esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Pointing and Stabilizing Manipulator for Optical Space Payloads
## 2 Characteristics and Algorithms for Large-Scale Pointing Adjustment
### Algorithm of Large-Scale Adjustment
Considering that the dynamics modes of a large spacecraft are numerous and complex, the dynamic coupling of \(\mathbf{A}(s)\) and \(\mathbf{C}(s)\) would raise difficulties in controller design. Fortunately, there is an approximation for the case of large spacecraft and small payloads. Define mobile-base factor \(v\) as  
\[v=\left\|\mathbf{U}_{22}^{-1}(s)\mathbf{U}_{21}(s)\right\|_{\infty}=\left\| \mathbf{Y}(j\omega)\right\|_{\infty}=\underset{\omega\in\mathbb{R}}{\sup} \sigma(\mathbf{Y}(j\omega)) \tag{41}\]  
Combining Formula (30), the mobile-base factor of the nominal system could be estimated as  
\[v\approx\sigma\left(\mathbf{M}_{\epsilon b}^{-1}\mathbf{M}_{cbf}\right) \tag{42}\]According to Formulas (27) and (28), the norm of \(\mathbf{M}_{eb}\) mainly depends on the total mass of the support module and the payload, while \(\mathbf{M}_{ebf}\) mainly depends on the mass of the payload. For the case of large spacecraft and small payload, \(\left(\mathbf{M}_{b}+\mathbf{M}_{\beta}\right)\gg\left(\mathbf{M}_{p}+\mathbf{ M}_{a}\right)\) and then \(v\approx 0\), i.e., \(\mathbf{Y}(s)\approx\mathbf{O}\). Consider the above facts and then \(\mathbf{C}(s)\) and \(\mathbf{A}(s)\) could be rewritten as  
\[\mathbf{C}(s)=-\mathbf{Y}(s)\mathbf{G}_{11}(s)=-\mathbf{Y}(s)(\mathbf{U}_{11}( s)-\mathbf{Y}(s)\mathbf{U}_{21}(s))^{-1} \tag{43}\]  
\[\mathbf{A}(s)=\left(\mathbf{J}_{\theta f}-\mathbf{J}_{\theta b}\mathbf{Y}(s) \right)\!\left(\mathbf{U}_{11}(s)-\mathbf{Y}(s)\mathbf{U}_{21}(s)\right)^{-1} \tag{44}\]  
If \(\mathbf{Y}(s)\approx\mathbf{O}\), then each sub-matrix of the transfer function could be approximated as  
\[\mathbf{C}(s)\approx\hat{\mathbf{C}}(s)=\mathbf{O} \tag{45}\]  
\[\mathbf{A}(s)\approx\hat{\mathbf{A}}(s)=\mathbf{J}_{\theta f}\!\left(s^{2} \mathbf{M}_{ef}+s\mathbf{C}_{f}+\mathbf{K}_{f}\right)^{-1} \tag{46}\]  
It is seen that the approximate active control characteristics of the manipulator \(\hat{\mathbf{A}}(s)\) is no longer affected by support modules modes.
###### Abstract  
Low-orbit satellite communication networks have gradually become the research focus of fifth-generation (5G) beyond and sixth generation (6G) networks due to their advantages of wide coverage, large communication capacity, and low terrain influence. However, the low earth orbit mega satellite network (LEO-MSN) also has difficulty in constructing stable traffic transmission paths, network load imbalance and congestion due to the large scale of network nodes, a highly complex topology, and uneven distribution of traffic flow in time and space. In the service-based architecture proposed by 3 GPP, the introduction of service function chain (SFC) constraints exacerbates these challenges. Therefore, in this paper, we propose GDRL-SFCR, an end-to-end routing decision method based on graph neural network (GNN) and deep reinforcement learning (DRL) which jointly optimize the end-to-end transmission delay and network load balancing under SFC constraints. Specifically, this method constructs the system model based on the latest NTN low-orbit satellite network end-to-end transmission architecture, taking into account the SFC constraints, transmission delays, and network node loads in the end-to-end traffic transmission, uses a GNN to extract node attributes and dynamic topology features, and uses the DRL method to design specific reward functions to train the model to learn routing policies that satisfy the SFC constraints. The simulation results demonstrate that, compared with graph theory-based methods and reinforcement learning-based methods, GDRL-SFCR can reduce the end-to-end traffic transmission delay by more than 11.3%, reduce the average network load by more than 14.1%, and increase the traffic access success rate and network capacity by more than 19.1% and two times, respectively.  
LEO satellite network; routing; service function constraints; graph convolution network; deep reinforcement learning +
Footnote †: journal: Physics Letters A  
Article
# A Metric for Secrecy-Energy Efficiency Tradeoff Evaluation in 3 GPP Cellular Networks
## 3 Metrics for the Evaluation of the Effective Secrecy-Energy Efficiency Tradeoff  
In this section, we propose two new metrics for the evaluation of the tradeoff between the width of the surface of the cell where a target secrecy rate can be delivered and the power spent to obtain it.
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 4 Performance Evaluation  
### Performance Evaluation  
To demonstrate the superiority of the scheme proposed in this study, we conducted performance evaluations of the safety message dissemination scheme in an urban environment. For this, two experimental evaluations were conducted. The first compared the packet loss of safety messages when cluster candidates were maintained by the cluster candidate algorithm and not maintained. The second compared the number of RSU processing requests and duplicate messages when all vehicles requested safety messages from the RSU according to the vehicle request algorithm and when clusters were used to request safety messages in a V2I environment. Table 1 shows the environments in which the performance evaluations were conducted.  
This study implemented a safety message dissemination environment proposed according to the communication standards of VANET. Table 2 shows the parameter values applied in the performance evaluations. The performance evaluations were implemented with the Python programming language. The values of the parameters, such as communication period, transmission rate, and message size, were implemented according to the communication standards of VANET. We performed comparative evaluations using the same message size and communication environment as previous studies.
## 3 Results and Discussion
### Coral Health Predictions from Lab Data  
Prior to using the lab-derived models to predict field coral bleaching susceptibility, we first ensured that the AI could distinguish field corals of the various fragment health designations (Figure 4c); note that this analysis featured the fourth, high-temperature-acclimating fragment health designation. As with the lab data, PLS-DA could resolve these phenotypes at 100% accuracy when using only 16 proteins. In contrast, PLS-DA would _not_ be effective at predicting the colony health designation (Figure 3); indeed, the \(p\)-value derived from the NP-MANOVA of the 16 proteins versus the three field coral colony health designations was 0.94 (Table S1). In contrast, a neural network (Figure 4d) trained with only those five host coral proteins in both the lab and field coral datasets was 94% accurate. This finding means that, were one to measure the concentrations of these five proteins in a random _O. faveolata_ sample that had not yet manifested any signs of bleaching and input these data into the neural network depicted in Figure 4d (& described in Table 3 as \"CHD-lab-CHD-field(a)\"), the AI would correctly predict whether the coral would bleach (bleaching-susceptible), partially bleach (intermediate), or resist bleaching in 94% of instances.  
We therefore took a more detailed look at this single-hidden layer model and ran 20 additional simulations in which both training and validation data were used to calculate the misclassification rate (representing a more conservative approach). Upon doing so and then averaging the collective misclassification rates across all 40 simulations (including the original 20, in which misclassification rates were only calculated from the validation samples held back from the AI), the resulting accuracy was 86% (Table 3); although having decreased slightly, this predictive confidence nevertheless surpassed our 80% threshold.  
A dependent resampled inputs analysis was undertaken to determine which of the five proteins was more heavily weighted in the model, and a DELTA-actitoxin involved in prey envenomation was characterized by a two-fold greater effect size than the second most influential protein (an E3 ubiquitin protein ligase; Table 4). Although it is tempting to develop a story of coral resilience around prey caption and heterotrophy based on this finding, it is important to note that, in a non-parametric one-way ANOVA, the concentration of this protein was statistically significant only at an alpha of 0.05 (\(p\) = 0.03; Table 4), and the difference between bleaching-resistant versus bleaching-susceptible corals was only 50% (former > latter). In other words, concentrations of this protein in isolation would be insufficient to resolve differences in bleaching susceptibility, even if it does imply a role of coral heterotrophy (which could be hypothesized to benefit corals that have been photosynthetically compromised on account of prolonged high-temperature exposure [16]).
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 2 Literature Review
Here, micro-and pico/femtocells serve as supporting BSs, consuming less energy due to their lower coverage area. As a result, HetNets can increase frequency reuse, and the different cells create hotspots that reduce the lack of cellular network coverage in dead places, saving energy and expanding the coverage area. Reference [36] describes a load-balancing approach that can help to improve HetNets' ranges. The tradeoff between multiple QoS criteria for individual applications may be optimized using an effective resource scheduling technique. Reference [37] developed a minimum assured resource scheduling technique for NRT users. Reference [38] has provided a queue-size-aware, channel-adapted, and buffer-aware (CABA) packet scheduling algorithm. However, this solution cannot make use of all of the benefits of the popular proportional fair (PF) scheduler. Authors in [39] proposed a radio resource allocation architecture to assure proportionate fairness among the end-users. The impacts of the DRX setting on UE energy savings and delay were investigated by the authors in [40; 41]. As seen in [42], Nokia has performed research on industrial DRX. It is generally understood that a tradeoff is always present between power savings and delay. Numerous DRX techniques are introduced in [43; 44] to optimize the tradeoff.
_Int. J. Mol. Sci._**2015**, _16_, 16497-16513; doi:10.3390/ijms160716497  
International Journal of  
**Molecular Sciences**  
**ISSN 1422-0067**  
www.mdpi.com/journal/ijms  
_Article_  
**Morphogenesis, Flowering, and Gene Expression of**  
_Dendranthema grandiflora_ **in Response to Shift in Light**  
**Quality of Night Interruption**  
**[PERSON] 1, [PERSON] 2 and [PERSON] 1,2,3,***  
* 1 Institute of Agriculture and Life Science, Gyeongsang National University, Jinju 660-701, Korea; E-Mail: [EMAIL_ADDRESS]
* 2 Division of Applied Life Science (BK21 Plus), Graduate School, Gyeongsang National University, Jinju 660-701, Korea; E-Mail: [EMAIL_ADDRESS]
* 3 Research Institute of Life Science, Gyeongsang National University, Jinju 660-701, Korea
* 4 Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-55-772-1913.  
Academic Editor: [PERSON]  
_Received: 3 June 2015 / Accepted: 14 July 2015 / Published: 21 July 2015_  
**Abstract:** The impact of shifts in the spectral quality of light on morphogenesis, flowering, and photoperiod gene expression during exposure to light quality of night interruption (NI) was investigated in _Dendranthema grandiflora_. The circadian rhythms of plants grown in a closed walk-in growth chamber were interrupted at night for a total of 4 h, using light-emitting diodes with an intensity of 10 \(\upmu\)mol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) PPF. The light quality of the NI was shifted from one wavelength to another after the first 2 h. Light treatments consisting of all possible pairings of blue (B), red (R), far-red (Fr), and white (W) light were tested. Plants in the NI treatment groups exposed to Fr light grew larger than plants in other treatment groups. Of plants in NI treatment groups, those in the NI-WB treatment grew the least. In addition, the impact of shifts in the light quality of NI on leaf expansion was greater in treatment groups exposed to a combination of either B and R or R and W light, regardless of their order of supply. Flowering was observed in the NI-RB, NI-FrR, NI-BFr, NI-FrB, NI-WB, NI-FrW, NI-WFr, NI-WR, and SD (short-day) treatments, and was especially promoted in the NI-BFr and NI-FrB treatments. In a combined shift treatment of B and R or B and W light, the NI concluded with B light (NI-RB and NI-WB) treatment induced flowering. The transcriptional factors _phyA_, _cry1_ and _FTL_ (_FLOWERING LOCUS T_) were positively affected, while _phyB_ and _AFT_ were negatively affected. In conclusion, morphogenesis, flowering, and transcriptional factors were all significantly affected either positively or negatively by shifts in the light quality of NI. The light quality of the first 2 h of NI affected neither morphogenesis nor flowering, while the light quality of the last 2 h of NI significantly affected both morphogenesis and flowering.  
**Keywords:** anthesis; blue LED (light-emitting diode); chrysanthemum; flowering; light quality; photomorphogenesis; photoreceptor
# Energy Efficient Determinism in WSN through Reverse Packet Elimination
## 8 Conclusions  
A novel method, Reverse Packet Elimination (RPE), for packet replication is proposed and analyzed in 6 TiSCH. RPE delays the replicated packet from being transmitted and implements a method for cancelling the transmission of the replication packet if the original packet reached its destination. RPE enables WSN with higher reliability, lower average latency and increased node lifetime compared to traditional WSN. Clearly, as with any packet replication method the traffic increases and thus the average energy consumption in the network increases. However, the method of cancelling the replication packet proves effective as it reduces unnecessary transmissions. Related work Leapfrog Collaboration (LFC) finds a 99.1% worst case reliability, with the disadvantage of increasing energy consumption. Our method has similar reliability as LFC, as we got worst-case reliability of 97.7%. Another key point, RPE decreases the funneling effect, thus reducing the energy consumption of nodes closer to the sink and increasing the WSN lifetime. In fact, the lowest node lifetime increased by 1.74% compared to not utilizing any packet replication. However, a drawback of RPE versus LFC is a slight increase in average latency due to the delayed replication packet. This implies that RPE is not optimized for WSN with the most extreme latency requirements. LFC reports a 41% reduction in average latency compared to standard retransmission schemes, while we found that RPE has a 39.1% reduction with equal retransmission attempts. Moreover, LFC is by design dependent on high node density. RPE requires only two disjoint paths to the sink. This makes RPE less dependable on topology and presumably suitable for other WSNs.
## 3 Results and Discussion
### Volatile Compounds
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Volatile Compounds** & **\% of Samples** & **Concentration (\(\mu\)g/g)** & **Identification a** \\ \hline (_E_)-2-Hexenal & 100 & \begin{tabular}{c} 258.9 \(\pm\) 187.4 (5.3–781.1) \\ \end{tabular} & MS/IK.DB \\ \hline (_E_)-2-Tridecenal & 53 & \begin{tabular}{c} 0.3 \(\pm\) 0.2 (0.1–1.0) \\ \end{tabular} & MS/IK.DB \\ \hline 2-Methyl-4-pentenal & 100 & \begin{tabular}{c} 37.7 \(\pm\) 21.7 (2.8–94.4) \\ \end{tabular} & MS/IK.DB \\ \hline Nonanal & 100 & \begin{tabular}{c} 11.5 \(\pm\) 6.9 (1.2–32.8) \\ \end{tabular} & MS/IK.DB \\ \hline Octanal & 5 & \begin{tabular}{c} 2.8 \(\pm\) 0.3 (2.5–3.2) \\ \end{tabular} & MS/IK.DB \\ \hline \multicolumn{4}{c}{**Esters**} \\ \hline (Z)-3-Hexen-1-ol acetate & 100 & \begin{tabular}{c} 45.0 \(\pm\) 78.2 (2.3–582.5) \\ \end{tabular} & MS/IK.DB \\ \hline Acetic acid, hexyl ester & 96 & \begin{tabular}{c} 5.6 \(\pm\) 4.9 (0.6–26.5) \\ \end{tabular} & MS/IK.DB \\ \hline (_E_)-Hex-3-enyl butyrate & 7 & \begin{tabular}{c} 0.3 \(\pm\) 0.1 (0.1–0.4) \\ \end{tabular} & MS/IK.DB \\ \hline Methyl salicate & 60 & \begin{tabular}{c} 0.3 \(\pm\) 0.2 (0.1–0.9) \\ \end{tabular} & MS/IK.DB \\ \hline Prop-2-yn-1-yl 2-methylbutanoate & 91 & \begin{tabular}{c} 3.3 \(\pm\) 1.7 (0.9–9.6) \\ \end{tabular} & MS \\ \hline \multicolumn{4}{c}{**Ethers**} \\ \hline (Z)-1-Methoxy-3-hexene & 91 & \
\begin{tabular}{c} 7.0 \(\pm\) 4.4 (1.4–18.6) \\ \end{tabular} & MS \\ \hline \multicolumn{4}{c}{1-Methoxy-hexane} & 63 &
\begin{tabular}{c} 3.4 \(\pm\) 1.4 (1.5–6.5) \\ \end{tabular} & MS/IK.BD \\ \hline \multicolumn{4}{c}{**Hydrocarbons**} \\ \hline
1,2,4-Metheno-1H-indene, octahydro-1,7a-dimethyl-5- (1-methylethyl)-, & & & \\
70 &
\begin{tabular}{c} 0.8 \(\pm\) 0.7 (0.1–3.3) \\ \end{tabular} & MS/IK.DB \\
3a.beta.4.alpha.5.alpha., 7a.beta.8”) & & & \\ \hline
4,8-Dimethyl-1,7- (1,7-) nonadiene & 30 &
\begin{tabular}{c} 4.1 \(\pm\) 1.6 (1.6–8.7) \\ \end{tabular} & MS \\ \hline
1-Undecene & 5 &
\begin{tabular}{c} 1.3 \(\pm\) 0.4 (0.7–1.7) \\ \end{tabular} & MS/IK.DB \\ \hline
3-Ethyl-1,5-octadiene & 100 & \
\begin{tabular}{c} 16.0 \(\pm\) 14.3 (1.5–56.5) \\ \end{tabular} & MS/IK.DB \\ \hline (_E_)-5-Octadecene & 100 &
\begin{tabular}{c} 11.8 \(\pm\) 10.8 (1.4–43.0) \\ \end{tabular} & MS \\ \hline
1-(1,5-Dimethyl-4- (1,5-Dimethyl-4- (1,5-Dimethyl-4- (0.1–1.0)) \\ \end{tabular} & 25 &
\begin{tabular}{c} 0.4 \(\pm\) 0.3 (0.1–1.0) \\ \end{tabular} & MS/IK.DB \\ \hline
1,3,5-Tris(methylene)- (cycloheptane) & 95 &
\begin{tabular}{c} 0.8 \(\pm\) 0.5 (0.2–2.4) \\ \end{tabular} & MS \\ \hline
1,1-Dimethyl-2-(1-methyl- 2-propenyl)-cyclopropane, & 11 &
\begin{tabular}{c} 2.9 \(\pm\) 1.6 (0.3–5.3) \\ \end{tabular} & MS/IK.DB \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 2 Data Sources and Methods
### Network Architecture of Our Method  
The HSCNN model is divided into five functional groups of components, input (a), inner-CNN (b), edge-CNN (f), vote function (j), and output (k), as shown in Figure 1. Both the edge-CNN and inner-CNN have convolution layers, an encoder layer, and a classifier layer. In the training stage, the inputs are original images and artificial classification labels. In the classification stage, the inputs are original GF-2 images, output is a single-band file, and content of each pixel in the output is the category number of the corresponding original image pixel. The HSCNN indicates the winter wheat area using category number 100, and category number 200 distinguishes other land use. The reason for adopting the two numbers is to fit with the coding value table we are working on to obtain detailed land use information.
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Peptides in Cancer Therapy
#### 3.2.4 Entap  
Entap (6.2 kDa, 58-62 amino acids) is a newly discovered peptide with anticancer activity. Entap is an antiproliferative peptide produced by _Enterococcus_ strains. The antiproliferative activity of Entap on human carcinoma cells has been examined by a study that demonstrated it inhibits the cell cycle at the phase of G1 and induces autophagopsis. The antiproliferative activity of Entap has been examined in vitro on breast adenocarcinoma cell line MDA-MB-231 [43; 56].
## 5 Challenges and Opportunities
### Involving Network and Industry Researchers in Protocol Development  
Our analysis has identified that even newer marine network protocols may choose to incorporate algorithms and approaches that have been known to be vulnerable. For example, OneNet utilizes Anonymous Diffie-Hellman for the connection between Applications and the Pairing Service. Instead, a viable alternative is Elliptic Curve Cryptography (ECC), which is considered to be faster and harder to crack than many traditional cryptosystems including Diffie-Hellman. Another example where advances in research can result in faster implementation is with the use of machine learning (also deep learning) solutions in order to prevent DoS attacks [44]. As a valuable strategy that is also suggested in literature [45], we recommend that manufacturers and organizations involve network researchers that can further evaluate the security hardness of these protocols and play an integral role in their secure development. This can also help bridge the gap between technological advances and industry limitations. One such example is shipping vessels and the the low profit margins that they operate in. The fiducary duties of decision-makers in such organizationstoward cost minimization may influence decisions for enhanced security measures, and, as such, security professionals have to better understand and articulate the associated risks that may come with such decisions.
## 4 Conclusions and Recommendations  
Based on the study of the relationships among the reverse torque, vibration, and input parameters of the mud pumps in the riserless mud recovery drilling, the following conclusions can be drawn:  
1. The error between the theoretical calculation and the actual simulation of the reverse torque of the mud pump is inevitable, and the average relative error obtained from the experiments in this paper is only 3.41%, which is within the permissible range.
2. When the speed of the mud pump is 0 rpm, the reverse torque of the mud to the pump is the lowest, at \(-\)320.4 N\(\cdot\)m, and when the speed of the mud pump is 1500 rpm, the reverse torque of the mud to the pump is the highest, at 3620.28 N\(\cdot\)m. As the speed of the mud pump increases, the reverse torque of the mud to the pump also increases, which is in line with the actual working condition.
3. The amplitude of the mud pump exhibits a pattern of initially increasing and then decreasing with an increase in speed. In the range of 0-1440 rpm, the amplitude increases slowly with the speed. When the motor speed is in the range of 1440-1500 rpm, the amplitude decreases rapidly with an increase in speed. At a speed of 1440 rpm, the amplitude reaches its maximum value of 1.5499 mm. The variation of the rotor amplitude with speed is consistent with the variation of torque with speed in the mechanical characteristics curve of the mud pump.
4. The SVR algorithm is applied to predict the amplitude of the mud pump and the predicted values closely match the actual values, which indicates a high level of reliability in the prediction results. The prediction results provide a reference for the stability of the mud pump.  
Conceptualization, G.H. and B.X.; data curation, H.C. and R.Q.; formal analysis, G.Y. and B.X.; methodology, C.L.; project administration, G.H. and R.Q.; software, G.Y. and B.X.; writing--original draft, C.L. and G.H. [PERSON] and [PERSON] contributed equally to this work and should be considered co-first authors. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2021 YFC2800802; 2021 YFC2800803), the Hi-tech Ship Project of Ministry of Industry and Information Technology (CJ05N20), the Marine Economy Development Foundation of Guangdong Province (GDNRC [2022]44), and Technical Support for the Stimulation and Testing of Gas Hydrate Reservoirs.  
Figure 15: (a) Comparison of prediction results for training set: mse = 0.00082288, R\({}^{2}\) = 0.99588, (b) comparison of prediction results for testing set: mse = 0.00033816, R\({}^{2}\) = 0.99775.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.