esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Unmanned aerial vehicle (UAV) communications play an important role in modern network infrastructure, particularly in flying ad hoc networks (FANETs) integratedwith cell-free massive multiple-input multiple-output (CF-mMIMO) [1; 2]. These modern communications setups rely on effective routing methods which have a direct impact on performance and efficiency. Recently, security has emerged as a significant concern, warranting special attention to prevent potential breaches [3; 4; 5; 6; 7; 8].  
Given its airborne position, the UAV can cover large areas of the ground, providing superior network coverage over traditional ground-mounted access points (APs) [9; 10]. Therefore, clustering or member management is very important, ensuring optimal utilization of the UAV's wide range of capabilities and maintaining network efficiency [11; 12]. One of the major security threats to these networks is wormhole attacks which are characterized by capturing enemy nodes and tunnelling packets to other locations on the network [13]. This misleading tactic breaks regular data transmission pathways and modifies network topology causing substantial network performance and reliability disruptions. Consequently, understanding these attacks and developing robust defenses against them is essential to ensure secure and reliable UAV communications.  
A multicast routing protocol is a key technique in ensuring reliable data communication to multiple destinations [14]. The multicast routing protocol can select the best next node to establish the optimal route from the source to the multiple destinations [15]. Particularly in the FANET environment, UAVs move dynamically so that there can be a risk of broken connectivity and attack from eavesdroppers [16]. The authors of [16] proposed a method to detect a black hole in a short time. The authors of [17] studied a hybrid authentication scheme with a digital signature to improve the security performance in the UAV and ground node against wormhole attacks. However, the proposed method has a high processing time to authenticate the secure node.  
In this context, deep learning models present an intriguing opportunity [18; 19]. When the deep learning models are applied to FANETs, these models can offer sophisticated route verification mechanisms. These mechanisms predict the next secure node in the route, ensuring that the chosen path is free from wormholes before initiating data transmission. The use of deep learning to improve security in UAV communications forms the basis of this paper.
# Effect of Local Gas Flow in Full Penetration Laser Beam Welding with High Welding Speeds
## 3 Results
### 3.3. _Spatter Reduction by Use of Local Gas Flow_
#### 3.3.6 Effect on Weld Seam Geometry  
After analyzing the effect of the gas flow on the flow field conditions, spatter formation mechanism as well as static and dynamical keyhole dimensions, the effect of the gas flow was characterized on the weld seam geometries in order to determine a change in energy absorption and melt flow conditions (Figure 10). Based on cross-sections, the seam width was measured at the bottom side as well as inside the specimen (two thirds of specimen thickness).  
Independent on flow rate, the use of the gas flow resulted in a local widening of the seam top. While the seam width of the top side was widened by up to a factor of 1.4, the inner weld seam showed constant dimensions. Based on the reduced loss of mass, the top side was characterized by a homogenous weld seam formation. By contrast, a change in welding speed showed no effect at all. The found seam widening can be attributed to an affected melt pool temperature. Following, the heat balance is affected by heat dissipation due to forced convection. In contrast to this, the contra-orientation of the gas flow and the escaping metal vapor on the top side of the specimen results in the formation of a heat accumulation due to an impeded degassing. Subsequently, by changes in melt pool temperature, the flow conditions of the melt pool are affected by varying flow fields well as by the temperature-dependency of rheological quantities (e.g., viscosity, plasticity). According to the law of continuity, the flow velocities of the keyhole surrounding melt can be reduced, which can contribute to less spatter formation.
## 3 Results  
Figure 2 shows longitudinal OCT monitoring of the developing retina, and the means and standard deviations of the layer thicknesses are summarized in Table 1. After eye opening, the retinal layer thickness kept changing until P28. Figure 2A shows the enface image of the dorsal quadrant, which indicates the location of the B-scans in Figure 2B. Figure 2B shows the representative B-scans used for layer thickness measurement. The red arrows indicate an HRL, which was unambiguously observed between P14 and P17 and gradually disappeared until P28. The green, purple, and orange two-headed arrows represent the thicknesses of the INL, outer nuclear layer (ONL), and external limiting membrane (ELM) to retinal pigment epithelium (RPE). Figure 2C shows the reflectance profiles of the B-scans in Figure 2B. The results show that the HRL was obvious before P17, but gradually disappeared after P21. ELM and ellipsoid zone (EZ) were very dim at P14, but the reflectivity gradually becomes stronger with aging. The interdigitation zone (IZ) (yellow arrows, Figure 2C) was not observed before P17 but gradually becomes distinguishable after P28. Figure 2D shows the distribution of the HRL thicknesses over the whole retina. The result shows that the HRL exists all over the retina at P14 to P21, with the center region slightly thicker than the peripheral region. Figure 2E shows the layer thickness changes with ages from P14 to P56. The inner retina layer thickness change is shown in Figure 2(E1). The NFL thickness decreased from P14 to P21 and then remained the same. The HRL thickness kept decreasing after eye opening and disappeared at about P28. The IPL thickness did not change significantly between P14 to P56 days. The INL thickness kept decreasing after eye opening until P28. Figure 2(E2) shows layer thickness change in the outer retina. The ONL thickness kept decreasing until P28. On the contrary, the ELM-RPE thickness kept increasing until P28. Figure 2(E3) shows the thickness change of the inner retina, outer retina, and the whole retina. The thickness of the inner retina and the whole retina kept decreasing until P28. On the contrary, the thickness of the outer retina slightly increased after eye opening. Figure 3 shows a comparative volumetric analysis of the inner retina of a P17 mouse (Figure 3A) and a P56 mouse. After frame registration and super averaging, the detailed structure was disclosed. In the OCT B-scan of the P17 mouse (Figure 3(A1)), individual RGCs were observed in the HRL. The stratification of the IPL of the P17 mouse was not clear; however, it was clearer for the P56 mouse (Figure 3(A2,B2)). The nerve fibers of P17 and P56 were clearly observed (Figure 3(A3,B3)). The RGCs were observed in the enface images (Figure 3(A4,B4)). For the P17 mouse, the RGCs were located in the HRL; thus, the individual RGCs were clearly observed. For the P56 mouse, the RGCs were closely packed with the NFL and the IPL, and the contour of RGCs was not clear.
## 3 Results and Discussion
### Phenolic Compound  
Blackcurrant fruits contain phenolic compounds, such as phenolic acids, flavonoids, flavan-3-ols (catechins) and tannins, with potential health-promoting properties, making them a functional food ingredient. According to the obtained results, the average content of total polyphenols is significantly higher for organic currents (189.15 mg/100 g f.w.) than for conventional ones (128.68 mg/100 g f.w.). Differentiation of total polyphenols content is also shown for individual cultivars of which the highest polyphenols content is observed for the 'Titania' cultivar (198.45 mg/100 g f.w.) and the lowest for the 'Tiben' cultivar (166.10 mg/100 g f.w.). Similar results were obtained by [PERSON] et al. in (2016), in which they analyzed three varieties of blackcurrant ('Ben Hope', 'Ben Alder', 'Titania'), both from organic and conventional cultivation. They found that the average content of polyphenols for fruits from organic cultivation was 183.1 mg/100 g d.m., which is higher compared to conventional ones 154.3 mg/100 g d.m. Moreover, a significant influence of the cultivation method on the polyphenol content was determined. Similar to our own study, the highest polyphenol content was found in organic 'Ben Alder' variety (225.3 mg/100 g d.m.). As shown by [PERSON] et al. (2010), the flavonoid content for blackcurrant varieties ranged between 9.6 mg/100 g f.w. and 21.6 mg/100 g f.w., while the average content of these compounds was 14.32 mg/100 g f.w. As shown in Table 2, the content of phenolic acids, flavonoids and flavonoids is significantly different both for individual varieties and cultivation methods. Relationships are also observed (Table 2, \(p<0.001\)) for the magnitude of variance for the year of cultivation vs. cultivar, the year of cultivation vs. cultivation methods, and for phenolic acids, flavonoids and flavonoids as well as for the year and method of cultivation and cultivar. Detailed analysis (Table 3) of individual phenolic acids and flavonoids indicates variations in the content of chlorogenic acid, kaempfer-3-O-glucose and myricetin for individual varieties and cultivation systems, but do not show a relationship related to year of cultivation. This is confirmed via analysis of variance, indicating the relationship between the year vs. currant variety, year vs. cultivation system and varietyvs. cultivation system. Quercetin-3-O-rutinoside and quercetin-3-O-glycoside show a relationship between the varieties and cultivation system, which is not found for caffeic acid, ferulic acid and kaempfer. All analyzed phenolic acids, flavanols and flavonoids show a distinct relationship between variants for the year of cultivation vs. cultivation system and variety. Similar results were obtained by [PERSON] et al. in [16] in which currants from conventional cultivation contained more total flavonoids (4.10 mg/100 g d.m.) than organic currants (3.99 mg/100 g d.m.), and there was observed statistical significance in the content of flavonoids for individual varieties. Moreover, the 'Ben Alder' variety can be characterized by the highest concentration of these compounds.
## 5 Discussion  
The proposed resonant converter with ESC-based control is inherently capable of operating across a range of voltages and load impedances, enabling compatibility with typical battery voltage ranges encountered in lithium-ion and lead-acid battery systems. However, as ESC is focused on maximizing power extraction, it does not enforce the Constant Current or Constant Voltage charging profiles required for battery health and safety. These profiles must be ensured via integration with a battery management system (BMS), or through an outer loop, supervising and limiting voltage and current levels. Moreover, the converter can adapt to changes in load impedance, such as the connection or disconnection of parallel battery units. Such events may cause shifts in the converter's operating point, but the ESC algorithm dynamically adjusts the switching frequency to track the new maximum power point.  
Robustness under temperature and irradiance variations is critical in PV systems and has been validated under both temperature and irradiance disturbances. The simulation results demonstrate that the proposed system tracks the maximum power point with a less than 3% steady-state error and a dynamic response time of under 50 ms following irradiance changes, and less than 150 ms after temperature changes. As a model-free algorithm, ESC adapts well to these conditions. Compared to conventional MPPT methods, ESC offers advantages in systems where linearization is difficult, such as PFM converters. A general comparative radar diagram is presented in Figure 20, highlighting the key differences between the proposed SRC converter and other topologies for MPPT battery charging applications.  
In addition, Table 4 provides a quantitative comparison that highlights the system's unique benefits and implementation simplicity compared with other topologies reported in the literature.  
The ESC algorithm demonstrated very good tracking of the maximum power point in the simulation results. One of its main advantages is that it avoids voltage overshoot because of its smooth and gradual response. This makes it especially suitable for PV applications where stable voltage is essential. Another benefit is that all components of the ESC algorithm, such as the low-pass filter, high-pass filter, and integrator, operate in continuous time, which gives it the flexibility to be used in either analog or digital form, unlike many other MPPT methods that rely solely on digital control. Table 5 presents a comparison of the ESC method with other MPPT techniques based on four key factors: the  
Figure 20: Comparison between capacitive isolated series resonant converter and other used topologies.  
computational effort, cost of implementation, need for a system model, and suitability for continuous-time (analog) implementation.
# Reservoir Computing Based Echo State Networks for Ventricular Heart Beat Classification
## 2 Materials and Methods
### ECG Signal Preprocessing
#### 2.2.2 High Frequency component (Peak) Detection  
This study carried out the second step peak detection which was very useful for further steps.  
The High-frequency components are detected by applying the modified Pan Tompkins algorithm [44]. The algorithm consists of the following steps:  
Figure 2: (**a**) Representation of Noisy ECG signal; (**b**) Representation of Filtered ECG signal.  
In the first step, this study used a block of the differentiation equation to obtain the high slope values; secondly, the output values of the signals were squared to extract the R-peaks; in the last step, this study applied the summation on all values of R-wave slope. The values related to the R-wave, for instance, R-location and amplitudes of R-wave were stored in the \(\mathbf{R^{nXm}}\) matrix. Figure 3 represents the example of detecting the R-peaks based on the Pan Tompkins algorithm. The reason for applying this modified Pan Tompkins algorithm is that it can easily adapt the variation of signal changes and extract the high-frequency component in an efficient manner.
# Power Prediction of Bifacial Si PV Module with Different Reflection Conditions on Rooftop
## 3 Proof of Theoretical Approach
### Application Test 1  
In the previous experiment, when the solar irradiation amount was 700 W/m\({}^{2}\) or more, the error rate was low in predicting the backside irradiance, but, when the solar irradiation amount was 700 W/m\({}^{2}\) or less, the error rate was large. The back side irradiance shows a large influence by the total solar irradiation. Accordingly, the outputs of the bifacial module with a white back sheet and one with a transparent back sheet in the same environment were compared.  
Figure 20 shows the module used in the experiment having 72 p-type cells and maximum power of the front side under \(STC\) is 340.4 W, while the one of rear side is 221.1 W, showing 17.73% of module efficiency.  
Figure 19: The difference in the values of the irradiance measured through the rear pyranometer and predicted through the modeling.  
On one day in August, the output of a bifacial PV module was measured and predicted with a white painted backside environment shown in Figure 7. The maximum output power of the module in _STC_ condition is 340 W.  
It is very important to notice that the maximum power was 439 W instantly with a white painted reflection condition. Figure 21 shows 66 data measured every 5 min from 11:03 to 16:28 on one day in August.  
Figure 21: Theoretical prediction and measured data at 5-min intervals from 11:03. to 16:28 on 25 August 2017 (white painted back ground).  
Figure 20: The 72-cell Si bifacial photovoltaic (PV) module used in application test.  
From the results in Figure 21, when the irradiation amount on bifacial module is more than 700 W/m\({}^{2}\), majority of the power induced is found to come from the back reflection only which could be also confirmed from a previous study [29]. However, when the irradiation is less than 700 W/m\({}^{2}\), additional factors, such as scattered irradiance from surroundings, should be considered due to the decrease of rear side reflection. It is very interesting to note therefore that theoretical prediction would lead to an increase of an error rate (average 10.6%) at low irradiation shown in the figure without counting the additional factors, while average error rate of 3.1% was calculated at the solar irradiation mostly exceeding 1000 W/m\({}^{2}\) for \(\pm\) 1 h based on noon.  
From the result, it is important to note that, if the environment using the bifacial module is optimized for back side reflection, the maximum efficiency would increase more than 32%, compared with the power at _STC_, which is measured in this experiment.
# Transcriptional Analysis of Masson Pine
## 4 Discussion  
### Effects of Elevated CO\({}_{2}\) on Masson Pine Energy Metabolism  
The results of this study showed that in the major energy metabolism pathways of Masson pine, the expression levels of most genes involved in photosynthesis, including light and dark reactions, showed a significant downward trend under CO\({}_{2}\) stress. We speculate the main cause of this phenomenon  
Figure 5: The expression changes in the 9 randomly selected genes were determined using quantitative real-time PCR (qRT-PCR) results and sequencing data. The x-axis represents different processing times, and the y-axis represents changes in gene expression under CO\({}_{2}\) stress. The data show the fold change in the expression of each gene under high CO\({}_{2}\) relative to control conditions. Error bars represent standard deviations. Red indicates the RNA-sequencing results under the TPM (transcripts per million reads) algorithm, and blue indicates the qRT-PCR results.  
should be the decrease in the expression of Rubisco, which is a key enzyme in photosynthetic carbon assimilation, directly affecting the photosynthetic efficiency of Masson pine [36]. Combined with the up-regulated of key genes in the carbohydrate metabolism during the same period (Figure 3B), it leads to the activation of its signaling pathway, which inhibits the expression of _Rbc_S (Rubisco small subunit) and other photosynthesis-related protein-coding genes through hexokinase transmission, thereby affecting the photosynthetic efficiency [37].  
Significantly, previous studies [38; 39] have shown the expression of _Rubisco_ will show an up-regulated trend under the high CO\({}_{2}\) treatment condition. But the results of this experiment are the opposite. This may be because the expression of carbon sequestration gene is greatly affected by the leaf age [40]. The photosynthetic acclimation of coniferous leaves in one-year needles under high CO\({}_{2}\) treatment was more obvious than that in mature coniferous leaves [41]. According to the experimental results, the expression of each gene in photosynthesis showed a downward trend when the sample was analyzed at the shortest detection time (6 h). Therefore, we speculate the photosynthetic acclimation should be completed within 6 h.  
However, in other energy metabolism pathways of Masson pine, the expression levels of various genes under high CO\({}_{2}\) stress generally showed an upward trend. [PERSON] et al. [42] obtained similar results in an experiment on the effect of CO\({}_{2}\) on rice, but the difference in gene expression was inferior to our study, possibly because rice belongs to herbage, and the adaptability of rice to CO\({}_{2}\) stress is higher than that of Masson pine. Previous studies found that a high concentration of CO\({}_{2}\) promoted the production of a large amount of sucrose in leaves, and the increase in sucrose concentration induces the expansion of glycolytic flux in plants and simultaneously increases the distribution of carbon in the organic acid and TCA cycles, thereby regulating the increase in related gene expression [43].
## 2 Materials and Methods  
### Data Sources and Reprocessing  
#### 2.1.1 SIF Datasets  
The GOSIF (Global OCO-2 SIF) is a reconstructed SIF product based on Orbiting Carbon Observatory-2 (OCO-2) observations, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation data, and meteorological reanalysis data. The GOSIF datasets from 2003 to 2016 were downloaded from [[http://globalecology.unh.edu/](http://globalecology.unh.edu/)]([http://globalecology.unh.edu/](http://globalecology.unh.edu/)) (accessed on 10 February 2021), which were globally spatio-temporal continuous at 0.05\({}^{\circ}\) and 8-day resolution derived with a machine learning algorithm trained with OCO-2 SIF [28]. The datasets had a good performance validated by original SIF observations (RMSE = 0.07 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) and also showed a good correlation with the in-situ GPP over flux sites (R\({}^{2}\) = 0.73, \(p\) < 0.001) [28].
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 4 Simulation
### Simulation Result  
The simulation results using the implemented simulator and algorithms are illustrated in Figure 15. The first part is shown in Figure 15a-d, and the second part in Figure 15e-h. The mobile base moves to the selected position, as shown in Figure 15a, and then the manipulator harvests the target fruits, as shown in Figure 15b-d. The same procedure was repeated for other targets, as shown in Figure 15e-h. The generated paths for the UGV are shown in Figure 16. In each simulation part, the initial positions are represented in red, the selected final positions from IRM in green, and the mapped obstacle trees in brown circles. The intermediate paths from the initial position to the selected final position were generated by MPAPF and are represented in blue. The computation times for position and orientation selections were 2.064 and 1.831 s for the first and second parts, respectively. The path planning computation times were 0.114 and 0.04 s for the first and second parts, respectively. Ten and six path points were generated, and the paths were set up for the mobile base to pass through the points sequentially without generating trajectories.  
The harvesting paths of the manipulator for each part of harvesting are shown in Figure 17. The paths for harvesting three fruits in each part are shown in red, green, and blue, respectively. The target fruits are represented by the orange circles, while other obstacle fruits are represented by cyan circles. Each generated path ensures collision-free reaching of the target fruits without movement of the base. The computation times for the paths were 2.985, 2.623, 3.923, 2.815, 2.847 and 4.054 s, and the number of path points  
Figure 16: Path of mobile base: (**a**) first part; (**b**) second part.  
Figure 15: The progress of the harvest process simulation: (**a**) approach from initial position to selected location; (**b**–**d**) harvesting fruits; (**e**) approach to another selected location; (**f**–**h**) harvesting fruits.  
generated were 84, 74, 110, 84, 82 and 112 for the path planning of each of the six target fruits. The joint angles of the manipulator are shown in Figure 18, where the black and blue solid lines represent the joint angles for the first and second part, respectively, and the red dashed line indicates the joint limits. All the paths were generated to ensure that the joint angles did not exceed their limits.  
Figure 17: Harvesting paths of the manipulator for each simulation part.  
Figure 18: Joint angles of manipulator at each part of harvesting.