esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 One-Dimensional Formulation
### Governing Equations  
The beam energy components are essential for the application of [PERSON]'s principle, including the strain energy, the potential energy, and the kinetic energy. By definition, they are separately given by the following equations.  
\[U=\frac{1}{2}\iiint\limits_{V}\epsilon^{\rm T}\sigma\rmd V=\frac{1}{2}\int \limits_{L}\int\limits_{A}\rmx^{T}\psi^{\rm T}H^{\rm T}c^{\rm T}EcH\psi xd\rmd \rm dz \tag{5}\]  
\[U_{\rm P}=-\int\limits_{L}\int\limits_{A}\rmU^{T}pd\rmd A\rmz-\int\limits_{A} \left[\rmU^{T}\bfp\right]_{z=z_{1}}^{2=z_{2}}\rmd A=-\int\limits_{L}\rmx^{T} \psi^{\rm T}H^{\rm T}pdz-\left[\rmx^{T}\psi^{\rm T}H^{\rm T}\overline{\sigma} \right]_{z=z_{1}}^{2=z_{2}} \tag{6}\]  
\[T=\frac{1}{2}\iiint\limits_{V}\rho\frac{\partial\rmU^{T}}{\partial t}\frac{ \partial\rmU}{\partial t}\rmd V=\frac{1}{2}\int\limits_{L}\int\limits_{A}\frac {\partial\rmx^{T}}{\partial t}\psi^{\rm T}H^{\rm T}\rho H\psi\frac{\partial \rmx}{\partial t}\rmd A\rm dz \tag{7}\]  
where \(V\), \(A\) and \(L\) are the volume, the cross-section area and the length of the structure, respectively; \(\rho\) is material density; \(\bfp\) denotes the loading vector consisting of distributed loads in the axial, tangential, and normal directions; coordinates \(z_{1}\) and \(z_{2}\) (\(z_{1}<z_{2}\)) represent the axial coordinates of the two ends of the thin-walled structure and \(\overline{\sigma}\) indicates the strain vector imposed at the ends.  
The formulation of the dynamic governing equation involves the application of Hamilton principle:  
\[\delta\int\limits_{t_{1}}^{t_{2}}L_{\rm a}\rmd t=0 \tag{8}\]  
where \(L_{\rm a}\) is the Lagrangian defined as \(L_{\rm a}=T-U-U_{\rm P}\) in this paper, and \(t_{1}\) and \(t_{2}\) are the start and end times, respectively.  
Substituting Equations (5)-(7) into Equation (8) and applying the integration by parts, by imposing the condition  
\[\delta\rmx\left|{}_{t=t_{1}}=\delta\rmx\left|{}_{t=t_{2}}\right.=0 \tag{9}\]  
yields  
\[\begin{array}{rl}\delta H&=-\int\limits_{t_{1}}^{t_{2}}\left[\int\limits_{L }\int\limits_{A}\delta\rmx^{T}\psi^{\rm T}H^{\rm T}\eta H\psi\frac{\partial^{2 }\rmx}{\partial t^{2}}\rmd A\rmdz\right]\rmd t-\int\limits_{t_{1}}^{t_{2}} \left[\int\limits_{L}\int\limits_{A}\delta\rmx^{T}\psi^{\rm T}H^{\rm T}c^{\rm T }EcH\psi xd\rmd\rm dz\right]\rmd t\\ &+\int\limits_{t_{1}}^{t_{2}}\left[\int\limits_{L}\int\limits_{A}\delta\rmx^{ T}\psi^{\rm T}H^{\rm T}pd\rmd A\rmz+\int\limits_{A}\left[\delta\rmx^{T}\psi^{\rm T }H^{\rm T}\overline{\sigma}\right]_{z=z_{1}}^{2=z_{2}}\rmd A\right]\rmd t\\ &=0\end{array} \tag{10}\]  
The dynamic governing equation of the thin-walled structure can then be derived as  
\[\begin{array}{rl}\int\limits_{L}\int\limits_{A}\delta\rmx^{T}\psi^{\rm T}H^ {\rm T}\eta H\psi\frac{\partial^{2}\rmx}{\partial t^{2}}\rmd A\rmdz+\int\limits _{L}\int\limits_{A}\delta\rmx^{T}\psi^{\rm T}H^{\rm T}c^{\rm T}EcH\psi xd\rmd \rm dz\\ &-\int\limits_{L}\int\limits_{A}\delta\rmx^{T}\psi^{\rm T}H^{\rm T}pd\rmd A\rm dz -\int\limits_{A}\left[\delta\rmx^{T}\psi^{\rm T}H^{\rm T}\overline{\sigma} \right]_{z=z_{1}}^{2=z_{2}}\rmd A=0\end{array} \tag{11}\]
## 2 Literature Review  
[PERSON] et al. [5] presented a novel routing scheme for underwater communication. Further work was reported by [PERSON] et al. focusing on energy consumption [6]. [PERSON] et al. [7] suggested ways of improving the quality-of-service optimization approaches in underwater communication systems for marine networks. In a study by [PERSON] et al. [11], the sensor nodes were implemented to achieve this task. In the study by [PERSON] et al. [20], the authors focused on privacy and security in underwater communication systems. [PERSON] [29] proposed work focusing purely on sensor node communication, and used NS3 to perform simulations. The results revealed that their proposed scheme was able to cope with the conditions of the environment. [PERSON] et al. [37] introduced an energy-efficient routing protocol for underwater marine network improvements. They used PDR, EED, PL, Jitter, Throughput, Alive and Dead Nodes, and Frequency of Probability as the core evaluation parameters [38; 39; 40; 41].  
Similarly, [PERSON] et al. [42] proposed a novel nature-inspired routing protocol that takes the idea of ants and acts like them in order to deliver data from one source to another. This method focuses on beam technology, which has improved over time. The authors suggested that sensors can acquire a lot from nature by using an AI enabling approach, and can adapt accordingly. [PERSON] et al. [43] proposed a UWSN communication-based scheme targeting radio propagation and acoustic channels. The authors presented the idea of ocean-based communication incorporating a next-generation 5G network. This would allow satellites and other terrestrial networks to communicate.  
[PERSON] et al. [44] implemented a secure and reliable communication scheme for marine communication. Their proposed method focused on link stability and link quality. The simulations revealed that improving the link quality and stability could ultimately improve the network by avoiding the occurrence of path loss during transmission. [PERSON] et al. [45] and [PERSON] et al. [46] improved network performance by focusing on underwater acoustic channel communication. [PERSON] et al. [47] targeted the acoustic and channel utilization in their proposed method. Neighboring-based approach have been applied, whereby the nearest nodes take part in underwater communication ([PERSON] et al. [50], [PERSON] et al. [51], [PERSON] et al. [52], [PERSON] et al. [53].  
Different approaches have been introduced to enhance and improve the service quality, and achieve energy optimization, void hole avoidance, underwater localization, link stability, etc. In these cases, a number of authors have presented works aiming to solve or avoid the existing problems. Based on the previous works, the proposed method focuses on cooperation with relay optimization, with a further focus on energy optimization. Cooperation, relay optimization, energy optimization, energy consumption, neighbor node identification, and head node selection are the core research gaps addressed in the proposed method. Table 1 provides a tabular summary of the previous literature.
## 3 Analysis and Design of the Force Control System
### Estimation of the Maximum Velocity at Which the Task Is Executed  
Assume \(e_{max}\) is the maximum tolerable force error in the force control system in Figure 3. Then, all the errors \(e_{ss}<e_{max}\) are acceptable.  
On the other hand, when one selects the maximum gain \(K_{GMmax}K_{p}\) from Figure 6, this gain is the critical gain since it corresponds to the case \(|L(j\omega)|_{test}=1\). In practice, one should avoid having a critical gain, since the system may have an oscillatory response, and a small disturbance may cause instability. Therefore, the selection of the controller gain should be \(K_{p}<K_{GMmax}K_{p}\).  
Considering the maximum error \(e_{max}\) and the recommended selection of the gain \(K_{p}<K_{GMmax}K_{p}\), it is possible to obtain a more selective set of gains \(K_{p}\) from Figure 6. In Figure 7, certain values for \(e_{max}\) and \(K_{p}<K_{GMmax}K_{p}\) are presented. One can observe a selective set of gains \(K_{p}\) defined by the limits \(e_{max}\) and \(K_{p}<K_{GMmax}K_{p}\). The mentioned region contains the set of gains that ensure an acceptable force error and a safer response, since \(K_{p}\) is far from the critical gain. Additionally, there are curves corresponding to different values of \(M\), and a maximum value \(M_{max}\) can be obtained from the region. In Figure 7, \(M_{max}=1.5M\). Since \(M\) is proportional to the velocity at which the task is executed (see Section 3.1), the maximum velocity can be estimated from \(M_{max}\).  
Figure 6: Design method: steady-state error \(e_{ss}\) in terms of control gain \(K_{p}\) and ramp value \(M\)\(K_{p}\) considering gain margin.
## 3 Proposed FDTE with Iterative Channel Estimation
### A Posteriori Soft Decision for Iterative Channel Estimation  
After the first iteration, the symbol of the a posteriori soft decision \(\tilde{x}_{k}\) of equalized symbol \(x_{k}^{e}\) can be obtained, similar to those in [9; 14]:  
\[\tilde{x}_{k}=\sum_{a_{q}\in\mathcal{A}}\alpha_{q}P\big{(}x_{k}=\alpha_{q}\mid x _{k}^{e}\big{)}, \tag{34}\]  
where the a posteriori probability \(P\big{(}x_{k}=\alpha_{q}\mid\tilde{x}_{k}^{e}\big{)}\) is given by  
\[P\big{(}x_{k}=\alpha_{q}\mid x_{k}^{e}\big{)}=\frac{P\big{(}x_{k}^{e}\mid x_{k}= \alpha_{q}\big{)}P\big{(}x_{k}=\alpha_{q}\big{)}}{P\big{(}x_{k}^{e}\big{)}}. \tag{35}\]  
In (32), \(P(x_{k})\) is calculated in (7), and \(P(x_{k}^{e})\) is the normalization factor. The marginal \(P(x_{k}^{e}|x_{k}=\alpha_{q})\) is calculated as \[P\big{(}x_{k}^{e}|x_{k}=\alpha_{q}\big{)}\propto\exp(-|x_{k}^{e}-\alpha_{q}|^{2}/v^ {e}). \tag{36}\]  
With the continuous iteration of the turbo equalization, the reliability of the a posteriori soft feedback keeps increasing to accelerate the convergence of the estimation.  
Thus, the iterative channel estimation in (9) after the first iteration can be rewritten as  
\[\mathbf{h}_{m}(k+1)=\left\{\begin{array}{l}\left(1-\frac{\gamma}{|d_{m}(k)|} \right)\frac{\tilde{d}_{m}^{2}(k)\mathbf{\Theta}_{m}^{D}\mathbf{x}(k)}{\mathbf{ k}(k)^{D}\mathbf{\Theta}_{m}^{D}\mathbf{x}(k)+\delta}+\\ \mathbf{h}_{m}(k)-Q_{m,k}\tilde{\mathbf{h}}_{m}(k),\big{|}d(k)\Big{|}>\gamma\\ \mathbf{h}_{m}(k),\quad\text{otherwise}.\end{array}\right., \tag{37}\]  
where \(\tilde{d}_{m}(k)=y_{m}(k)-\tilde{\mathbf{h}}_{m}^{T}(k)\tilde{\mathbf{x}}(k)\) with \(\tilde{\mathbf{x}}_{\mathbf{k}}=[\tilde{x}_{1},\tilde{x}_{2},\ldots,\tilde{x }_{K}]\).
# Polymers and Polymer Nanocomposites for Cancer Therapy +
## 3 Biopolymers and Biopolymer Nanocomposites
The most recent progress in the compatibility of the marine biopolymer nanocomposites in vitro and in vivo was discussed, along with their future treatment of cancer, in Reference [65]. A nano-biodegradable polymer blend made of starch and cellulose acetate-co-acrylate was loaded with DOX. The antiproliferative effect of the drug slowly released from the nano-biodegradable polymer illustrated growth inhibition potency toward the human colon and liver cancer cell lines LoVo and Hepg2 [66]. Research has investigated the controlled delivery of Campothecin and DOX through encapsulation in self-assembled hyaluronic acid (HA)/testosterone conjugate nanoparticles. Based on the results of in vitro studies, the researchers concluded that the HA/testosterone nanocomposite is a promising carrier candidate in the chemotherapy of cancer [67]. In order to study multidrug resistance, environment-sensitive polylactide-based nanoparticles (100-150 nm) with low toxicities were assembled through multiple hydrogen bonds. Most importantly, the finding of this research was that drug resistance in the case of cancer cells could be overcome through the use of tailor-made nanoparticles, which were biodegradable and pH-responsive. These features indicated that supramolecular nanocarriers can effectively increase the efficiency of chemotherapy against the drug resistance of cancer tumors [68]. Polyllysine-coated Tamoxifen-loaded poly(lactic-co-glycolic acid) nanoparticles were fully characterized in terms of their in vitro performance against breast adenocarcinoma cells. The successful incorporation of the drug within the hydrophobic matrix of nanoparticles was evidenced by a high loading efficiency (86%) [69]. Besides their main characteristics of biocompatibility and biodegradability, biopolymers are considered to be an important alternative to synthetic polymers from an environmental point of view due to the fact that in their production, energy consumption and gas emissions are reduced. Table 1 below presents examples of carrier-drug systems used in some studies.
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA)  
The thermogravimetric curves of the VNR/MWCNT nanocomposite specimens are shown in Figure 8. Figure 8 illustrates the loss of mass for each specimen as a function of temperature. Consequently, it is possible to identify the temperature range in which mass loss events occur. In the first event, moisture is lost from the VNR/MWCNT nanocomposites and pure VNR at 100 \({}^{\circ}\)C, representing approximately 1% of the mass loss. At approximately 180 \({}^{\circ}\)C, the second event occurs for all specimens and is related to volatile compounds with low thermal stability, such as fatty acids and non-rubber components [69]. It can be observed that all the VNR/MWCNT specimens and neat VNR present the same thermal behavior, i.e., only one main thermal event of mass loss. There is a third event, of greater intensity, that occurs between 350 and 400 \({}^{\circ}\)C, corresponding to 75-78% of the mass loss, which is due to the degradation of the isoprene chain in natural rubber [70]. A shoulder on the DTG curve can be observed as the last event at 500 \({}^{\circ}\)C (Figure 9), which represents 14-16% of the mass loss. This is a result of less accessible regions of the VNR that were not decomposed during the main event, which required more thermal energy to decompose [71]. A residual mass of less than 6% is composed of inorganic materials, including zinc used in the curing process.
## 3 Performance Evaluation Results and Discussion
### Effects of the Node Density
This is due to the protocol relying solely on node location information during selection. Therefore, as the network continues to operate without other intervention, there may be one or several nodes frequently serving as forwarding nodes, which will accelerate node depletion. When selecting nodes in the FLRAF protocol, the output layer fuzzy reasoning system is considered to evaluate the priority of candidate nodes. When the node energy is low, the fuzzy logic output value is small, indicating that the node is no longer suitable for serving as a forwarding node. Therefore, this method indirectly achieves the function of nodes taking turns to serve as forwarding nodes, which significantly helps to prolong the network lifetime. Figure 19: Effects of node density on network lifetime. Figure 18: Effects of node density on average energy consumption.
# Multi-Objective Parameter Optimization of Flexible Support System of Optical Mirror
## 1 Introduction
proposed a multi-point flexible unit and a four-point length adjustable radial lens support structure to support and position a lens assembly with large aperture and high precision in an on-orbit assembly space telescope validation prototype [21]. [PERSON] et al. designed a new type of three-leaf flexible structure based on the spring principle and optimized the size parameters of the flexible structure through the finite element analysis method [22]. [PERSON] et al. used floating support for a 1.2 m thin mirror support and proposed a new floating support force distribution algorithm [23]. To achieve an accurate surface form and optical axis stability of a large aperture lens in a ground-based telescope system subject to various load cases, [PERSON] et al., proposed a novel lens support with a multi-point flexible support structure and based on low-order modes and system accuracy to optimize its supporting structure [24]. [PERSON] et al. designed a tangential flexible support structure and an axial flexible support structure to compensate for temperature and gravity load disturbances on a large-aperture optical mirror [25]. The above-mentioned scholars have carried out a substantial amount of research on the support systems of optical mirrors. They used the support systems for the installation, positioning, and detection of large-diameter or thin mirrors, by improving traditional mechanisms, designing new support systems, and optimizing mechanism parameters of the support systems. However, few scholars use the support system in the optical mirror processing stage. This is particularly so with regard to the application of the FSS in the processing of large-scale optical mirrors, which is deficient in terms of relevant theoretical foundation. Inengineering applications, a few scholars have successfully applied FSS to the processing of thin-walled parts, such as aircraft skins [26; 27]. Therefore, it is necessary to further study the application of the FSS in the processing stage of large-aperture optical mirrors. An FSS with adjustable rigidity is designed to enable the adjustment of the rigidity of the bottom support system of a large-scale optical mirror during processing and to effectively unload the impact force generated by the grinding disc. In addition, the main components of the FSS are optimized for multi-objective parameters using the finite element method, in order to reduce the internal resistance of the FSS and improve the consistency of force output. In Chapter 2, the motion resistance modeling of FSS and support cylinder are carried out, and the target parameters to be optimized are determined. In Chapter 3, through the multi-objective optimization analysis, the optimal target parameters of beryllium bronze Feed and rolling diaphragm are determined. In Chapter 4, the performance of the optimized support system is verified by experiments. In Chapter 5, the conclusion of this paper is drawn.
## 4 Results and Discussion
### Results Analysis
#### 4.2.3 Dataset 2  
In Dataset 2 (Figure 11), all the stall points at 23:19, 23:29, and 00:15 were successfully detected.  
Figure 11: Plot for Dataset 2 datapoints and FIS outputs.  
At around 00:26, there was an increase in pressure which did not result in a positive output for PDM Status and shows the importance of subtracting \(\sigma_{WHP}\) when calculating CP_deviation (\(\sigma_{CP}\)) as in Formula (6). Without this compensation, the system would not account for the increase in CP_deviation because, since it is a result of an increase in the _WHP_, it could wrongly activate one of the outputs and generate a false output.  
The FIS detected 4 out of 4 stalls present in this data.
## 2 Time Series Modeling
### ARMA Model in Structural Identification  
Past studies revealed the coherent relationships between the ARMA parameters and structural properties in system identification [23]. Based on these efforts, a mapping between the physical parameters of a multi-degree-of-freedom (MDOF) system and the ARIMA parameters is derived herein. Based on the dynamics of structures, an \(n\) MDOF system excited by a ground motion is represented by a second-order differential equation, Equation (3), where \(\mathbf{u}\) is the relative displacement vector with the superposed dot indicating the time derivative, and \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) are the \(n\times n\) mass, damping, and stiffness matrices, respectively. If the input ground motion acceleration, \(\tilde{u}_{g}(t)\) assumed to be white noise, i.e., \(w(t)=\tilde{u}_{g}(t)\), with \(\mathbf{1}\) as an \(n\times 1\) unit vector, the right-hand side can be rewritten as the product of an \(n\times 1\) spatial distribution vector \(\mathbf{\Gamma}=-\mathbf{M}\mathbf{1}\) as follows,  
\[\mathbf{M}\tilde{\mathbf{u}}+\mathbf{C}\tilde{\mathbf{u}}+\mathbf{K}\mathbf{u }=-\mathbf{M}\mathbf{1}\tilde{u}_{g}(t)=\mathbf{\Gamma}w(t). \tag{3}\]  
Suppose \(y(t)\) represents the target single degree of freedom (DOF) response, e.g., a horizontal acceleration response of the \(i\)-th floor, which is a sequence of a scalar quantity. Subsequently, we convert Equation (3) to a state-space representation considering measurement noise as follows,  
\[ \begin{split}\dot{\mathbf{x}}(t)&=\mathbf{Ax}(t)+ \mathbf{B}w(t),\\ y(t)&=\mathbf{D}\mathbf{x}(t)+v(t),\end{split}  \tag{4}\]  
where \(\mathbf{x}= \begin{bmatrix}\mathbf{u}\\ \dot{\mathbf{u}}\end{bmatrix} _{2n\times 1}\) is the system state vector, \(\mathbf{A}= \begin{bmatrix}\mathbf{0}\\ -\mathbf{M}^{-1}\mathbf{K}&-\mathbf{M}^{-1}\mathbf{C}\end{bmatrix} _{2n\times 2n}\) is the state matrix, \([\mathbf{0}]\) is the zero matrix, \([\mathbf{I}]\) is the identity matrix, \(\mathbf{B}= \begin{bmatrix}\mathbf{0}\\ \mathbf{M}^{-1}\mathbf{\Gamma}\end{bmatrix} _{2n\times 1}\) is the input matrix, \(\mathbf{D}\) is a \(1\times 2n\) output vector, and \(v(t)\) is a scalar measurement noise. Based on [23], the general solution of \(\mathbf{x}\) for uniformly sampled data with the time interval \(\Delta_{t}\) is expressed in Equation (5), where \(t\) in discrete time represents the time index for the sampling of discrete quantities at \(\Delta_{t},2\Delta_{t},\ldots,k\Delta_{t}\). In the following text, \(\mathbf{x}(k\Delta_{t})\) is denoted as \(\mathbf{x}(k)\) for short.
## 5 Conclusions  
In this paper, the GPP of a near-natural peatland was successfully modelled using Sentinel 2-derived vegetation indices and a LUE model. Six hybrid models based on multiple regression were developed and tested for various literature-based LUE parameter values specifically for raised bogs; modelled GPP using hybrid Models 2 and 4 correlated well (0.89 to 0.96) with the EC-derived GPP. This study also reports site-specific LUE parameter values based on light response curves for an extremely dry year and normal weather under normal climatological conditions years, which could be useful in the future to help upscale estimates of GPP in other studies on near-natural peatlands. These results provide useful insights on the use of high-resolution-derived vegetation indices and their potential to be used across various scales and peatland types. Additionally, this approach can be further refined using high-resolution imagery, such as planet scope, to estimate carbon fluxes from remote locations. This information will also be useful as a reference for sensor selections and future sensor designs for ecosystem studies.  
**Author Contributions:** [PERSON] conceived the ideas, designed the methodology, collected and analyzed the data, and wrote the manuscript; [PERSON] contributed with Sentinel 2A imagery analysis; [PERSON] provided the water table data; [PERSON] supervised the research and contributed to the development of the manuscript; [PERSON] reviewed and helped with development of the manuscript; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] assisted in reviewing the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Trinity College Dublin, Provost's Ph.D. Scholarship awards and the Environmental Protection Agency of Ireland 2014-2020 programme of research (project ref: 2018-CCRP-LS.2).  
**Acknowledgments:** The authors wish to thank the Environmental Protection Agency (EPA) for their financial support under the SmartBog project (2019-2023).  
**Conflicts of Interest:** The authors would like to report no existing or potential conflict of interest.