esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Performance Analysis by Video Type
#### 4.2.3 Dynamic Background Clusters (DBC)  
This type of video features a dynamic background that complicates the detection of drones; in this instance, the wind interacts with the trees. This group contains a total of 14,400 images, thus rendering it the fourth smallest group.  
GELAN clearly stands out in this category, exhibiting significantly higher accuracy, which suggests that its architecture is better suited to handling dynamic backgrounds. DETR also demonstrates acceptable performance; however, it is inferior to GELAN (see Table 6). YOLO, due to its emphasis on speed, encounters difficulties in these scenarios as evidenced by its lower accuracy. Figure 6 presents examples of the results in this category.  
Figure 5: Detection results for target scale (TS).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **IoU Small** & **IoU Large** & **AP** & **F1-Confidence** \\ \hline YOLO & 73.33\% & 79.5\% & 73.5\% & 77\% at 0.093 \\ \hline GELAN & 76.74\% & 80.4\% & 82.5\% & 84\% at 0.426 \\ \hline DETR & 80.66\% & 81\% & 80.3\% & 82\% at 0.321 \\ \hline ViTDet & 62.41\% & 70.6\% & 64\% & 66\% at 0.122 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for video type target scale (TS).
## 4 the Proposed Secure Multicast Routing Protocol: DLSMR  
In this section, we describe the structural characteristics of our network, the specifications of the UAV and ground node, and the existence and implications of wormhole attacks within the UAV layer.  
Our network consists of two types of nodes \(\mathcal{K}=\{F_{k}\}_{k=1}^{K}\) represented by a set of flying nodes \(\mathsf{F}\) and \(\mathcal{L}=\{G_{I}\}_{I=1}^{L}\) represented by a set of ground nodes \(\mathsf{G}\). These nodes are organized into clusters, \(\mathsf{CL}_{c}\triangleq\{c=1,2,\cdots,C\}\), through a top-down methodology that enhances network scalability and performance. UAVs have a distinct set of characteristics that are crucial for our protocol. These flying nodes are mobile and have the ability to cover large ground areas, providing superior network coverage compared to traditional ground nodes. However, their movement speed, direction, and remaining energy must be accurately tracked to maintain network efficiency and stability. In contrast, ground nodes serve a vital purpose as points of connection. They receive, process, and transmit data to UAVs, playing an important role in establishing and maintaining the network's functionality. One of the major security threats in our setup is the presence of wormhole attacks within the UAV layer. These attacks involve malicious nodes that tunnel packets from one location to another within the network. This deceptive maneuver disrupts regular data transmission pathways and modifies network topology, causing significant disturbances to the network's performance and reliability. To counteract these challenges, we propose a novel DLSMR protocol and a TD-PSO-based clustering protocol. The DLSMR protocol is designed to predict secure routes and avoid wormhole nodes, thereby securing data transmission. Concurrently, the TD-PSO clustering protocol groups UAVs and ground nodes into clusters based on their positions, speed, direction, and remaining energy, providing stable connectivity and improving overall network performance. Through these techniques, we aim to build a secure, scalable, and efficient communication network that effectively counteracts wormhole attacks while satisfying the unique characteristics and needs of UAVs and ground nodes.
# Graphene-Based Cylindrical Pillar Gratings for Polarization-Insensitive Optical Absorbers
###### Abstract  
In this study, we present a two-dimensional dielectric grating which allows achieving high absorption in a monolayer graphene at visible and near-infrared frequencies. Dielectric gratings create guided-mode resonances that are exploited to effectively couple light with the graphene layer. The proposed structure was numerically analyzed through a rigorous coupled-wave analysis method. Effects of geometrical parameters and response to the oblique incidence of the plane wave were studied. Numerical results reveal that light absorption in the proposed structure is almost insensitive to the angle of the impinging source over a considerable wide angular range of 20\({}^{\circ}\). This may lead to the development of easy to fabricate and experimentally viable graphene-based absorbers in the future.  
Keywords:diffraction grating; guided mode resonance; graphene; absorption +
Footnote †: journal: Applied Physics Letters
## 5 Limitations and Lessons Learned  
The main limitations of this study were sample size and follow-up time frames which limits the generalizability of the results. However, this was a feasibility study to inform a larger project, the study size was determined primarily by the funding timeframe of 12 months.  
Although it was the intention of this study to recruit fifty percent Aboriginal people due to previous ACS admissions the majority who presented during our limited recruitment period were ineligible. The lesson learned from this was that a longer recruitment period would be required for future studies.  
Managing Big Data from the tracking apps was a challenge with thousands of data points delivered daily and a large amount of research funding consumed to manage and prepare these data for analysis. A lesson learned from this experience would be that for future similar studies the budget must include funding for a data manager whose role is to solely tend to Big Data management. Logistical issues also occurred in managing an individual email account for each phone/participant from the GoogleFitTM app.  
Some mobile phones exhibited poor battery life and some participants forgot to charge the phones nightly; this was remedied by the addition of a nightly text reminder service.  
Weekly follow-up calls were also a workload burden even for this small group. Since the apps were installed on mobile phones, we would recommend text messages communication for future studies.  
Due to the co-location of many community health services data may have overestimating access to cardiac-related healthcare services.  
Most data points were captured by GPS. Rural participants had a higher proportion of network-capture than metropolitan participants due to line of sight from their location to the mobile phone tower.  
GPS tracking devices did not differentiate travel by vehicle or educate or foster engagement with the tracking process.  
Both apps (FollowMeeTM and GoogleFitTM) were off the shelf and offered limited customizability; furthermore, the FollowMeeTM app was prone to crashing several times a week for some users.  
Finally, the naming of this study was a lesson learned. The original title given to the study was \"The Tracker Study\". Early in the recruitment phase we were informed that a \"Tracker\" is a euhemism for ankle bracelet home detention devices. After consultation with the Wardliparringga Aboriginal Research Unit the study title was modified.
## 6 Examples
### Single-DOF System--Maxwell Model of Viscoelastic Element  
The system under consideration is a single-degree-of-freedom system, as depicted in Figure 9, where the viscoelastic element is described using the Maxwell model. The presented method requires the equations of motion to be formulated in a way that describes the relationship between the central value of the parameter and its uncertainties, as outlined in Equation (31). In the case of the Kelvin model, as discussed in the previous section, achieving this is relatively straightforward. However, for the Maxwell model, it is necessary to use a representation involving an internal variable indicated in Figure 9 as \(q_{d}\) ([PERSON] et al. [52]). The equations of motion then take on the following form:  
Figure 8: Displacement plots—case 4: (**a**) real part; (**b**) imaginary part.  
Figure 7: Displacement plots—case 3: (**a**) real part; (**b**) imaginary part.  
Figure 11: Displacement plots—case 2 without transformation (34): (**a**) real part; (**b**) imaginary part.  
Figure 12: Displacement plots—case 1 with transformation (34): (**a**) real part; (**b**) imaginary part.  
Figure 13: Displacement plots—case 2 with transformation (34): (**a**) real part; (**b**) imaginary part.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Damping Coefficient \(\mathbf{c_{1}}\)** & **Fractional** & **Natural Frequency** & **Nondimensional** \\  & **[Ns\({}^{\alpha}\)/m]** & **Order \(\alpha\)** & **\(\omega\) [rad/s]** & **Damping Ratio \(\gamma\) [-]** \\ \hline
1 & 120 & 1.0 & 5.8398 & 0.0952 \\
2 & 80 & 0.8 & 5.4130 & 0.0756 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of parameters for example 6.2.  
The results are calculated using three methods: vertex, DIM, and the presented method.  
In the cases considered without transformation (34), the results from the presented method and the DIM are almost identical, leading to overlapping plots in most instances. However, it is important to note that there is an overestimation, particularly evident in the example shown in Figure 10. In this case, the plots do not resemble the results from the vertex method at all, indicating complete inaccuracy. On the other hand, applying transformation (34) brings the results obtained using the fixed-point iteration method closer to those from the vertex method, leading to a significant reduction in overestimation.  
Both in examples 6.1 and 6.2, the results are magnified for the resonance zone, as the outcomes in this zone are significant from an engineering perspective. The differences visible in the plots are thus emphasized through magnification. Based on the presented examples, it is also noticeable that the presented method provides more accurate results when the nondimensional damping ratio of the analyzed system is higher.
### General Characteristics of Low-Temperature Heating Systems  
Low-temperature heating systems have been a subject of extensive research during the last two past decades. The simulations and laboratory measurements have shown that this heating system is characterized by:  
\(\bigcirc\) Higher pumping energy need compared to high-temperature systems [17].  
\(\bigcirc\) Higher heat output sensitivity due to variations in water flow rates compared to high-temperature systems [17].  
\(\bigcirc\) Ability to provide better or the same level of thermal comfort as traditional high-temperature systems [20].  
\(\bigcirc\) Higher share of thermal radiation (up to 50-70%) compared to traditional systems, and high levels of perceived thermal comfort and indoor air quality [19; 21; 22].  
\(\bigcirc\) Uniform heat distribution, low temperature stratification, low indoor air turbulence and low radiant temperature asymmetry inside the served rooms [23].  
Lower heat losses in distribution networks and more efficient use of heat pumps, low-grade waste and renewable heat compared to traditional high-temperature systems [24; 25].  
Reduced primary energy use (up to 12%) compared to traditional high-temperature systems [26; 27].  
It should be noted that previous studies seldom explicitly evaluated the importance of building heating demand and efficiency of entire system on ability of a heating system to be operated with low-temperature water supply. Therefore, one of the goals of this study was also to explore and rate the importance of these two important preconditions.
## 4 Transport of TIAs in and between Organs, Tissues, Cells and Subcellular Compartments  
Tissue, cell-specific and multi-organelle-participated synthesis of specific TIAs have been extensively studied with various techniques, including in situ hybridization, immunoblot, transcriptomic analysis and GFP-fusion of metabolic enzymes or transporters. Highly complicated regulation of various regulatory and structural genes, translocation of metabolic enzymes and transport of metabolic intermediates through different tissues, cells and subcellular compartments are required for efficient biosynthesis of TIAs upon various hormonal and environmental cues [49]. Three modes of transport of alkaloids in plants have been reported: inter-organ, inter-cellular and intra-cellular [50].  
The typical inter-organ alkaloids transports are for berberine and nicotine: berberine is synthesized in the root of _Coptis japonicais_ and then transported to the rhizome through a long distance [51], nicotine is produced only in the root of _Nicotiana tabacum_ and transported to the leaves of the plant via the xylem [52]. In _C. roseus_, synthesis of some TIAs such as vindoline and catharanthine, mainly takes place in young leaves and stems, whereas synthesis of others such as ajmalicine and serpentine mainly occurs in roots. They also display complex inter-cellular and intra-cellular transport.
Structure and Density of Sedimentary Basins in the Southern Part of the East-European Platform and Surrounding Area  
[PERSON]  
1 German Research Center for Geosciences (GFZ), 14473 Potsdam, Germany; [EMAIL_ADDRESS]  
[PERSON]  
2 Geophysical Center of the Russian Academy of Sciences (GC RAS), 119296 Moscow, Russia; [EMAIL_ADDRESS] (A.G.); [EMAIL_ADDRESS] (A.O.); [EMAIL_ADDRESS] (R.I.K)  
[PERSON]  
2 Department of Mathematics, University of California, Berkeley, CA 94063, USA;  
papers [3; 4]. [PERSON] [3] demonstrated a detailed map of the sedimentary thickness for approximately the same area, however, this map is still based on old compilations, chiefly [5], which was also used in other studies (e.g., [6]). The map of Bronguleev [5] is obtained by a statistical analysis of the relationship between surface topography/bathymetry and existing seismic determinations of the basement depth measured until mid-70s, which was performed in a sliding window [7]. Obviously, this map might be strongly biased. Later on, this model was also incorporated in the generalized models of the European crust with the resolution \(1^{\circ}\times 1^{\circ}\)[8; 9]. Another example of the use of this model for studying of the lithosphere structure of the European sedimentary basins is presented in [4]. The deep structure of the Greater Caucas area was studied using microseismic sounding data combined with the previously obtained data from other geophysical methods [10].  
New data and interpretation techniques provide a possibility to revisit the problem of the sedimentary structure in the southern part of EEP. First of all, these are the new gravity field models, which are based on a combination of the results from recent satellite missions [11] and high resolution ground observations, e.g., [12]. Based on the gravity field, it has been developed a method based on the analysis of decompensative gravity anomalies, which was successfully used last years for studying of sedimentary basins in the Middle East and Antarctica [13; 14]. In this paper, we employ this method to improve existing models of the sedimentary cover (thickness and density) in the southern part of the EEP and adjoined basins approaching the Alpine Mediterranean fold belt.  
Figure 1: Study area (topography/bathymetry). Here and on all the subsequent maps the following abbreviations are used for the following sedimentary basins: PD—Pripyat depression basin, KMD –Kuma-Manych depression, KD—Karkinit depression, IKB—Indolo-Kuban basin, TCF—Terek–Caspian Foredeep, BSD—Black Sea Depression (lowland), FDB—Fore–Dobrogea depression, PCF—Pre-Carpathians foredeep.
# A Review of Unmanned Aerial Vehicle Based Antenna and Propagation Measurements
## 2 UAV-Based Measurements
### UAV-Based Far-Field Measurements
During the outdoor measurements, the UAV and AUT were maintained at 350 and 750 m, respectively. The TX antenna mounted on the UAV was supported by a gimbal, which was used to detect radiation patterns in the elevation plane, and the rotation of the UAV around the parabolic antenna was performed to measure the radiation patterns in the azimuth plane. All the measurements were performed at 14.5 GHz. During UAV measurements, errors may occur because of external winds, reflections from surfaces, and misalignment between the probe and the AUT caused by UAV propeller vibrations. To minimize these errors, various methods are employed, such as measurement of the AUT under additional conditions, such as rotating the device at 180\({}^{\circ}\), conducting measurements with different separation distances, and taking multiple measurements at a time in one cut and averaging them. Figure 5 illustrates how UAVs can measure the performance of antennas mounted on ships. In [28], a UAV carrying a vertically polarized ground plane monopole antenna and three vertical radials was used as an RX antenna to measure an X-band vertically polarized reflector antenna mounted on a ship that resonates at 9.5 GHz. Throughout the UAV flight,  
Figure 4: UAV–based in situ measurement for a parabolic reflector antenna system. Figure 3: Conventional elevated slant test range. the antenna mounted on the UAV is directed towards the AUT; however, the variance in the pitch and roll axes while the UAV is in motion creates a polarization mismatch. Based on the experiments conducted in [28], a loss of 0.2 dB is observed in the measured radiation pattern due to polarization mismatch. Traditionally, to measure the performance of an antenna installed on a ship, an RX antenna connected to a spectrum analyzer is required to collect the signals. The RX antenna is placed at the ground level on the shore. The reflector antenna, which is the AUT installed on the ship, continuously transmits signals. To measure the radiation pattern of the AUT in the azimuth plane, the ship carrying the reflector antenna should follow a circular trajectory in the sea, while the receiver collecting the signals is static at ground level. Conversely, for UAV-based measurements, an SDR mounted on the UAV is used to receive the signals. A reflector antenna installed on the ship transmits signals. Here, the ship carrying the TX is static, and the UAV carrying the RX antenna follows a circular trajectory with a constant radius around the ship situated at the center of the circle. When measuring an antenna installed on a ship, it is essential to meet specific criteria to ensure that there are no losses due to polarization mismatches [29] or multipath reflections from seawater [30]. Considering that the AUT has a maximum diameter of 1 m and operates at 9.5 GHz, it is imperative to maintain a far-field distance (Fraunhofer distance) greater than 60 m, and no obstacles should block the first Fresnel zone between the ship and the UAV.
## 3 Results and Discussion
### Spatial Coherence Trends in MLT Images of a Homogeneous Phantom
Therefore, by merging the information derived from the plots in Figures 2 and 3, we can observe that in all cases SC decreases when the lag between backscattered signals increases; this decreasing trend only follows a triangular pattern in the SLT case, when the pulse-echo beam is approximately a \(sinc^{2}\), while a sort of damped oscillatory behavior is shown in MLT imaging, as expected because of the more complex beam shape obtained when multiple beams are transmitted simultaneously (cf. Figure 1). Furthermore, Figure 2b,c and Figure 3b,c demonstrate that it is the angular distance between the multiple TX beams (i.e., \(\theta_{TX}\)) that mainly influences the SC trend over lags in MLT imaging, while the total SC value depends on the number of TX beams. When \(N_{TX}\) is fixed to four, for example, as in Figures 2c and 3c, total SC remains almost constant at -8, while the lobes of the SC trend gradually move at higher lags with increasing values of \(N_{TX}\). Conversely, when \(\theta_{TX}\) is fixed (Figures 2b and 3b), we have a similar trend in all the MLT cases, with a peak at about lag 19, but the total SC decreases as \(N_{TX}\) becomes higher. Both behaviors can be observed in Figures 2a and 3a, where \(N_{TX}\) and \(\theta_{TX}\) change at the same time in the different MLT configurations analyzed.
## 2 Solar-Hydrogen Storage System
### Workflow of Solar-Hydrogen Storage System
* Monitoring and Controlling: Monitoring and control systems supervise the actions of several components during the workflow, guaranteeing their safe and effective functioning. In order to maximize system performance, control units manage the flow of energy, water, and hydrogen, while monitoring sensors measure variables like solar irradiance, electrolyzer efficiency, hydrogen purity, and storage tank conditions [148; 149; 150]. * Maintenance and Optimization: The solar-hydrogen storage system is maintained and optimized on a regular basis to guarantee its efficiency and dependability. To enhance system performance over time, this may entail checking storage tanks, cleaning solar panels, examining electrolyzer components, and upgrading control algorithms [151; 152; 153]. Solar-hydrogen storage systems facilitate the effective conversion of solar energy into hydrogen gas through a prescribed workflow. This not only offers a clean and sustainable energy source for a range of applications but also a way to store excess energy for future use [154; 155; 156].
## 7 Conclusions  
Overall, this article proposes SAGIN-MEC, a SAGIN structure for heterogeneous device management and resource allocation optimization in IIOT and IOA scenarios. The structure uses the SDN and NFV technology for distributed control of the whole network. It also introduces MECs in satellite, aircraft, and ground hosts near the destination to perform computing tasks so as to reduce service delay. Based on this structure, we design a hybrid algorithm DRL-G based on deep reinforcement learning and the heuristic algorithm to solve the resource allocation problem in SAGIN. Several simulation experiments show that service delay in SAGIN-MEC can be reduced by 6-15 ms, and DRL-G significantly improves the success rate and delay. The next phase will focus on computing resource scheduling in SAGIN.  
X.F.: Conceptualization, Investigation, Methodology, Software, Formal analysis, Writing--Original draft preparation. M.H.: Writing--review &editing, Supervision, Project administration, Funding acquisition. L.Z.: Writing--review@editing, Supervision, Project administration, Funding acquisition. Y.S.: Data curation, Visualization, Writing--review@editing. R.P.: Data curation, Software. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Major Project of Henan Province, China (NO. 221100210900-03) &The Scientific and technological project in Henan Province (Project No. 232102210154) & Pre-research Project of Songshan Laboratory (Project No. YYJC022022001).  
The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SAGIN & Space-Air-Ground Integrated Network \\ QoS & Quality of Service \\ SDN & Software Defined Network \\ NFV & Network Function Virtualization \\ MEC & Mobile Edge Computing \\ DRL & Deep Reinforcement Learning \\ \end{tabular}  
Figure 12: The number of SFCs exceeding energy constraints with different algorithms.