kgN\(\cdot\)hm\({}^{-2}\cdot\)a\({}^{-1}\) (high nitrogen addition, HN). Our results showed that all nitrogen addition treatments increased daily average and accumulated gross primary production (_GPP_), crown respiration (_R_), and net crown CO\({}_{2}\) exchange (_Ne_), especially at medium and high nitrogen levels. Similarly, maximum net photosynthetic rate (_Ne\({}_{\text{max}}\)_) and apparent quantum efficiency (_a_) were promoted. The change of _Ne_ with _PAR_, _T\({}_{\text{air}}\)_, and _VPD_ showed that nitrogen addition postponed the appearance of photosynthesis midday depression. In addition, the monthly accumulation of \(R\) with all nitrogen addition treatments showed an increasing trend (June to July), and then decreased (July to September) during the growing season, while the _Ne_ and _GPP_ decreased gradually with seasonal vegetation senescence. Finally, the crown shifted from carbon sink to carbon source at the end of the growing season, however, the change under high nitrogen treatment occurred 3 days later. The crown CO\({}_{2}\) exchange measurements provide a new perspective to better understand the response of forest ecosystem CO\({}_{2}\) exchange to elevated nitrogen deposition and provide a basis for related carbon model parameter correction under the influence of nitrogen deposition.  
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## 2 Results and Discussion  
### Solid-Phase Synthesis of Novel FEYNF-NH\({}_{2}\) Pentapeptide  
Certain peptides have the inherent capability to undergo self-assembly, leading to the formation of gels characterized by adjustable properties under specific environmental conditions [12]. In a previous study, we demonstrated the propensity of a pentapeptide inspired by the hen egg-white lysozyme, specifically, FESNF (where F-phenylalanyl, E glutamyl, S-servl, and N-asparaginyl), to self-assemble into ordered and dense fibrils [23]. Given the fact that aromatic amino acid side chains (e.g., phenylalanine, tyrosine, tryptophan, and histidine) are likely to participate in \(\pi\)-stacking interactions [24], we suggested substituting the serine (S) in the FESNF peptide with the aromatic amino acid tyrosine (Y). This led to the synthesis of a novel amidated peptide, FEYNF, with the chemical structure (2D geometry) represented in Figure 1a.  
The synthesis of the FEYNF-NH\({}_{2}\) pentapeptide was effectively achieved using the solid-phase peptide synthesis (SPPS) approach, rooted in the established Fmoc/tBu methodology [15; 16; 25; 26]. As illustrated in Figure 1b, the SPPS technique primarily entailed employing a Rink amide resin to optimize the peptide synthesis environment, along with Fmoc group deprotection, coupling of activated Fmoc-amino acids, and subsequent detachment of the peptide from the resin. It is noteworthy that amino acids with suitable protective groups were selected to ensure alignment with the 9-fluoreylmethylethoxycarbonyl/tert-butyl (Fmoc/t-butyl) approach for solid-phase peptide synthesis (SPPS). In addition, the amidation at the peptide's C-terminal is expected to diminish its net charge, potentially resulting in reduced solubility. Nevertheless, this alteration augments the peptide's stability, aligning it more closely with native proteins, thereby enhancing its biological relevance [27].
# Evaluation of Different Internal Designs of Hydraulic Nozzles under an Accelerated Wear Test
###### Abstract
[17] concluded that the mathematical model that they developed could be used to determine the wear degree of the nozzles depending on the spray distribution produced from those nozzles. According to [PERSON] [18], as nozzles start to wear, their orifices will increase in size and this will enhance the minimum flow rate required for the complete atomization of the spray. However, this increase in the flow rate (if the sprayer pump will provide it) will decrease the minimum spray pressure required for the nozzles. In work of [PERSON] [19], the main specifications applied to evaluate nozzle condition were the flow rate and the spray volumetric distribution, and then a laser technique was introduced to measure the drop size of the spray. As operating pressure affects the flow rate and the life of the nozzle (the orifice dimensions), in this study, it is used as an indicator for nozzle performance. Nozzle wear can affect the nozzle performance with time, leading to a change in the produced spray characteristics. According to the literature, this wear is a function of different factors. However, the effect of the wear on the nozzles when they have the same nominal flow rate, but are made with a different internal design, is unknown. In our work, to understand this form of wear effect, nozzles of different internal design (from the same material and manufacturer) were chosen, namely, pre-orifice, twin orifice, and twin chamber nozzles types. The objectives of this study were, firstly, to compare and evaluate the wear behavior of different internal design nozzles, and, secondly, to determine the changes in the main performance parameters of those nozzles after they had been subjected to the accelerated wear test.
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 4 Multicast Mechanism
### MJN Selection
When using the nearest MJN selection strategy, N4 selects MJN1 as the target MJN to join because the distance between MJN1 and N4 is less than the distance between MJN2 and N4. When using the minimum cost MJN selection strategy, N4 calculates the comprehensive cost of MJN1 and MJN2, respectively. We assume that the network diameter is 4, the link bandwidth is 1 Gbps, the NFT load threshold is 100, and the \(\alpha\), \(\beta\), and \(\gamma\) used in the comprehensive cost function are all 1/3. On the path from MJN1 to N4 (MJN1\(\rightarrow\)N1\(\rightarrow\)N4), we used the NFT load of N1 as PS and the link load of L2 as PT. Then, we calculated that the comprehensive cost of MJN1 is 0.7. The path from MJN2 to N4 is MJN2\(\rightarrow\)N2\(\rightarrow\)N3\(\rightarrow\)N4. Since the NFT load of N3 reaches the threshold, we used the NFT load of N2 as the PS and the link load of L5 as the PT. Then we calculated that the comprehensive cost of MJN1 is 0.45. Since \(\cos\text{t(MJN1)}<\cos\text{t(MJN2)}\), N4 chooses MJN2 as the target MJN to join.
## 2 Analytical Model
### Instantaneous Pressure
#### 2.2.1 Incident Pressure Field  
The incident pressure field results from the direct'source-to-observer' propagation of the acoustic waves, and it can be expressed as follows:  
\[p_{inc}(\mathbf{x},t)=p_{s}(r,\theta,t) \tag{2}\]  
Figure 1: Sketch of the experimental set-up, with geometrical construction of the specular reflections using source images.  
where \(p_{s}\) is the pressure resulting from the source emission alone, i.e., out of any installation effect due to the duct. In the (here assumed) absence of any flow, this incident pressure solely depends on both the propagation distance (\(r\)) and the emission angle (\(\theta\)) between the source and the observer. The propagation distance is simply the Euclidean length between the two points, which in the present case of a two-dimensional (2D) problem, is given by  
\[r=|\mathbf{x}-\mathbf{x}_{s}|=\sqrt{\left(x-x_{s}\right)^{2}+\left(y-y_{s}\right)^{2}} \tag{3}\]  
Similarly, the incident angle can be expressed in 2D as (cf. Figure 1)  
\[\theta=\frac{\pi}{2}-\left(\alpha+\beta\right) \tag{4}\]  
where  
\[\beta=Atan\bigg{(}\frac{y-y_{s}}{x-x_{s}}\bigg{)} \tag{5}\]
# Improved PPP Ambiguity Resolution with the Assistance of Multiple LEO Constellations and Signals
## 3 Results
### PPP AR with the Augmentation of Multi-Frequency LEO Observation  
With the stable UPD products of GNSS and LEO, LEO-augmented PPP ambiguity resolution can be achieved. The augmentation performance with different LEO constellations was evaluated by first introducing three types of LEO constellation consisting of 60, 192, and 288 LEO satellites, respectively. For the purpose of investigating the contribution of multi-frequency LEO observation to ambiguity resolution, three solutions, including the dual-frequency PPP float solution (Float), dual-frequency PPP fixed solution (DF_fixed), and triple-frequency PPP fixed solution (TF_fixed), were analyzed and compared. Note that in the processing of triple-frequency PPP AR, only triple-frequency LEO observations are employed, while dual-frequency GNSS observations are used.
# Adaptive Seasonality Anomaly Detection Algorithm under Intricate KPI Profiles
## 4 Results
### KPI Anomaly Detection Evaluation  
In the application of KPI anomaly detection, fragmented alarms are more practical, so the statistics in this section take the form of fragmented alarms. The fragmented alarm processing process is as follows. The abnormality in the nearby and continuous time window is deemed a hit in the abnormal monitoring procedure if an anomaly is identified in the KPI time series data at a certain point. Specifically, the fragmented alarm is shown in Figure 11. The original KPI time series data is represented by the first row of data in the graphic, and each square represents one sampling point at a time. If a point is found to be abnormal, it is given a value of 1, otherwise it is given a value of 0. The anomaly score computed by the KPI anomaly detection algorithm is in the second row of data, and data in each square is the anomaly score at the corresponding time point. The discovered findings are indicated in the third row of data, and a point is considered abnormal if the abnormal score exceeds a particular threshold. The experimental statistical results are in the fourth line of data, which are fine-tuned by fragmented alarm [13]. The adjusted statistical result are Recall = 50%, Precision = 50% and F1-Score = 0.5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(ASAD\) & **Auto-Correction** \\ \hline Accuracy Rate & 75\% & 43\% \\ Error Rate & 25\% & 57\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of seasonal length evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Average Time (s)** \\ \hline Argus & 219 \\ EGADS & 51 \\ Period & 29 \\ \(ASAD\) & **39** \\ \hline
**Algorithm** & **Clustering Accuracy** \\ \hline Period & 57\% \\ \(ASAD\) & **84\%** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Time and performance comparison.  
This section analyzes the effects of _ASAD_, Period, EGADS and Argus for KPI anomaly detection in order to assess the overall effect of KPI anomaly detection. We also utilize F1-Score as a comparative metric because it can synthetically reflect algorithm quality. Each dataset is separated into numerous samples of 1 month in length for the experiment. Argus, EGADS, Period and _ASAD_ are four algorithms that are evaluated on several samples of diverse datasets. The recall rate, accuracy and F1-scores of each algorithm on various datasets are shown in Figure 12. According to the analysis on most datasets, the F1-score of the _ASAD_ method is higher than the other three algorithms. Only on dataset E is the _ASAD_ algorithm's F1-score slightly lower than Period's F1-score.
# A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds
## 3 The Equivariant Vector Network with Self-Supervised Learning
### Network Backbone  
#### 3.4.1 Patch Generation of Point Clouds  
When generating a 2D image patch, the inherent constraints and relatively square shape of a 2D image allow for a direct division of the patch in a checkerboard-like manner. However, as a point set in 3D space, the shape of each point cloud is different, and there is no uniform size constraint; therefore, this paper uses the Farthest Point Sampling (FPS) [20] method and the K-Nearest Neighbor (KNN) [20] algorithm to divide the point cloud into patches. Given point cloud data \(X\in\mathbb{R}^{N\times 3}\) with \(N\) points, the role of FPS is to find the \(n\) center points \(p_{center}\in\mathbb{R}^{n\times 3}\) that can most concisely describe the contour of this point cloud. Based on these \(n\) center points, the KNN algorithm finds the \(k\) closest points to each center point in the original point cloud data \(X\). These point groups and the \(n\) center points together form \(n\) point patches, that is, \(P\in\mathbb{R}^{n\times k\times 3}\).  
\[p_{center}=\text{FPS}(X),\ \ p_{center}\in\mathbb{R}^{n \times 3} \tag{11}\] \[P=\text{KNN}(X,p_{center}),\ \ P\in\mathbb{R}^{n\times k \times 3} \tag{12}\]  
In these point patches, each point within the patch undergoes coordinate normalization operations with respect to the central point of the patch, resulting in a well-converged group of points. In terms of mask operation, this study masks the generated patch with the mask rate of \(m\). Visible patches \(P_{vis}\in\mathbb{R}^{(1-m)n\times k\times 3}\) will be used as input and sent into the later module, and masked patches are regarded as the ground truth for loss calculation with reconstructed patches, formed like \(P_{mask}=P_{gt}\in\mathbb{R}^{mm\times k\times 3}\). In the selection of the masking strategy, we adopt the effective random masking strategy, the same as for the MAE [23].
## 3 Results and Discussion
### Limitations  
This scoping review targets the elderly population and vulnerable people, focusing on peer-reviewed journal articles and conference papers written in English and indexed in acknowledged databases. The review excludes case studies, clinical reports, and articles that lack sufficient references to support their findings. It centers on technological advances, particularly in remote technology, within a defined time frame. It aims to provide a comprehensive overview of recent developments in remote healthcare systems and the analysis of physiological signals. Instead of addressing specific illnesses, this review seeks to identify key growth opportunities in the field, mapping technological innovations to support the design and implementation of cost-effective solutions intended to enhance the healthcare and well-being of the elderly and vulnerable populations.
# Microencapsulation of Natural Food Antimicrobials: Methods and Applications
## 4 Food Applications of Encapsulated Antimicrobials
### Cereal Products  
In the case of neglecting good practices, dough is prone to deterioration by various microorganisms in ingredients used in the formulation, equipment and process environment. Different chemical preservatives, such as organic acids and their salts (sorbates, benzoates, propionate... ), can be used for the inhibition of microbial growth in dough. However, during the last few decades, there has been a great demand for consuming food products without added chemicals [122]. The antifungal activity of pure and microencapsulated rosemary EO prepared through spray drying with modified starch-maltodext as wall materials, has been investigated in fresh dough. At least 0.7 and 1.5 log cycle reductions for total fungal count were obtained for fresh dough treated with 1.5% pure and 1.5% microencapsulated oil, respectively, after 8 days of storage at 25 \({}^{\circ}\)C compared with control product (EO-free). Microencapsulation of rosemary oil contributed to more effective and extended growth prevention of fungi for two main reasons, including the slow core release into the food system and gradual volatilization [114]. The effect of curcumin microencapsulation on the shelf-life of bread, tofu and cooked pork through spray drying with gelatin-porous starch wall materials has been studied [113]. Different concentrations of curcumin microcapsules were added to tofu, bread and cooked pork; at concentrations \(\geq\)0.035%, the complete inhibition of mold growth was obtained after 72 h (disease index of all the three food products reached zero).  
Regarding the importance of applying heat treatments in food processing, the antifungal activity of curcumin microcapsules on the mentioned products was examined after heat treatment. Despite losing some activity of encapsulated curcumin and increasing the effective concentration from 0.035 to 0.040% after applying the heat treatment, most of the microcapsules showed a high inhibitory effect and good heat stability compared to free curcumin, which lost all its activity upon heating [113].
# Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks
## 3 Results
### Results of Experimental Measurement Data
#### 3.2.2 Results Comparison of Different Fault Diagnosis Models  
Similarly, this paper compares the performance based on experimental data of SVM, Xgboost, KNN, RF models, and CNN. Figure 14 shows the results of the prediction accuracy of crack location and crack depth. The maximum accuracy of traditional machine learning methods for crack location and depth detection is 85.7% and 66.3%, respectively, while the prediction accuracy of the CNN is 91.6% and 88.2%. Compared to several other machine learning models, the CNN designed in this study has better accuracy in detecting blade crack location and crack depth.  
Figure 14: Prediction accuracy of different deep learning models.  
Figure 13: The first three orders of natural frequency of the blade at different crack depths.  
The confusion matrix of crack location classification for the test set is shown in Figure 15a. Furthermore, the classification accuracy for the blade crack failure location is high. Only part of location one (the crack in the top) is incorrectly predicted as a crack in other locations, which also contributes to its relatively low prediction accuracy. The main reason may be the small effect of crack position one on blade frequency and the small difference in vibration amplitude of the cracked blade, which also corresponds to the results in Figure 13. Figure 15b shows the density statistics of the relative errors in the test results. The bar graph represents the density of distribution within each error interval, and the curve represents the Gaussian distribution of relative errors. When the width of the curve is smaller and the centerline is closer to 0, it indicates that the predicted results are more accurate. From the figure, it can be seen that the error of most of the predicted values of the crack degree is within \(\pm\)20%.
###### Abstract  
The Caatinga dry forest encompasses 11% of the total continental territory of Brazil. Nevertheless, most research on the relationship between phenology and ecosystem productivity of Brazilian tropical forests is aimed at the Amazon basin. Thus, in this study we evaluated the seasonality of ecosystem productivity (gross primary production--GPP) in a preserved Caatinga environment in northeast Brazil. Analyses were carried out using eddy covariance measurements and satellite-derived data from sensor MODIS (MODerate Resolution Imaging Spectroradiometer, MODI7 and MODI3 products). In addition to GPP, we investigated water use efficiency (WUE) and meteorological and phenological aspects through remotely sensed vegetation indices (NDVI and EVI). We verified that ecosystem productivity is limited mainly by evapotranspiration, with maximum GPP values registered in the wetter months, indicating a strong dependency on water availability. NDVI and EVI were positively associated with GPP (r = 0.69 and 0.81, respectively), suggesting a coupling between the emergence of new leaves and the phenology of local photosynthetic capacity. WUE, on the other hand, was strongly controlled by consecutive dry days and not necessarily by total precipitation amount. The vegetation indices adequately described interannual variations of the forest response to environmental factors, and GPP MODIS presented a good relationship with tower-measured GPP in dry (R\({}^{2}\) = 0.76) and wet (R\({}^{2}\) = 0.62) periods.  
land surface phenology; vegetation dynamics; carbon exchange 2022143955[[https://doi.org/10.3390/rs14163955](https://doi.org/10.3390/rs14163955)]([https://doi.org/10.3390/rs14163955](https://doi.org/10.3390/rs14163955))
## 3 Results
### Stability Test of MNDVI Estimation for \(f\)PAR
#### 3.3.2 Accuracy Assessment Based on Different Lighting Conditions  
When estimating the vegetation index _f_PAR, the changing light conditions during crop growth, including solar zenith angle and light intensity under different weather conditions, play a crucial role in determining the stability of the correlation between the vegetation index and _f_PAR. These light conditions include solar zenith angle and light intensity under different weather conditions, which can affect the correlation between _f_PAR and other indicators. Therefore, when using MNDVI to estimate _f_PAR, it is necessary to validate the accuracy of this index on different observation dates. To validate the estimation accuracy of MNDVI on different observation dates, we calculated the corresponding estimated values using Equation (3). These estimates represent the _f_PAR at different observation dates. Our study aims to demonstrate the high stability of using MNDVI for _f_PAR estimation during different phenological stages and under varying light conditions. The accuracy validation results are shown in Figure 4, which contributes to evaluating the applicability of this method under different environmental conditions.  
Figure 3: Validation of estimation accuracy of index model based on MNDVI in different varieties.