esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of Rice Seedings Using the YCrCb Color Space and an Improved Otsu Method
## 2 Materials and Methods  
### Experimental Fields and Rice Cultivars  
The test images in our paper were collected from Agricultural Research and Demonstration Base of Anhui Agricultural University, which is located in Anhui province, China (30.57 N, 117.01 E). In the field, rice (cultivar: Fengliangyou No.4, Huiliangyou 898, Zhendao No.18 and Shangnongjing No.2) was planted. Fengliangyou No.4 and Huiliangyou 898 are two types of hybrid indica rice and Zhendao No.18 and Shangnongjing No.2 are two types of conventional japonica rice, which are widely planted in southern China. The transplanting time and model are identical with those of local custom farm practices. The inter-row space was about 20-30 cm. The plant density was approximately 27 \(\times\) 10\({}^{4}\)-30 \(\times\) 10\({}^{4}\) hole/ha and 4-5 seedlings per hole. The total area of each rice cultivar was about 0.15 ha.
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 5 Conclusions  
The obtained results have shown that it is feasible to perform simultaneous orbital and attitude maneuvers by using the Drag Maneuvering Device as the only control means in conditions where physical and environmental parameters are uncertain.  
In particular, adaptive controllers for orbital and attitude maneuvers were integrated to perform roto-translational maneuvers in the presence of uncertainty in atmospheric density, drag/lift coefficients, location of the Center of Mass and inertia matrix. The uniformly ultimately bounded convergence of the attitude error and relative orbit states is guaranteed by the Lyapunov-based stability analysis. Validation through numerical simulation of a phasing maneuver with simultaneous attitude control requirement results in ultimate errors below 60 m and 6.5 degrees for the translational and attitude maneuvers, respectively.  
The algorithms developed together with the Drag Maneuvering Device could be particularly useful for providing small platforms, such as CubeSats, with propellant-less translational and attitude maneuverability. In-orbit inspection and servicing are envisioned as future applications for this technology.  
Future work will explicitly include the influence of flexible bodies on the spacecraft dynamics and will be considered in the controller design. Studies on how to incorporate compensation for time-varying uncertain parameters, as well as efforts on relaxing the requirement of using numerical algorithms to solve for the DMD lengths, are considered of great importance to improve the obtained stability result. Opportunities for improving the distribution of the control effort between chaser and target without necessarily centralizing the control algorithm have also been identified as topic of interest for future research efforts.  
Conceptualization, methodology, software, validation, formal analysis: C.R.-R. and A.F.; supervision, funding acquisition: R.B. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the Air Force Office of Scientific Research (AFOSR), United States of America award number FA9550-19-1-0169. Any opinions, findings and conclusions are those of the authors and do not necessarily reflect the views of the sponsoring agency.  
The authors declare no conflict of interest.  
Figure 15: FFT of the torques applied to the target (**left**) and chaser (**right**) spacecraft.
# Dynamic Path Planning for Vehicles Based on Causal State-Masking Deep Reinforcement Learning
## 4 Causal State-Masking TD3 Algorithm  
To enhance the convergence and generalization capabilities of DRL in path planning within complex dynamic environments, we integrate a causal state-masking mechanism into the TD3 algorithm, resulting in the CSM-TD3 algorithm. The TD3 algorithm is an advanced deterministic policy gradient method in DRL that effectively mitigates the problem of overestimation bias and improves the convergence speed and stability of the algorithm through the mechanisms of dual-value networks and delayed policy updates [40]. Building upon TD3, CSM-TD3 enhances the model's causal inference capabilities by introducing state masks and intervention mechanisms, enabling the agent to focus more on causal features that have the greatest impact on path-planning decisions, exhibiting greater efficiency and robustness in complex environments.  
In the standard actor-critic architecture [41], the actor network is responsible for generating policies, while the critic network evaluates the value of policies. Based on this framework, we design and modify the critic and actor networks by incorporating causal inference theory into the TD3 algorithm. The following sections detail the design of the critic and actor networks, as well as the overall steps of the algorithm.
## 1 Introduction
In this paper, we first develop a novel hybrid fingerprint model by combining camera intrinsic features with a specifically designed tag. To safeguard both the content and source of the images, we implement a privacy-preserving strategy. Following this strategy, we design a hybrid fingerprint model within an encrypted environment. Finally, we present the proposed distributed source camera identification scheme. Based on the binary hypothesis testing theory, we present a generalized likelihood ratio test (GLRT) to detail the source camera identification process performed by a single secondary user. The proposed scheme not only enhances identification accuracy, but also ensures the protection of sensitive information throughout the identification process.
## 4 Comparative Results
### Robustness Against Tampering Attack
For example, if the carrier video segment has a dimension of \(512\times 512\times 512\), then a data loss of 50% means cropping the carrier video segment with a \(256\times 256\times 256\) cube. Based on Figure 8, the proposed Octree-hiding technique has achieved acceptable recovering results. The reason is that the proposed Octree segmentation technique segments the host into cubes, and based on Figure 8 tampering is done in a cube in the top-left corner of the carrier signal. Therefore, any hidden data within that specific block will be destroyed. On the other hand, data that are embedded in other cubes will be intact. Figure 8: Examining the robustness of the proposed Octree technique against data tampering. The percentages shown in the figure are calculated based on the dimension of the tampered cube with respect to the dimension of the carrier frame.
# A Collaborative Control Method of Dual-Arm Robots Based on Deep Reinforcement Learning
###### Abstract  
Collaborative control of a dual-arm robot refers to collision avoidance and working together to accomplish a task. To prevent the collision of two arms, the control strategy of a robot arm needs to avoid competition and to cooperate with the other one during motion planning. In this paper, a dual-arm deep deterministic policy gradient (DADDPG) algorithm is proposed based on deep reinforcement learning of multi-agent cooperation. Firstly, the construction method of a replay buffer in a hindsight experience replay algorithm is introduced. The modeling and training method of the multi-agent deep deterministic policy gradient algorithm is explained. Secondly, a control strategy is assigned to each robotic arm. The arms share their observations and actions. The dual-arm robot is trained based on a mechanism of \"rewarding cooperation and punishing competition\". Finally, the effectiveness of the algorithm is verified in the Reach, Push, and Pick up simulation environment built in this study. The experiment results show that the robot trained by the DADDPG algorithm can achieve cooperative tasks. The algorithm can make the robots explore the action space autonomously and reduce the level of competition with each other. The collaborative robots have better adaptability to coordination tasks.  
deep reinforcement learning; multi-agent collaboration; dual-arm robot; coordinated manipulation +
Footnote †: journal: Applied Sciences
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Informal Security Analysis
#### 5.3.11 Key-Compromise Impersonation Resistance  
Even if the secret value stored in the SM is obtained by the adversary, since the session key of different smart meters is different, it will only cause the session key leakage between \(SM_{i}\) and \(DC_{j}\) in this period, and will not cause the session key leakage between \(SM^{\prime}_{i}\) and \(DC^{\prime}_{j}\). So, the scheme can resist key-compromise impersonation attack.  
We compared the proposed scheme with relevant representative schemes (i.e., [PERSON] et al. [2], [PERSON] et al. [13], [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [20]) in terms of security, and the comparison results can be seen in Table 1. The symbol \"\(\check{\mathsf{V}}\)\" represents that the scheme has this security feature, and \"\(\check{\mathsf{K}}\)\" indicates that the scheme does not meet or does not mention this security feature. From Table 1, it can be seen that the proposed scheme provides higher security and is more suitable for AMI scenarios.
## 4 CEL Modelling
### Discussion  
Figure 16 illustrates the comparison of internal energy (\(E_{i}\)) and kinetic energies (\(E_{k}\)) for forced replacement method and pile installation on deep mixing soil. The energy balance can be used to check the appropriate analysis in CEL technique. The ratio of kinetic energy to internal energy (\(E_{k}/E_{i}\)) should has the small fraction, typically meaning under 5%. For forced replacement methods, \(E_{k}/E_{i}\) of \(H=1\), 2 and 3 m were 2.7%, 0.1% and 0.5%, respectively, at the final analysis step. For pile installation on deep mixing soil, \(E_{k}/E_{i}\) of self-standing, pile jacking and pile driving were 1.9%, 1.9% and 1.6%, respectively. Based on the results of energy balance, it is found that all cases in the present analysis are in equilibrium and appropriate.  
Based on the results of CEL analyses, two ground improvement methods, i.e., the forced replacement method and the deep mixing method, can improve a certain soil strength, however,  
Figure 16: Comparison of internal and kinetic energies: (**a**) Forced replacement method; (**b**) pile installation on deep mixing soil.  
Figure 15: Vertical displacement with edges of pile equipment: (**a**) Self-standing of pile equipment; (**b**) pile jacking; (**c**) pile driving; and (**d**) comparison with methods of pile installation.  
there are the risks for constructability during constructions such as pile installation. In addition, the soil conditions composed of very deep soft soil in this site is unfavorable to economic feasibility. Therefore, the installation of temporary bridges is more economically analyzed than the two ground improvement methods, and the temporary bridges were actually installed during the real construction.
## 2 Materials and Methods
### Enzymatic Activity  
To study enzymatic activity, shrimp were sampled after the feeding experiment, and the hepatopancreata were dissected and stored in Falcon tubes in a freezer at \(-\)80 \({}^{\circ}\)C. The hepatopancreata were later homogenized into 1 mL of Milli-Q water using a pestle and mortar, with three hepatopancreas per tube, and centrifuged at 15,000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C. The supernatant (crude extract) was then collected for the analysis of amylase, trypsin, and chymotrypsin, which was performed in triplicate. Before performing enzymatic analyses, the crude extract was thawed and diluted 3000 times in ultrapure water [36].  
Trypsin and chymotrypsin activities were assessed using the synthetic substrates N-\(\alpha\)-Benzoyl-DL-arginine-p-nitroanilide (BAPNA) and N-succinyl-Ala-Ala-Pro-Leu-p-nitroanilide (SAAPLNA), respectively. Substrates were prepared in dimethyl sulfoxide (DMSO) at 2 mM and diluted in phosphate buffer (pH 7.5) in a 1:1 (\(v/v\)) ratio to obtain a final concentration of 1 mM. Reactions were performed by mixing 50 \(\upmu\)L of enzyme extract with 50 \(\upmu\)L of substrate solution. Absorbance was recorded every 15 min over a 60 min period at 30 \({}^{\circ}\)C. Enzyme activity (U) was defined as the conversion of 1 \(\upmu\)mol of the substrate per minute, determined using a standard curve of p-nitroaniline. Specific activity was expressed as U mg\({}^{-1}\) of protein.  
Amylase activity was determined using a 2% soluble starch solution in sodium acetate buffer (100 mM, pH 6.5) and the 3,5-dinitrosalicylic acid (DNS) method. A volume of 25 \(\upmu\)L of enzyme extract was mixed with 25 \(\upmu\)L of the substrate. Samples were incubated at 30 \({}^{\circ}\)C for 60 min, and the reaction was stopped every 15 min by adding 100 \(\upmu\)L of DNS. Tubes were heated at 100 \({}^{\circ}\)C for 5 min, followed by the addition of 100 \(\upmu\)L of distilled water. Absorbance was measured at 550 nm. One unit of amylase activity was defined as the release of 1 \(\upmu\)mol of glucose per minute based on a standard glucose curve. Specific activity was expressed as U mg\({}^{-1}\) of protein.  
Lipase activity was analyzed using umbelliferyl oleate dissolved in acetonitrile at a final concentration of 1 \(\upmu\)M. Reactions were carried out in 96-well plates by mixing 50 \(\upmu\)L of enzyme extract with 50 \(\upmu\)L of the substrate. Fluorescence was recorded every 2 min for 20 min using excitation at 355 nm and emission at 460 nm. One unit of lipase activity was defined as the release of 1 \(\upmu\)mol of methylumbelliferone per minute. Specific activity was expressed as U mg\({}^{-1}\) of protein.  
All measurements were performed using a microplate reader (Infinite Pro, TECAN, Mannedorf, Switzerland). Absorbance was measured at 410 nm for trypsin and 550 nm for chymotrypsin and amylase. Lipase fluorescence was measured at the specified excitation/emission wavelengths.
## 1 Introduction
U-Nb alloys exist in the BCC structure only at elevated temperatures, but the metastable phases, \(\alpha^{\prime}\), \(\alpha^{\prime\prime}\), \(\gamma^{0}\), and \(\gamma^{s}\), can be obtained by rapid quenching to room temperature, as has been discussed above [4; 7; 9; 19; 27; 30; 31; 32; 34]. [PERSON] et al. [29] found that increasing a Nb equivalent (Nb\({}_{\text{eq}}\)) of the U-6 wt.% Nb alloy (adding some small amount of titanium at the expense of uranium) promotes the formation of the \(\gamma^{0}\)-tetragonal phase at the expense of the \(\alpha^{\prime\prime}\)-monoclinic phase, resulting in a doubling of the yield strength relative to the water quenched (WQ) \(\alpha^{\prime\prime}\) phase and a strain induced transformation to the \(\alpha^{\prime\prime}\) phase with superelastic strains to 4.5%. Here, we address the question of the phase stability in the U-Nb and U-Nb-Ti alloys from first-principles theory. We note that this approach is very accurate for both components of the alloy, namely, uranium and niobium. The ground-state phases are correctly predicted for both uranium [38] and niobium [39], and one can assume that the corresponding alloys are also accurately modeled within the same theoretical framework. We also use first-principles theory to confirm that 25 at.% of BCC Nb (4d-transition metal) can stabilize the BCC-like metastable \(\gamma^{s}\) phase in the U\({}_{3}\)Nb compound that was discovered by [PERSON] and [PERSON] [34] and modeled by [PERSON] et al. [35]. Uranium belongs to the series of actinide metals that display many interesting properties that are primarily driven by the nature of the 5f electrons that dominate the chemical bonding in all naturally occurring actinides [37]. Fortunately, the electronic-structure theory founded on density-functional-theory (DFT) is very suitable and quite accurate in describing these metals. In Section 2, we briefly introduce two techniques that are based on DFT to calculate, at zero temperature, the total energy of a particular metal, alloy, or compound. The two techniques are complimentary, as one is more accurate for metals and compounds while the other has the capability to treat disordered alloys efficiently and accurately. The results are presented in Section 3, where the two computational techniques are utilized and compared to available experimental data. Finally, we conclude and summarize our investigation in Section 4.
###### Abstract  
This paper describes a singly-fed circularly polarized rectangular dielectric resonator antenna (RDRA) for MIMO and 5G Sub 6 GHz applications. Circular polarization was achieved for both ports using a novel-shaped conformal metal strip. To improve the isolation between the radiators, a \"S\" shaped defective ground plane structure (DGPS) was used. In order to authenticate the estimated findings, a prototype of the suggested radiator was built and tested experimentally. Over the desired band, i.e., 3.57-4.48 GHz, a fractional impedance bandwidth of roughly 36.63 percent (\(-10\) dB as reference) was reached. Parallel axial ratio bandwidth of 28.33 percent is achieved, which is in conjunction with impedance matching bandwidth. Between the ports, isolation of \(-28\) dB is achieved Gain and other far-field parameters are also calculated and found to be within their optimum limits  
s 2022
## 2 Methods
### Analytical Methods  
The most popular methods used for calculating the horizontal displacements of typical reinforced soil structures are shown in Table 1. The solutions that are commonly used are not very new, with most having been developed in the 1990s. In the 1990s, the level of professional knowledge was different and more limited. All of the factors/parameters that strongly influence the behavior of RS structures were not considered. Typically, they neglect the effects of the time-dependent properties of the materials used as reinforcement. Thus, the results obtained using these methods are usually not very accurate [77]. More detailed information about these analytical methods is presented by [PERSON] and [PERSON] [40].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Reference** & **Basic Correlations** & **Assumptions** & **Notations** \\ \hline
[81] & \(\delta_{h}=\frac{\varepsilon_{L}L}{2}\) & \(\delta_{h}\)—horizontal deformation of RS walls & the strain limit is established \\  & & \(\varepsilon_{d}\)—assumed strain limit & as less than 10\% \\ \hline
[73] & \(\Delta_{h}=\left(\frac{1}{2}\right)\left(\frac{P_{\text{max}}}{K_{\text{real} }}\right)\left(H-z_{l}\right)\). & \(\Delta_{h}\)—deformations of RS wall & \\  & \(\cdot\left[\tan\left(45^{0}-\frac{y}{2}\right)+\tan\left(90^{0}-\phi_{ds}\right)\right]\) & \(p_{m}\)—max. force & \(\frac{L}{H}\geq 0.7\); \\  & & \(\Psi\)—dilatation angle of soil used as backfill & \\ \hline
[82] & \(D_{L}=\frac{2h_{\text{real}}D_{z}}{\varepsilon_{L}}=\frac{D_{\text{max}}}{ \varepsilon_{\text{real}}}=2\varepsilon_{v}\) & \(D_{V}\)—vertical deformation & the same values of soil and reinforcement horizontal \\  & & \(\varepsilon_{L}\)—horizontal strain & deformations are assumed \\  & & \(\varepsilon_{v}\)—vertical strain & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Short description of the most popular analytical methods used to calculate the horizontal deformations of reinforced soil walls.  
Figure 2: Methods used to investigate deformations in RS walls.
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Higher Level: Highly Analysis Ready Data
#### 3.3.3 Level 3: Highly Analysis Ready Data  
The FORCE Level 3 Processing System (FORCE L3 PS) temporally condenses multi-temporal observations into a more controllable amount of spatially complete data, which lowers the usage barrier compared to Level 2 ARD. Thus, these data are referred to as highly analysis ready data (hARD). hARD products have undergone the necessary processing required for many machine-learning-based land cover/change classification purposes, which put spatial completeness before temporal exactness. Acquisition dates and quality flags of Level 2 ARD are retained as suggested by [44].  
FORCE L3 PS is capable of producing best-available-pixel composites [45] and spectral temporal metrics [46] (Figure 8). Both concepts utilize all available observations within a defined temporal window; best-available-pixel composites are produced by selecting the optimal observation with respect to defined criteria, whereas spectral temporal metrics are produced by a statistical description of all available spectral observations. Composites are optimal to preserve spectra for physical interpretation, but are often noisier than spectral temporal metrics. Spectral temporal metrics are produced band wise, thus physical interpretability is limited. However, they provide rich information on temporal variability and data distribution and are thus ideal predictors for machine-learning techniques that require independent features. However, their quality is closely related to data availability as a sufficient number of clear sky observations (in dependence of the statistical moment) are required to produce reliable statistics.  
Figure 7: Processing scheme of FORCE clear sky observations (CSO).  
FORCE employs a parametric weighting scheme [45] as implemented in [47]. For each pixel, the observation with the highest total score is selected for the best-available-pixel composite. Only highest-quality pixels are considered, i.e., observations with very low cloud or haze score are discarded. Similarly, observations with very low seasonal score are discarded, which ensures that Level 3 products are representative of the season of interest (can be switched off to produce annual products). The best-available-pixel composite composites can either be parameterized using a static target date [45] or by inputting a land surface phenology dataset to dynamically adapt the target dates for each pixel [47] (example: Figure 9). Over persistent water, the compositing algorithm is switched to minimum shortwave-infrared (SWIR2 band) compositing, as the parametric weighting selection is often noisy due to the high temporal variability of water reflectance. Currently implemented spectral temporal metrics are the per-band average, standard deviation, minimum, maximum, range, skewness, kurtosis, median, 25/75% quantiles, and interquartile range of reflectance.  
Figure 8: Processing workflow of the FORCE Level 3 Processing System (L3 PS). STM—spectral temporal metric; BAP—best-available pixel composite.