esa-sceva/satcom-chunk-collection huggingface dataset:
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 7 Nematodes
Combining EPNs with other control tactics has been shown to be effective (see Synergistic Interactions section) which may increase their popularity in the future. As mentioned, epizootics have been reported but data on natural infections are very limited. [PERSON] [207] reviewed reports of natural EPN infections that ranged from persistently stable with moderate infection rates to epizootics that resulted in negative density-dependent impacts on the host population. Of the documented reports, only two involved scarabs; the garden chafer (_Phyllopertha horticola_) ([PERSON], 1994) in the Netherlands and a _Leppidiota_ complex [223] in Germany. Both occurred on sugarcane and involved _Heterohabditis_ species that caused 70-80% infection rates. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Nematode Species**} & \multicolumn{8}{c}{**Field Effectiveness**} & \multicolumn{8}{c}{**Pot Effectiveness**} \\ \cline{2-13}  & **P** & **C** & **A** & **A** & **C** & **P** & **A** & **A** & **M** & **P** & **C** & **A** & **A** & **C** & **P** & **A** & **A** & **M** \\  & **H** & **L** & **T** & **P** & **T** & **J** & **O** & **M** & **C** & **H** & **L** & **T** & **P** & **T** & **J** & **O** & **M** & **C** \\ \hline \multicolumn{13}{c}{Heterohabditidae} \\ \(H\)_bacteriophora_ & x & x & & x & & x & - & - & x & x & x & x & x- & x & - & - \\ \(H\)_zalandica_ & & x & & & x & & & - & x & & & & x & & & & \\ \(H\)_megidis_ & - & & & - & & & & x & x & & & - & - & & & \\ \(H\)_meralatus_ & & & & & & & & & & - & & & & - & & & \\ \(H\)_heliothidis_ & & & & & & & & & & & - & & & & & & \\ \(H\)_indicata_ & & & & & & & & & & x & - & & & & & & & \\ \hline \multicolumn{13}{c}{Steinrematidae} \\ \(S\)_s. glaseri_ & - & - & & & x & x & - & & x & x & x & x & x- & x & x & - & - \\ \(S\)_scarabaei_ & x & x & & & x & x & x & x & x & x & & - & x & x & x & x \\ \(S\)_s. arrapocarpas_ & x & & x & & x & x & & - & - & & & x & & & & \\ \(S\)_s. arenarium_ & & & & - & & & & & & & & & & & & \\ \(S\)_tushidai_ & & x & & & x & & & & x & & & & - & x & & - \\ \(S\)_feltiae_ & x & & & & - & & & x & - & & & - & & & & \\ \(S\)_longicaudanu_ & & & & & & x & & & & & & & & & & & \\ \(S\)_krausei_ & & x & & & - & & & & & & & & & & & & \\ \(S\)_ribbrave_ & & &Nematodes can persistent for years after application but usually do so at low levels unless hosts are continually present [224]. Many abiotic factors, such as temperature, moisture, and soil type, as well as biotic factors, can limit persistence [221; 225; 226]. Due to this, the use of entomopathogenic nematodes as biocontrol agents would likely require multiple applications.
## 3 Related Definition
### Definition of FRCPN and Definition of FRCPN Activity  
**Definition 6**.: _Formal definition of FRCPN based on semantic similarity for Web service composition [16]:_  
\[FRCPN=(P,T,F,\mu,PR,Q).\]  
(1) \(P=\{P_{1},P_{2},\cdots,P_{n}\}\) represents the place set, and each place represents a Web service.  
(2) \(T=\{T_{1},T_{2},\cdots,T_{m}\}\) represents the transition set, and each transition represents the matching relationships among Web services.  
(3) \(F\) is a dependence relation set, represented as an If-Then form.  
(4) \(\mu\) is a function of \(T\), and the function's value is in the range of [0,1], which is the triggered threshold of the transition. The function is also the semantic similarity threshold for Web services, and we can control the minimum triggered threshold using the function.  
(5) \(PR\) is the precondition set defined on the \(P\), which represents all preconditions being satisfied before the transitions are triggered.  
(6) \(Q\) is the QoS set defined on the \(P\); it indicates all QoS of the Web services that need to be satisfied before the transitions are triggered.  
We can measure the matching degree using semantic similarity between the interfaces of two Web services, and then establish the dependence relation. There are five kinds of dependence relations in FRCPN. The semantic similarity calculation considers some factors, including \(I/O\) interfaces \(P\), \(Q\), etc., so we adopt semantic similarity as reliability. It is necessary that the reliability value of the transition achieve semantic threshold \(k\) as a trigger.  
**Definition 7**.: _FRCPN activity. In FRCPN, if \(t\in T\), and a transition sequence exists, the transition sequence is triggered in sequence, and in semantic threshold \(k(0\leq k\leq 1)\), the reliability value \(\Gamma\) of each transition \(t\) is satisfied with \(\Gamma\geq k\), then transition \(t\) is triggered, we consider that transition \(t\) is active in semantic threshold \(k\). According to the dependence relations in FRCPN, we can calculate the reliability values of the Web services and the transitions. We use the reliability value of the end Web service in FRCPN as the reliability value \(\omega\) of FRCPN. In the reliability threshold \(a(0\leq a\leq 1)\), all of the transitions in FRCPN are active in semantic threshold \(k\), and the reliability value \(\omega\) of FRCPN satisfies \(\omega\geq a\), so FRCPN is activity in semantic threshold \(k\) and reliability threshold \(a\)._
# Efficient and Robust Image Communication Techniques for 5G Applications in Smart Cities
## 3 Model Description
### Maximal Ratio Combining (MRC)  
In MRC diversity scheme, the output is obtained by summation of all branches that are weighted by different weight parameters, the \(\rm\alpha_{i}\)s in Figure 2 are all zero. Subsequently, the signals are co-phased, \(\rm\alpha_{i}=\alpha_{i}\,e^{-i\theta_{i}}\), where \(\rm\theta_{i}\) is the phase of the incoming signal in the ith branch. The envelope of the combiner output is presented in Equation (3). It represents that the received signal is the submission of the input signal coming from different branches, i.e., \(\rm r_{i}\) multiplied by different weight parameters.  
\[\rm r=\sum\
olimits_{i=1}^{M}\rm 4_{i}\rm r_{i} \tag{3}\]  
Figure 2: Linear Combiner.  
Supposing the same Noise Power Spectral Density (PSD) N\({}_{0}\) in each branch yields a total noise PSD. N\({}_{\text{tot}}\) at the combiner output of and is depicted in Equation (4).  
\[\text{N}_{\text{tot}}=\sum_{\text{i}=1}^{\text{M}}\text{a}_{\text{i}}^{2}\text{N }_{0} \tag{4}\]  
N\({}_{\text{tot}}\) represents the total noise power at the receiver side. It is very evident from Equation (4) that the total noise power also depends upon the weight parameters. The weight parameters are decided to boost the signal that contains the information components and attenuate the signal, which contains noise components. Therefore, the output SNR of the combiner is presented in Equation (5). It depicts that the SNR is the ratio of the weighted received signal and weighted noise signal of all the branches. The weight parameters must be optimally defined to have maximum SNR.  
\[\gamma_{\Sigma}=\frac{\text{r}^{2}}{\text{N}_{\text{tot}}}=\frac{1}{\text{N}_{ 0}}\frac{\left(\sum_{\text{i}=1}^{\text{M}}\text{a}_{\text{i}}\text{r}_{\text {i}}\right)^{2}}{\sum_{\text{i}=1}^{\text{M}}\text{a}_{\text{i}}^{2}} \tag{5}\]  
To maximize the \(\gamma_{\Sigma}\), the optimum selection of \(\alpha_{\text{i}}\)s is required. The optimum selection should be done so that the branches having higher SNR must be weighted more compared to branches having lower SNR values. This weighting strategy will result in making the weights \(\text{a}_{\text{i}}^{2}\) proportional to the branch SNRs \(\text{r}_{\text{i}}^{2}/\text{N}_{0}\). The values of \(\alpha_{\text{i}}\)s that will maximize the \(\gamma_{\Sigma}\) can be obtained by taking the partial derivatives of Equation (6) or using the Swartz inequality. Unraveling for the optimal weights yields \(\text{a}_{\text{i}}^{2}=\text{r}_{\text{i}}^{2}/\text{N}_{0}\), and the resultant combiner SNR becomes:  
\[\gamma_{\Sigma}=\sum_{\text{i}=1}^{\text{M}}\text{r}_{\text{i}}^{2}/\text{N}_{ 0}=\sum_{\text{i}=1}^{\text{M}}\gamma_{\text{i}} \tag{6}\]
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using On-Policy
#### 3.1.7 Robot Control II  
In [65], the authors developed a framework for robots to navigate staircases in both upward and downward directions. This work addresses the challenges of autonomous robot navigation in complex environments, particularly focusing on staircase traversal. The authors developed controllers that can undergo simulation training and transfer trained models to physical robots; this process enables the robot controller to train faster and in varied environment settings.  
The authors in [65] utilized RL to develop robot controllers that can navigate stairs efficiently and safely. The authors used the Gazebo simulator to train their robot based on the PPO algorithm to train the robot movements, which receives rewards or penalties based on the direction of the move, as shown in Figure 6. The authors meticulously designed reward functions to motivate task completion and ensure adherence to safety measures, including reducing the likelihood of tipping over or excessive shaking while navigating stairs in both upward and downward directions.  
The above-mentioned work has significantly advanced the field of robotics through the development of a powerful and resilient framework tailored for staircases. The authors' thorough examination of reward function design, policy comparison utilizing KL Divergence, and evaluation of real-world experiment performance offer valuable insights for  
Figure 6: Simulation-in-loop block diagram for RL-learned staircase navigation. For more information, please refer to Table 4.  
the field of robotics and RL, enhancing the progress toward creating robots that are more adaptable and capable. The work has substantial implications across diverse applications, including search and rescue missions, services for people with mobility challenges, and operations in hazardous environments.
## 3 Results
### QSAR Predictions for DKP Carcinogenicity  
The identified degradation impurity was subsequently evaluated with respect to its cancer risk. The performed QSAR analysis provided predictions for DKP mutagenicity, genotoxicity and carcinogenicity using different in silico models, as reported in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compound** & **Chemical Formula** & **Calculated Molecular Mass** & **Observed \(m\)/z Value, ES–/ES+** \\ \hline Degradation impurity (DKP) & C\({}_{23}\)H\({}_{30}\)N\({}_{2}\)O\({}_{4}\) & 398 & 399/397 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The \(m\)/z values of [M – H]\({}^{-}\) [M + H]\({}^{+}\) for DKP and RAM.  
Figure 5: RAM degradation pathway under dry air conditions.  
The provided ADI value was used to measure the reliability of the prediction in a manner described in the Section 2. It corresponded with the wording result expressed as 'low','moderate' or 'high'. Furthermore, the provided ES for each model were unified into average endpoint scores (ES\({}_{\text{av}}\)) for respective endpoints (mutagenicity, carcinogenicity, genotoxicity), calculated as an arithmetical mean. They were the following: ES\({}_{\text{av}}\) for DKP non-mutagenicity equaled 0.2, for carcinogenicity models, ES\({}_{\text{av}}\) was 0.5, while a combined ES\({}_{\text{av}}\) for genotoxicity was 0.6. ES\({}_{\text{av}}\) were used to make a final decision on respective endpoints' reliability in a manner described in the Section 2. Unfortunately, the VEGA software was unable to predict the activity of DKP in in vivo micronuclele assays. In summary, DKP was predicted to be non-mutagenic with high reliability (ES\({}_{\text{av}}\) 0.2), carcinogenic with insufficient reliability (ES\({}_{\text{av}}\) 0.5) and genotoxic with insufficient reliability (ES\({}_{\text{av}}\) 0.6). Thus, the direct mutagenicity of DKP was excluded. The remaining results, although alarming, were not conclusive. For this reason, further toxicological assessment in an in vitro setting was conducted.  
In addition to this, the same simulations were performed for a structural analog of DKP-IMD-DKP. These results are available in Supplement S5: QSAR predictions for IMD and IMD-DKP carcinogenicity, genotoxicity and mutagenicity. Here, the calculated ES\({}_{\text{av}}\) for respective endpoints were the following: 0.15 for non-mutagenicity prediction, 0.3 for non-carcinogenicity and 0.5 for non-genotoxicity. The individual predictions for chromosomal aberration assay and in vivo micronuclele assay were negative, with moderate reliability. This suggests that IMD-DKP is neither mutagenic nor carcinogenic, while its genotoxic effect, despite the insufficient reliability of the model, is rather unlikely.  
\begin{table}
\begin{tabular}{c c c c} \hline
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 4 Applications of Chitosan-Based Nanocomposites
### Water Purification
The selective adsorption properties of these composites were investigated for six dyes, i.e., methylene blue (MB), malachite green (MG), amaranth (AM), acid orange 10 (AO10), Congo red (CR), and acid blue74 (AB74). The synergistic effects of amino/hydroxyl-containing functional groups from CS, sulfonic groups from LS, and nitrogen-containing functional groups from PPY chains resulted in high adsorption performance (feasible and spontaneous) for dyes with the PPY-CS-LS composite.
## 4 Discussion
Additionally, COSMIC observations detected ionospheric disturbances south of Beirut extending up to 10\({}^{\circ}\) latitude in the conjugate hemispheric regions. These conjugate ionospheric Ne disturbances at (30\({}^{\circ}\)N, 20\({}^{\circ}\)S) around UT \(\sim\)15.5 h suggest the role of ionospheric electric fields generated at the northern hemisphere (at 30\({}^{\circ}\)N), which are instantaneously mapped along magnetic field lines to the southern hemisphere (20\({}^{\circ}\)S), subsequently initiating disturbances through the **E**\(\times\)**B** process [8; 17]. It is worth noting that Ref. [17] suggested that the polarisation electric field for the observed airglow event propagates normal to the medium-scale TIDs and is instantaneously transmitted to its conjugate point, leading to ionospheric drift. These disturbances are mirrored in both hemispheres. Accordingly, we ensured that these disturbances were in the conjugate hemispheric region using the previously performed IGRF mapping technique. Subsequently, we investigated the frequency-domain features of these disturbances in both the hemispheres. The wavelet transformation of the electron density shown in Figure 12 clearly demonstrates that both the Swarm and DMSP Ne data exhibit similar periodic oscillations. These oscillations, observed by both satellites and in both hemispheres, have horizontal wavelengths of the dominant period in the range of 64-128 s. The observed similarities suggest that the electric field generated in the Northern Hemisphere propagates along magnetic field lines to the Southern Hemisphere, inducing electron density perturbations with oscillatory features that mirror those observed in the Northern Hemisphere. In contrast, the later disturbances (UT = 18.1 h) at 10\({}^{\circ}\)N suggest that the propagated TIDs continued travelling southward over large radial distances from the explosion site. The observation of disturbances at higher altitudes, as detected by COSMIC, further supports the interpretations of [29], who attributed the sustained ionospheric disturbances at the altitudes of the DMSP to the lower ionospheric density, which maintained large TIDs amplitude over long distances. Further investigations and analyses are crucial for confirming whether the generation of ionospheric disturbances at equatorward sites after the explosion is related to TIDs or other mechanisms. Figure 11: COSMIC trajectory and profiles at UT = 15.5 h in (**a**) and (**b**), and at UT = 18.1 h in (**c**) and (**d**), respectively.
## 4 Conclusions  
We presented the new URSA-PQ instrument which is now available for general users at the FLASH FEL facility. We described its major design features and its flexibility because of its adherence to CAMP instrument dimensions. The instrument is already equipped with a molecular source and a magnetic bottle spectrometer, allowing for ultrafast X-ray probe studies of photoexcited molecular dynamics. We demonstrated the current spectral resolution of the MBES of 1/40 using the M4,5 NN Auger decay of Kr in the range of 30-45 eV electron energy. This can be improved further by treating electron signals digitally using a combination of an edge finder and time-to-digital conversion. We investigated the time-resolution in conjunction with the FLASH 2 pump-probe laser delivering 266 nm pulses at FL 24. For that purpose, we used a UV-induced shift of the sulfur 2p photoelectron line of the molecules 2-thiouracil. We find a temporal resolution of 190 fs using photoelectron features from UV excited 2-thiouracil.  
Figure 5: (**a**) Experimental photoelectron spectrum showing the sulfur-2p photonic of 2-thiouracil taken with a photon energy of \(\sim\)270 eV. (**b**) Increase of the absolute value in the difference signal \(|(\text{UV}_{\text{on}}-\text{UV}_{\text{off}})|\) as a function of pump-probe delay (blue dots) and fit by the model explained in the text (orange line).  
**Author Contributions:** Instrument design, [PERSON] and M.G.; MBES design, [PERSON] with help of [PERSON] and [PERSON]; realization of the instrument: [PERSON], D.M., F.L., M.N., M.S.R., S.D., [PERSON]; instrument software, [PERSON] with help of [PERSON], performing experiments, all authors; analyzing data, [PERSON], [PERSON] with help of [PERSON]; writing paper: [PERSON], [PERSON], [PERSON] and [PERSON]; comments and suggestions on the paper drafts, all authors. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The URSA-PQ instrument and the position of [PERSON] was funded by the German Federal Ministry for Education and Research (BMBF) under 'Verbundforschungsprojekt 05K16 IP1'. The Potsdam Research Group is funded by a Lichtenberg Professorship of the Volkswagen-Stiftung. We acknowledge DFG funding via grant GU 1478/1-1. We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Part of this research was carried out at FLASH2. [PERSON] and R.J.S. received funding from the Swedish Research Council and the Knut and Alice Wallenberg Foundation, Sweden. We acknowledge the support of the Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of University of Potsdam.  
**Acknowledgments:** We acknowledge [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] for participating in the experiments on thiouracil.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Experimental Scenario
### Performance Comparison  
The tracking performance comparison is provided in Figure 10.  
The result shows that the system response due to the traditional SMC is much slower to reach the steady state as shown in Figure 10. After reaching the steady state, we can see that the vehicle is tracking the target speed well even in the nonlinear scenario, this shows us the natural behavior of sliding mode controllers in handling external disturbances. However, the main drawback of the traditional SMC can be seen evolving in its control input over time. At the same time, it is noticed that the chattering is attenuated using the proposed control method refer to Figure 10. Therefore, it is noticed that the tracking performance is improved by applying the proposed method.  
Figure 9: Proposed method performance analysis at different desired speeds.
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 6 Comparison of the Used Commercial Software
### Abaqus  
The CAD module of ABAQUS is quite complex and demands many inputs from the CAD designer. The created 3D models can be easily exported to different file types or transferred to the FEM module for pre-processing. The FEM module here is also complex and quite demanding. However, it contains any possible type of analysis, load case, and boundary condition. In addition, it offers different finite elements as well as meshing properties.  
The TO module of ABAQUS is its strongest part. A plethora of design values, called design responses, can be used for either objective functions or constraints, making the ABAQUS TO module a powerful optimization tool that can solve complex multi-optimization problems. However, the available manufacturing constraints in the TO module are only focused on the design for CPM and not for AM. The optimized designs are faceted geometries and can be exported as STL files. Stress, displacement, and strain plots of the optimized designs are available. However, as in the case of SolidWorks, these are estimations and not the actual values found from a validation study. However, ABAQUS does not have the appropriate tools to support either the redesign or the 3D preparation of the optimized geometries. Thus, the post-processing of the results is not possible in ABAQUS, making it dependent on other software for the implementation of the validation study.  
ABAQUS created the lightest designs in all case studies except the bridge, where its optimization solution was the worst in terms of weight reduction compared to the other two software platforms. Furthermore, the TO in ABAQUS is time demanding since the optimization of the models lasted five to eight times more than the other software.  
The authors' intention with this comparison was not to promote one software platform above another but to present the capabilities and limitations of their TO modules. All three of these software platforms are competent and provide the CAD designer with useful design, analysis, and optimization tools. As general guidance, it could be claimed that ABAQUS is more of a TO software platform due to its plethora of available options. Complex multi-optimization problems can be solved here. However, the solution of the optimization tasks is time demanding in ABAQUS. On the other hand, ANSYS is a simulation platform with many capabilities. The ANSYS TO module contains level set and lattice optimization, which are not available in the other two platforms. ANSYS creates balanced designs in terms of weight and optimization time. Furthermore, it offers remarkable post-processing tools for the optimized designs for both CPM and AM. Finally, SolidWorks is more of a CAD/FEM platform. The SolidWorks TO module is quite new and is limited to a few optimization options. However, it is quite efficient in terms of time and is recommended for big structures, such as the optimized bridge in this paper, where SolidWorks created the lightest design.