esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Home Ranges of Satellite-Tracked Reef Manta Rays  
The majority of the satellite-tracked reef manta rays exhibited restricted home ranges around the tagging locations. Across 25 individuals, the 95% utilization distributions (UDs) varied substantially, ranging from 19 to 48,294 km\({}^{2}\) (mean = 4667 \(\pm\) 10,354 km\({}^{2}\)) (Table 1). The individual with the smallest 95% UD was observed in Nusa Penida (ID#140895). Among regions, Berau had the largest mean 95% UD at 9205 \(\pm\) 10,681 km\({}^{2}\), while Komodo had the smallest at 772 \(\pm\) 912 km\({}^{2}\). The mean 95% UD for individuals tracked in Nusa Penida was 1192 \(\pm\) 2442 km\({}^{2}\), and in Raja Ampat, it was notably larger at 6630 \(\pm\) 13,721 km\({}^{2}\).  
Several individuals in each region exhibited extended home ranges into neighboring areas. For instance, a female reef manta ray (ID#149141) tagged in Misool demonstrated an extended home range to Fakfak to the southeast of Raja Ampat, with a 95% UD of 48,294 km\({}^{2}\)--the largest among all the satellite-tracked individuals (Table 1). In Nusa Penida, a male individual (ID#140900) displayed a relatively large home range that included Bali, Lombok, and western Sumbawa (Figure 4), with a 95% UD of 5498 km\({}^{2}\). In comparison, the other individuals in this region had 95% UDs ranging from only 19 to 61 km\({}^{2}\), primarily near the tagging site. Additionally, in Komodo, a female individual (ID#140914) extended her home range to southern Sumbawa and western Flores, with a 95% UD of 2264 km\({}^{2}\); the other tagged individuals in Komodo showed restricted 95% UDs of 52-926 km\({}^{2}\).  
We conducted an unpaired two-sample Wilcoxon test to assess the differences in home ranges (95% UD) between the male and female reef manta rays, as a Shapiro-Wilk test indicated strong evidence (\(p<0.001\)) of non-normal distribution, particularly in females' home ranges. The Wilcoxon test found no significant effect of sex on home ranges (\(p=0.409\)). The mean home range for female reef manta rays was 4989 \(\pm\) 12,192 km\({}^{2}\), which was comparable to the mean for males at 4522 \(\pm\) 5365 km\({}^{2}\). When considering only individuals tracked for longer than the average period (50 days) and excluding an outlier (ID#140896) with a 95% UD of 21,010 km\({}^{2}\), the home range of females (mean = 1498 \(\pm\) 787 km\({}^{2}\)) was significantly smaller than that of males (mean = 5243 \(\pm\) 1300 km\({}^{2}\)), as indicated by an unpaired two-sample t-test (\(p<0.001\)).  
The combined home ranges (95% UD) of the satellite-tagged reef manta rays in each region were as follows: 21,522 km\({}^{2}\) in Berau, 5475 km\({}^{2}\) in Nusa Penida, 2398 km\({}^{2}\) in Komodo, and 65,373 km\({}^{2}\) in Raja Ampat (Figure 4). In relation to marine protected areas (MPAs), less than half of the home ranges in the study regions fell within the boundaries of the numerous MPAs gazetted in these four regions: 12.1% in Nusa Penida, 15.0% in Berau, 27.8% in Raja Ampat, and 47.4% in Komodo.
The status parameters of the RECAS sonde and RCUPS system during drilling were regularly transmitted to a domestic monitoring center via the Iridium satellite system. Domestic staff could also remotely send commands to direct the field drilling when it is necessary through the same communication means. A surface auxiliary drilling system was designed to facilitate the deployment of the RECAS sonde. In areas where a firm layer existed on top of the ice sheet, a shallow drill was used to penetrate the firm layer and create a pilot dry hole. Once the sonde returned to the surface, it was placed and fixed in the dry hole until the next season. A prototype with a drilling capacity of 500 m was developed, and the structure and testing of the EFSOS are elaborated in detail in this study.
# Data Sensing and Processing Tensioning System Based on the Internet of Things
## 2 Technical Framework and Analysis
### Application Service Layer  
The application service layer consists of a data-processing layer and an application layer. The data-processing layer processes data in line with the rule base of tensioning operations, and the application service layer provides app and web application services. The main tasks of the application service layer include (1) storing packets that match the rule; (2) detecting data items in the packets in accordance with the warning rule and triggering warnings depending on conditions; (3) extracting data items in job packets and synthesizing job data in line with job rules; (4) completing the pushing service of app messages; (5) completing the service monitoring and data browsing of app terminals; and (6) implementing the display function of web pages data.
## 4 Diatom Handcrafted Feature Descriptors
### Texture Descriptors in the Space-Frequency Domain  
The previously-introduced statistical descriptors are calculated in the space-frequency domain. It is assumed that features, somehow hidden, arise with higher visibility in this domain. Thus,  
Figure 5: LBP images corresponding to the following species: (**a**) _Cymbella excisa_ var _angusta;_ (**b**) _Cyclostephanos dubius;_ (**c**) _Gombonema insigniforme;_ (**d**) _Adnanthes subhudsonis._  
some transformations must be applied to the image to analyze its properties in this domain. In this study, a log-Gaborfilters were used to characterize the texture of diatoms. Then, for every transformed domain or sub-band, a total number of 964 statistical descriptors is calculated; see Section 4.2.  
Log Gabor Transform  
Log-Gabor filters are defined in the frequency domain as Gaussian functions shifted from the origin to avoid the singularity of the log function. In addition, the Gaussian envelope is modulated by a complex exponential with even and odd phases, which is effective for characterizing edges. Here, log Gabor filters proposed by [PERSON] et al. [35] have been used. The log Gabor descriptor is based on the energy calculated at every scaled level:  
\[\begin{array}{l}Gabor_{I}=\sum_{\rho=1}^{O}\sum_{u=1,\rho=1}^{U,V}|F^{-1} \left(G_{lo}\cdot I\right)\cdot B(u,\rho)|,\;\;\;l\in\{1,..,L\}\\ G_{lo}=\exp\left(-\frac{1}{2}\left(\frac{\rho-\rho_{l}}{\sigma_{\rho}}\right)^ {2}\right)\exp\left(-\frac{1}{2}\left(\frac{\theta-\theta_{\rho}}{\sigma_{ \theta}}\right)^{2}\right)\end{array} \tag{9}\]  
where \(F^{-1}\) is the inverse Fourier transform, \(G_{lo}\) is the log Gabor filter with \(L\) scales and \(O\) orientations in log-polar coordinates and \((\rho,\theta)\) and \((\sigma_{\rho},\sigma_{\theta})\) are the angular and radial bandwidths; see [35] for more details. Again, \(L=4\), \(O=6\), and the residual DC-component is discarded. Figure 6 shows the log Gabor filters (four bands: G1, G2, G3 and G4) applied to a diatom example.
# An Overview of Design Techniques for Two-Dimensional Leaky-Wave Antennas
## 3 Fabry-Perot Cavity Antennas
### Feeding Schemes and Matching Networks  
In this subsection, the physical implementation of the feeders of FPCAs is discussed. So far, ideal, dipole-like sources have been assumed. From a practical viewpoint, these kinds of feeders do not exist but they can be realized through devices with similar radiating features. Moreover, in order to avoid return-loss effects, which have been completely neglected so far, the feeding schemes have to be properly engineered to be adequately matched. Such a goal is addressed in different manners depending on the considered source.  
In the case of a VED-like feeder, a coaxial cable is commonly employed. The latter can be matched e.g., by efficiently choosing the penetration depth of its inner conductor and/or its dielectric filling in the FPCA. Moreover, a circular metallic patch can be properly designed in the center of the PRS as an additional element of the matching network. These parameters are usually optimized through parametric full-wave simulations [60; 61; 87]. Additionally, a similar approach is commonly employed for HED- and VMD-like sources,  
Figure 6: A schematic representation of a metal strip grating is reported on the left (metal and air are described in gray and blue, respectively). The equivalent sheet reactance of the homogenized metasurface is reported on the right, showing a remarkable agreement between the numerical analysis furnished in [72] and the proposed full-wave approach.  
since they are implemented through bent coaxial cables in the FPCA, viz. an L-probe [67] and a loop antenna [62; 63], respectively. Particular attention is needed for HMDs. The latter are implemented through rectangular slots on the ground plane and, depending on their feeding scheme, they can be matched in different manners. For subresonant slots, a microstrip feeding network is commonly employed and it can be matched through standard transmission-line techniques [88; 89]. In the case of quasi-resonant slots, the device can be fed through rectangular waveguides [44; 66] whose return loss can be improved with different techniques [88]. In particular, an efficient transmission-line-based method that requires a single full-wave simulation to design capacitive irises has been proposed and validated in [64].
## 4 Laboratory SSCB Model Test under the Lateral No-Flow Condition
### Infiltration Behavior in the SSCB Model Test  
#### 4.4.1 Result for Case 1  
Figure 6 shows the results of the volumetric water contents (\(\emptyset\)) measured by the EC-5 sensors for each case. It was observed that the measured values of sensors No. 3 and No. 4 after the breakthrough commonly increased up to 6 h. This tendency corresponds to a change according to the contact area with the sensor in the water infiltration paths. Thus, the infiltration path after 6 h in this study was also investigated, and the occurrence of an additional water infiltration path was not confirmed. After 6 h, since the amount of water drained from the soil tank was in a steady state compared to the precipitation, we decided to conclude the model test. It should be also noted that there was no surface runoff or predominant lateral runoff for each case in this study.  
The model test for Case 1 was performed under a rainfall intensity of 20 mm/h over 6 h (Table 3). Figure 6a shows the volumetric water contents (\(\emptyset\)) measured by the EC-5 sensors for the model test of Case 1: the response times of the four EC-5 sensors were visibly different. In fact, at \(t\) = 75 min, sensor No. 1 (installed in the sand layer) showed the fastest response, indicating a rapid increase in the volumetric water content. Meanwhile, sensor No. 2 reported a rapid increase in the volumetric water content 10 min later than sensor No. 1; after which the volumetric water content appeared to quickly reach the saturated condition (as in the case of sensor No. 1). These results indicate that, in Case 1, the sand layer became saturated after 180 min. Notably, sensor No. 3 (installed in the gravel layer) did not show any response during the test. Finally, sensor No. 4 showed a gradual increase in the volumetric water content after 210 min, demonstrating the occurrence of a breakthrough at that time.  
Figure 7 shows the occurrence of rainfall infiltration in Case 1 as a function of time. The infiltration of rainfall to the midpoint of the sand layer was observed after 60 min from the beginning of the test (dry state) (Figure 7a,b). After 200 min, the entire sand layer became saturated (Figure 7c). This trend was observed in terms of variations in the volumetric water content by sensors No. 1 and No. 2 (Figure 6a). Meanwhile, a breakthrough was clearly observed in Case 1 after 210 min (Figure 7d). After 360 min (6 h), sensor No. 4  
Figure 6: Measured volumetric water contents (\(\emptyset\)) for each case: (**a**) Case 1 (l = 20 mm/h); (**b**) Case 2 (l = 50 mm/h); (**c**) Case 3 (l = 100 mm/h).  
indicated an increase in the volumetric water content due to rainfall infiltration; however, sensor No. 3 did not measure any increase, since it was located at a distance from the location of the breakthrough event (Figure 7e). Notably, after \(t\) = 360 min, the observations of all sensors agreed well with each other (Figure 6a).
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 3 Materials and Methods
### Iris Landmark Annotations  
To annotate the iris, five key landmarks i.e., center, top, right, bottom, and left are identified. To handle the problem of the different diameters of the irises of different persons, the threshold method was used to check that if the iris diameter and the area selected by the proposed method were the same. After this, the annotation was started. The radius was adjusted to obtain a perfectly aligned circle with the iris. The threshold value was added or subtracted from the center of the iris. Algorithm 1 shows the steps involved in landmarks annotation.  
```
Input :Mouse_Event, x,y, file_name Output : Create a text file with the same image name and save the annotations in that file Procedure Annotate_Image if(Mouse_Event is equal to Mouse Move then  Draw a circle to check whether Matches Iris diameter or not end if ifMouse_Event is equal to Mouse Left Click then  Draw Circle on center, Draw Circle on Top, Draw Circle on Right, Draw Circle on Bottom, and Draw Circle in the left side of the iris. end ifMouse_Event is equal to Mouse Right Click then  generate output  end Procedure Main Create a directory to save images with annotations Input :Initialize a list with Image Names whileimages are available do  read Images call: Annotate_Image display image till next image or program ends end
```  
**Algorithm 1** Iris landmark annotation pseudo code.  
Figure 1: Iris landmark annotation samples.
## 5 Results and Discussion
### Real-World Applicability and Implementation Scenarios  
The two MHMM variants evaluated in this study-Expert MHMM and Trained MHMM-demonstrate the framework's adaptability to diverse real-world needs in Industry 5.0, where human-centric and resilient systems are foundational. The choice between a pre-defined or trainable model depends on the application context, data availability, and the balance between standardized assessment and context-specific adaptation, with both ensuring secure handling of sensitive multimodal data (e.g., HRV, TCT, NASA-TLX).  
* Expert MHMM: This model is suited for standardized, high-stakes settings with established proficiency benchmarks, such as surgical training programs. Its pre-defined parameters, derived from empirical literature (e.g., [PERSON] et al., 2025) [24], enable a computationally efficient \"plug-and-play\" system, ideal when large, context-specific datasets are unavailable. For example, in surgical simulation, the Expert MHMM could use securely collected HRV data to assess trainee proficiency, ensuring data privacy through encrypted transmission. Its interpretable transition probabilities (e.g., 15% Novice-to-Intermediate transition) support trusted decision-making in safety-critical applications. In our simulation, the Expert MHMM achieved 92.7% accuracy (Table 4), highlighting its potential for standardized assessments.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter (State)** & **Perturbation** & **Accuracy** & **F1-Macro** \\ \hline  & Baseline & 0.925 & 0.912 \\ \hline HRV mean (0) & \(\pm\)10\% & 0.919–0.917 & 0.904–0.902 \\ \hline HRV mean (1) & \(\pm\)10\% & 0.926–0.920 & 0.913–0.907 \\ \hline TCT std (0) & \(\pm\)15\% & 0.926–0.925 & 0.914–0.912 \\ \hline TLX mean (1) & \(\pm\)10\% & 0.919–0.920 & 0.904–0.908 \\ \hline TLX std (2) & \(\pm\)10\% & 0.925–0.924 & 0.912–0.911 \\ \hline \end{tabular}
\end{table}
Table 5: Selected parameter sensitivity results for trained MHMM.  
* Trained MHMM: This variant is ideal for dynamic environments where local factors influence performance, such as smart factories with unique workflows. By learning parameters via the Baum-Welch Algorithm [68], it adapts to context-specific variations, enhancing accuracy within particular settings. For instance, a manufacturing facility could train the MHMM on encrypted TCT data to monitor human proficiency with cobots, ensuring data security. The Trained MHMM's 92.5% accuracy (Table 4) demonstrates its robustness in data-driven scenarios. This adaptability supports Industry 5.0's vision of personalized operator support [68].  
The flexibility of the MHMM framework to accommodate both standardized and customized applications underscores its value for human-AI collaboration in Industry 5.0. While simulation results validate its efficacy, future empirical validation in real-world settings (e.g., manufacturing or healthcare) is needed to confirm its practical impact.
## 4 Discussion  
The proposed GLADE mission concept builds on proven concepts and achieves performance comparable to that of much more challenging designs previously submitted to M3 and M4 ESA calls.  
It may be noted that the achievable precision (Figure 7) is a performance indication related to the light deflection measurement, even if, for short, it is referred to as the PPN parameter \(\gamma\). At this level, the relevance of the PPN model itself may be questioned, and more detailed (e.g., parametrized post-post-Newtonian (PPPN)) interpretation models may be called for. In that case, the \(<\)10\({}^{-7}\) precision is spread among the different estimated parameters.  
In addition, light deflection is described throughout this paper as having a simple circular symmetry, which is not necessarily the case for all possible gravitation models. Also, deviations from circular symmetry might be induced in cases of violation of the Lorentz invariance [34]. GLADE is a convenient platform for the detection of such kinds of effects due to its principle of simultaneous measurement over a corona around the Sun.  
The GLADE concept is intrinsically compatible with a solar science payload, given the common observing region. The multiple payload observation is a common approach in this context, used in previous missions, e.g., SOHO [35]. In practice, a common external occulter may feed several instruments observing the Sun and circumsolar regions at different radii, in different spectral bands and with various spectroscopic and polarimetric capabilities. Future project development is therefore also a matter of negotiation among scientific communities interested, respectively, in fundamental physics, solar physics and space weather.  
The proposed implementation based on a 0.3 m telescope may be considered as an intermediate-scale version of the experiment, roughly corresponding to a medium-class mission on the instrument side (i.e., factoring out the occulter and formation flying aspects). It can be shown that a comparably limited upgrade of the instrument, switching to a one-meter-class telescope and corresponding detection system (at an order of 30 chips with comparable specifications), will provide a remarkable performance boost. In fact, the telescope diameter \(D\) affects both imaging resolution in diffraction-limited conditions, decreasing at \(1/D\), and S/N, increasing with \(D\) in photon-limited conditions; therefore, the location uncertainty from Equation (2) scales as \(D^{-2}\).  
This provides an overall improvement on light deflection determination, all other factors left unchanged, by about one order of magnitude, pushing the photon-limited precision level in the same mission lifetime below 10\({}^{-8}\).  
Figure 8: Distribution of star parallaxes and parallax errors from Gaia DR3 in the \(\pm\)0\({}^{\circ}\).84 ecliptic latitude strip observed by GLADE.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
## 6 Conclusions  
This paper proposes assessing the LoRaWAN CubeSat signal with an Adaptive Data Rate (ADR) while carrying the LoRaWAN gateway in the payload subsystem. It demonstrated a signal from nodes to Direct-to-Satellite (DtS) with a transmit power of 14 dBm.  
Figure 12: Relationship of RSSI and SNR at 0\(-\)150 dB attenuation levels with a step of 30 dB.  
Path loss link margin was calculated as stated in the literature and then verified by adding a fixed attenuator of up to 150 dB. The system proved and satisfied the signal transmitted from the node to the LoRaWAN satellite gateway at worst case scenario: 5\({}^{\circ}\) of elevation angle, roughly 2205.9 km, was successfully received by the gateway.  
Received signal strength and Signal-to-Noise Ratio (SNR) were observed for 4 h to identify any misbehavior during the window. The signal was observed to be stable, and the LoRaWAN gateway could receive signals per the calculated distance with a minimum transmit power. The results showed that the LoRaWAN gateway can receive a direct signal from the nodes. The gateway can be used in a CubeSat as a communication subsystem while deploying a sensor network of small satellites. The findings also demonstrated that spacecraft equipped with a LoRaWAN gateway and ADR can properly receive uplink messages from ground nodes independent of Doppler shift. However, in the real time use of LoRaWAN gateway, far field should have influence on the system which has to be taken into account.  
As indicated, this study shows the single-module possibilities of LoRaWAN. Compared to TinyGS architecture, the Kitsune mission, and the Birds mission, which exclusively employ LoRa transmitters, this research presents the LoRaWAN-enabled communication subsystem for the CubeSats missions as a low-cost, low-power approach for creating sensor networks.  
Additional work is needed for future investigation on delivering nodes data received by the gateway on LEO to the ground station. The investigation of achieving nodes data on the spacecraft onboard board computer with minimum computing power when the ground station is not visible is also needed.  
Formal analysis, S.Z.A.M.; Investigation, S.Z.A.M.; Supervision, A.M.A.; Writing--original draft, S.Z.A.M.; Writing--review and editing, A.M.A. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Higher Education Malaysia on Fundamental Research Grant Scheme with Project Code: FRGS/1/2020/TK0/USM/03/8, and the APC was funded by Universiti Sains Malaysia.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank all who have directly or indirectly provided assistance to support this project.  
The authors declare no conflict of interest.
## 3 Fundamentals of RF Energy Harvesting
### Components of an RF Energy-Harvesting System  
An RFEH system comprises several interconnected components, each playing a critical role in determining overall efficiency. The major components include the antenna, matching network, rectifier circuit, and energy storage unit.