esa-sceva/satcom-chunk-collection huggingface dataset:
# An Assessment of the Impact of Gypsum Deposit Development on Changes in the Radiation Environment
## 3 Results and Discussions
### Soils, Quaternary Deposits and Gypsum  
#### 3.3.1 Activity Concentrations of Radionuclides in Soils, Overburden Deposits and Gypsum  
Table 5 shows the results of determining the activities of radionuclides in soils, overburden deposits and gypsum.  
The \({}^{137}\)Cs activity in the litter-peat soil horizon \"O\" averaged 32.4 \(\pm\) 5.0 Bq kg\({}^{-1}\). This is five times higher than in the bottom sediments. However, it should be noted that in the three lakes with the maximum organic matter content, the \({}^{137}\)Cs activity ranged from 10.2 \(\pm\) 2.1 to 20.8 \(\pm\) 3.0 Bq kg\({}^{-1}\) (see Section 3.2.1). Earlier, in the eutrophic peat horizon \"TE\" 150 km northwest of the study area, average \({}^{137}\)Cs activity values of 50.9 Bq kg\({}^{-1}\) were established [20]. The background activity of \({}^{137}\)Cs at the level of 50 Bq kg\({}^{-1}\) was determined for peat soils in the northwest of the Kola Peninsula [64]. The higher activity concentration of \({}^{137}\)Cs is likely due to the presence of clay, as \({}^{137}\)Cs has a strong affinity for clay and organic matter. In moraine loans and gypsum, the activity of \({}^{137}\)Cs was below the detection limit.  
The average activities of the uranium isotopes \({}^{238}\)U (6.02 \(\pm\) 0.88 Bq kg\({}^{-1}\)) and \({}^{234}\)U (5.81 \(\pm\) 0.85 Bq kg\({}^{-1}\)) in soil were three times lower than in bottom sediments. They corresponded to a uranium concentration of 0.48 \(\pm\) 0.07 mg kg\({}^{-1}\). At the same time, the average values of the \({}^{234}\)U/\({}^{238}\)U activity ratio in soil and bottom sediments were the same and amounted to 0.96 \(\pm\) 0.14. This corresponds to the conditions of secular equilibrium associated with the formation of the mineral part of soils and bottom sediments due to rock dissolution and the absence of geologically long-term contacts with moving water [65]. In morazine loans, the uranium content was twice as high as in soils (0.95 \(\pm\) 0.14 mg kg\({}^{-1}\)), while in gypsum, it was minimal, amounting to 0.16 \(\pm\) 0.02 mg kg\({}^{-1}\).  
The activity of \({}^{232}\)Th in morazine loans was 1.3 times lower than in soils (16.7 \(\pm\) 3.4 Bq kg\({}^{-1}\)) and 1.7 times higher than in bottom sediments. The activity of \({}^{40}\)K in morazine loans was almost the same as in bottom sediments (355 \(\pm\) 68 Bq kg\({}^{-1}\)), while in soils, it was 1.5 times lower. In gypsum, the activities of these isotopes were below the detection limit. The highest activities of the \({}^{226}\)Ra isotope were in soils, from 12.1 \(\pm\) 2.0 to 39.9 \(\pm\) 5.8 Bq kg\({}^{-1}\), with an average of 26.8 \(\pm\) 4.0 Bq kg\({}^{-1}\). In morazine loans, they were comparable to the activities in bottom sediments (20.7 \(\pm\) 3.0 Bq kg\({}^{-1}\)), and in gypsum, they were minimal, amounting to 3.69 \(\pm\) 1.08 Bq kg\({}^{-1}\).
# RoseCliff Algorithm: Making Passwords Dynamic
## 2 Encryption
### Asymmetric Encryption  
Asymmetric encryption, often termed public key encryption, plays a fundamental role in securing digital data exchanges. It stands apart from symmetric encryption, which relies on a single shared key for both encryption and decryption processes. Instead, asymmetric encryption harnesses a dual key mechanism: a public key for encryption and a private key for decryption. In this framework, users create a key pair--consisting of a publicly shared key and a confidential private key. This setup empowers anyone to encrypt messages using the recipient's public key, while only the recipient, holding the corresponding private key, can decrypt them. Its versatile applications span secure communication (e.g., emails and financial transactions), digital signatures (verification of document authenticity), key management (secure key distribution without divulging secrets), and secure authentication methods (e.g., SSH and SSL/TLS protocols for web browsing) [22; 23; 24; 25; 26]. Asymmetric encryption encompasses a spectrum of algorithms and methods, each with unique strengths and applications. Among the prominent choices are RSA (Rivest-Shamir-Adleman), known for its reliance on prime numbers, and Diffie-Hellman, a key exchange protocol enabling secure symmetric encryption. Additionally, the Digital Signature Algorithm (DSA), rooted in modular arithmetic and discrete logarithms, is widely recognized for creating digital signatures. Noteworthy variants include ECDSA (Elliptic Curve Digital Signature Algorithm) [23; 27], ElGamal (ElGamal Encryption Algorithm) [24], and ECDH (Elliptic Curve Diffie-Hellman) [26].
# Future Perspectives for Gamma-ray Burst Detection from Space
## 6 LEAP
### Detector Design  
The LEAP payload (Figure 9) consists of an array of seven independent polarimeter modules, each with a 12 \(\times\) 12 array of optically isolated high-Z and low-Z scintillation detectors read out by individual PMTs. Each polarimeter module includes 84 plastic scintillator elements, 58 CsI(Tl) scintillator elements, and two \({}^{60}\)Co calibration sources. Each calibration source consists of a small plastic scintillator doped with \({}^{60}\)Co, which permits electronic tagging of each decay via the coincident \(\beta\) particle. As a Compton polarimeter, scatter events recorded by the scintillator array within each module are used to measure polarization. Within each module, the arrangement of plastic and CsI(Tl) scintillation detectors is designed to optimize the polarization response. The dominant type of scatter event is one involving only two detector elements, in which incident photons scatter from a low-Z plastic detector element into a high-Z CsI(Tl) element. The distribution of azimuthal scatter angles for these events provides a polarization signature. The total effective area for polarimetry is \(\sim\)1000 cm\({}^{2}\) at energies above 100 keV. Since the total energy deposit is a sum of the energy deposits in all triggered elements, spectroscopic information is provided by all types of events of any multiplicity (both single events and scatter events). To characterize the GRB parameters, spectroscopic measurements (20-5000 keV) are obtained using all event types (both multiple and single events), with a total effective area that reaches \(>\)3000 cm\({}^{2}\) between 50 and 500 keV.  
Figure 8: Schematic view of the HiZ-GUNDAM satellite.  
To accurately reconstruct the source spectrum and polarization, LEAP self-sufficiently determines the source direction using single events from all 420 CsI(TI) elements, whose relative response provides the source localization. Localization errors of \(<\)5\({}^{\circ}\) (1\(\sigma\)) are obtained at a rate of about 40 per year. This is sufficiently precise to enable rapid follow-up by many ground-based instruments using the rapid burst response messages that will be generated and distributed (in real time) by LEAP.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 3 Results and Analysis
### Results of Individual Tree Segmentation
#### 3.1.2 Trunk Detection Results  
Figure 6 shows the individual tree segmentation results obtained by using the proposed method, in which the yellow box is the part of the trunk detected after the initial clustering. In order to present the tree detection results of the six plots more vividly, we located the position of each tree by calculating the centroid of the point clouds at the bottom of the tree. TP indicates that the trunk is correctly detected. FP indicates that there is no trunk, but a trunk is detected. FN indicates that there are multiple trunks but only one trunk is detected. The location distribution of the segmentation results of the six plots is shown in Figure 7. A total of 45 trunks are correctly detected in Plot 1. There is a place where the two trunks are identified as one (FP is one). A total of 46 trunks are correctly detected in Plot 2. There are three places where two trunks are identified as one (FP is three). A total of 44 trunks are correctly detected in Plot 3. There is a place where the two trunks are identified as one (FP is one). In Plot 4, 30 trunks are correctly detected. There is a place where three trunks are identified as a single trunk (FP is two). A total of 49 trunks were correctly detected in Plot BR04. There are three places where the multiple trunks are identified as one (FP is four). A total of 34 trunks are correctly detected in Plot KA09. There are two places where the multiple trunks are identified as one (FP is three). As can be seen from the position distribution map, when the distance between the trunks is very close, these trees will be divided into one tree. Additionally, the minimum spacing between segmentation trees is determined by the parameter \(\varepsilon\). In conclusion, more than 88% of the trunks could be correctly detected in the six plots, although the trunks are near or far apart from each other.  
Figure 5: Individual tree detection results and accuracy of different plot.  
Figure 6: Trunk detection results based on improved DBSCAN.
## 2 Numerical Model
### Time-Domain Numerical Simulation  
Authors developed a numerical model [7] that can perform tunnel-mooring-coupled time-domain simulations using OrcaFlex [16] to produce data-sets for machine learning. The SFT was modeled by the long-beam model, which consists of nodes (lumped masses) and segments (massless linear and rotational springs). The mass, drag, and other important properties are all lumped at the nodes, and stiffness properties are modeled in the segments. The time-domain equation of motion for SFT can be expressed as:  
\[\mathbf{M\ddot{x}}+\mathbf{Kx}=\mathbf{F_{M}}+\mathbf{w}+\mathbf{F_{C}}\delta (\mathbf{x}_{i}-\mathbf{\xi}) \tag{1}\]  
where \(\mathbf{M}\) and \(\mathbf{K}\) are system's mass and structural stiffness matrices, respectively, \(\mathbf{x}\) is the displacement vector, \(\mathbf{x}_{i}\) is the longitudinal position vector of tunnel nodes, \(\mathbf{\xi}\) is the vector representing longitudinal locations of mooring lines, \(\mathbf{F_{M}}\) is the hydrodynamic force vector, \(\mathbf{w}\) is the wet-weight vector (i.e., net sum of weight and buoyancy), \(\mathbf{F_{C}}\) is the constraint force vector at the tunnel-mooring connection locations (i.e., coupling force induced by mooring lines on the tunnel and vice versa), and \(\delta\) is the Dirac delta function. Upper dot in the equations means time derivative of a variable.  
The line's elastic behaviors are considered by the \(\mathbf{Kx}\) term with axial, bending, and torsional springs. The axial and torsional springs located at the center of two neighboring nodes evaluate the tension force and torsional moment while the rotational springs located at either side of the node estimate the shear force and bending moment.  
The hydrodynamic force at nodes' instantaneous locations was evaluated by the Morison equation for a moving body, which can be written for a cylindrical object as:  
\[\mathbf{F_{M}}=-C_{A}\rho V\mathbf{\ddot{x}}^{n}+C_{M}\rho V\mathbf{\dot{n}}^ {n}+\frac{1}{2}C_{D}\rho A\big{|}\eta^{n}-\mathbf{\dot{x}}^{n}\big{|}\Big{(} \eta^{n}-\mathbf{\dot{x}}^{n}\Big{)} \tag{2}\]  
where \(C_{A}\), \(C_{M}\), and \(C_{D}\) are the added mass, inertia, and drag coefficients, respectively, \(V\) and \(A\) stand for the displaced volume and projected area, \(\rho\) is density of water, \(\eta\) is velocity of a fluid particle, and superscript \(n\) means the normal direction. More details of the numerical model can be found in [7]. The developed SFT dynamics simulation program was partly validated through comparisons with a series of experimental results for a small SFT segment with similar mooring set-up [18].
## 2 Rate Equations Model
### Power Spectral Density and Laser Intrinsic Linewidth
The only exception are the stable cases when meeting the three conditions (8), (11) and (12), yet the feedback-insensitivity provided by this case is also beneficial. Selecting an appropriate \(\Delta\phi_{\rm m}\) can thus be used to harness linewidth narrowing properties at a desired frequency. Furthermore, system tolerances to conditions (8) and (11) are studied, while maintaining condition (12). Results for up to a 20% deviation are shown in Figures 10-11 for cases 1, 2 and 3, respectively, where single valued solutions are observed in all cases in the full frequency span. Results show a high tolerance with respect to feedback strength. Looking at case 3, single valued solutions are obtained for \(\kappa_{1}/\kappa_{2}\in(0.2,1.7)\). While the system is no longer feedback-insensitive, results evidence single-valued solutions that are robust with respect to condition (8). The system is more sensitive with respect to (11), with single valued solutions achieved within \(t_{1}/t_{2}\in(0.47,1.2)\). Nevertheless, tolerances become once again stricter as feedback increases for both parameters, thus laser design is of paramount importance and should focus on meeting the discussed stability conditions. In particular, choosing equal lengths for both external cavities should suffice to meet  
\[\text{F}_{0} \equiv\zeta_{\text{s}}^{2}+\text{K}_{\text{c}}^{2}\tau_{\text{e}} ^{-2}-2\text{K}_{\text{c}}a_{g}G_{0}S_{0} \tag{23a}\]6 Numerical solutions for \(C=1.3\) under conditions (8) and (11) for different \(\Delta\phi_{\rm m}\). Double-feedback case shown in blue, single feedback case shown in orange. Column (**A**) shows the lasing frequency shift results. Column (**B**) shows the threshold gain shift. Column (**C**) shows the intrinsic linewidth variations. condition (11). Fabrication tolerances in foundry processes are the main limiting factor for time delay accuracy. To meet condition (8), a possible approach is to merge the output of the two external cavities into a single one using a coupler, which can be included in the laser on-chip. Finally, as mentioned before, condition (12) can be met by using a phase shifter, which is a mature component in active platforms. All in all, results demonstrate that, with proper design of the laser cavity, conditions (8), (11) and (12) can be met, with which it is possible to obtain lasing frequency and intrinsic linewidth insensitivity to feedback.
## 1 Introduction
The two-leaf canopy PRI (PRI\({}_{\text{t}}\)) was calculated as the sum of sunlit and shaded PRI weighted by their respective sunlit and shaded LAI, based on the principle of linear decomposition of canopy reflectance [70]. Tower-based flux and multi-angle PRI measurements were made over a subtropical forest to develop and validate the algorithm. At both half-hourly and daily time steps, PRI\({}_{\text{t}}\) can effectively improve (\(>\)60%) its correlations with LUE derived from the tower flux measurements over the big-leaf PRI taken as the arithmetic average of the multi-angle measurements in a given time interval. Even though the definitions of sunlit and shaded leaves are clear, except for these two parts, the penumbra region (part sun visible) is also an important element of the canopy since the sun is a solar disc with a finite size instead of a beam with parallel rays [74]. The penumbra region is the 'grey region' between sunlit and shaded leaves, and can be treated as partially sunlit and partially shaded blurring the boundary between sunlit and shaded leaf. Penumbra effect would influence the canopy photosynthesis and stomatal conductance to some extent. The method for estimating the fraction of sunlit leaves presented by [PERSON] et al. [73] fitted the definition of sunlit leaves; however, this method included a substantial portion of the penumbra region into sunlit leaves, which needed to be verified if the fraction of sunlit leaves is overestimated. This study aimed at testing three methods for estimating the fraction of sunlit leaves that were seen by a remote sensing sensor, which were further used to calculate two-leaf canopy PRI. Comparisons were made among the three methods including two models for estimating the fraction of sunlit leaves based on [PERSON]'s method [73] with different hypotheses on the way to treat penumbra region (hypothesis I: a substantial portion of penumbra region is treated as sunlit leaves; and hypothesis II: a substantial portion of penumbra region suffers a much lower level of light stress than sunlit leaves and similar as shaded leaves), and an geometrical-optical model, which was a novel tool to simulate the fractions of four components of the canopy (i.e., sunlit and shaded leaves and sunlit and shaded backgrounds) [72]. These models were tested against the tower-based flux and PRI datasets in order to find a simple and reliable two-leaf model for improving the ability of PRI measurements for tracking LUE as well as APAR of plant canopies.
###### Abstract  
Dam-break flood waves represent a severe threat to people and properties located in downstream regions. Although dam failure has been among the main subjects investigated in academia, little effort has been made toward investigating wave propagation under the influence of tailwater depth. This work presents three-dimensional (3D) numerical simulations of laboratory experiments of dam-breaks with tailwater performed at the Laboratory of Hydraulics of Iskenderun Technical University, Turkey. The dam-break wave was generated by the instantaneous removal of a shuice gate positioned at the center of a transversal wall forming the reservoir. Specifically, in order to understand the influence of tailwater level on wave propagation, three tests were conducted under the conditions of dry and wet downstream bottom with two different tailwater depths, respectively. The present research analyzes the propagation of the positive and negative wave originated by the dam-break, as well as the wave reflection against the channel's downstream closed boundary. Digital image processing was used to track water surface patterns, and ultrasonic sensors were positioned at five different locations along the channel in order to obtain water stage hydrographs. Laboratory measurements were compared against the numerical results obtained through FLOW-3D commercial software, solving the 3D Reynolds-Averaged Navier-Stokes (RANS) with the \(k\)-\(\epsilon\) turbulence model for closure, and Shallow Water Equations (SWEs). The comparison achieved a reasonable agreement with both numerical models, although the RANS showed in general, as expected, a better performance.  
dam-break; laboratory experiments; CFD; unsteady flow; tailwater; wave front velocity; RANS simulations; SWEs; FLOW-3D; image analysis measurement +
Footnote †: journal: Applied Sciences
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 1 Introduction
While the A-DBR scheme adjusts its transmission range to overcome the void hole problem to continue the greedy forwarding of the data towards the sink, B-DBR exploits a fall back recovery mechanism to find out an alternative route for delivering data at the destination. Additionally, we propose Cluster-based WDFAD-DBR (C-DBR) to minimize end-to-end delay and reduce energy consumption. The Collision Avoidance-based WDFAD-DBR (CA-DBR) handles the collision problem by selecting a potential forwarder node with the minimum number of neighbors. The main scientific contributions of this paper are:  
* Two techniques, A-DBR and B-DBR, are proposed to avoid void holes. * Two techniques, CA-DBR and C-DBR, are proposed to avoid collision and minimize the packet drop ratio. * C-DBR selects the forwarder node with the maximum residual energy in order to enhance the lifetime of the network. * The proposed schemes are compared with WDFAD-DBR in terms of average packet delivery ratio, energy tax, end-to-end delay and accumulative propagation distance. The rest of this paper is organized as follows: In Section 2, related work on existing schemes in UWSNs and the problem statement are presented. In Section 3, the background is discussed includingthe system model, energy consumption and propagation models. Section 4 describes the proposed schemes in detail. Simulation results are presented in Section 5. Performance trade-offs are given in Section 6, followed by the conclusion in Section 7.
# A Gamified AI-Driven System for Depression Monitoring and Management
## 5 Discussion
### Research Utility and Generalizability  
The system's simulation and dashboard modules turn it into an active research platform, enabling continuous monitoring, performance analysis, and hypothesis testing without repeated real-world trials.  
Including visual analytics, such as confusion matrices, ROC curves, and feature importance plots, allows researchers to interpret model behavior and evaluate decision confidence under different scenarios.  
However, generalizability is limited by synthetic data, and real-world applications must address issues like missing data, compliance variability, cultural differences, and comorbidities.  
A future extension could incorporate transfer learning techniques to adapt the pre-trained models to real-world contexts through transfer learning, improving the practical utility of the system in diverse populations.  
Another limitation of this work is the lack of direct benchmarking against existing digital mental health tools or clinical gold standards such as PHQ-9, GAD-7, or commercial platforms like Woebot and Wysa. This was primarily due to the synthetic nature of the dataset and the early-stage focus on system architecture and feasibility. Future work will involve empirical benchmarking using both self-report tools and clinician-validated assessments to compare performance and user outcomes.  
At this stage, the objective of the research is to test the hypothesis of having a gamified AI-driven system for depression monitoring and management, and further studies using real data and involving human interaction are required for the system's design and validation.