esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to a Case Study
### Analysis of the Factors Affecting the Health of an Offshore Platform Structure  
The natural frequency is a property of the structure that does not generally change with changes in external load. However, factors such as erosion and corrosion will cause the mass matrix and stiffness matrix to change, and this indirectly changes the natural  
Figure 10: First order strain difference diagram of 50% bar damage.  
frequency of the structure. This section discusses the impacts of scouring, corrosion, marine organisms, and temperature.  
Based on the finite element model established in Section 3.2, the effects of four actions on the model in six different years were considered. These four actions were scouring, corrosion, marine biological growth, and temperature, and the six time periods were 0, 5, 10, 15, 20, and 25 years. This is a total of 24 working conditions. For convenience of introduction, the three actions of scouring, corrosion, and marine biological growth are collectively referred to as external factors, and the effect of temperature is referred to as a climate factor. We extracted the natural frequencies of the model under the 24 working conditions to draw the diagram, and the time-varying effect on the frequency is shown in Figures 11-14.  
Figure 11: The diagram of time-varying effect on frequency under scouring. (**a**) Frequency change rate under scouring. (**b**) Frequency variation under scouring.  
Figure 12: The diagram of time-varying effect on frequency under corrosion. (**a**) Frequency change rate under corrosion. (**b**) Frequency variation under corrosion.
## 2 Materials and Methods  
### Materials  
Commercially available Type I ordinary Portland cement (OPC) and silica fume (SF) were used as binders. Their chemical compositions were given in Table 1. The chopped CFs with lengths of 4, 6, and 10 mm (L04, L06, and L10) were provided by Ace C & Tech Co., Ltd. (Yeongju-si, South Korea). As shown in Figure 1, their lengths were verified by an image analysis; once the scanned image of more than 40 CF bundles was converted into a binary image, the length of each bundle was measured by a mean of image analysis. In this study, ImageJ was employed as a tool of image analysis. This program is a Java-based freeware for an image processing. It can analyze images with from binary to 32-bit images. The counts depending on the lengths of CF bundles was expressed as a normal distribution as shown in Figure 1.  
The diameter and density of the samples were 15 \(\upmu\)m and 1.8 g/cm\({}^{3}\). A sizing agent was used to reduce the number of CF filaments in the chopped bundle to less than 1.0% because a large number of filaments in a CF bundle hinders dispersion.  
To prepare the cement composite samples, the water-to-binder ratio was set at 0.35. Among the total OPC weight of 1498 kg/m\({}^{3}\), 30% was replaced with SF. CFs were incorporated into cement composites at different volume contents in the range of 0.1% to 2.0% (CF0.1, CF0.5, CF1.0, CF1.5, and CF2.0). The flow of the fresh cement composites was controlled within the range of 150-200 mm using a polycarboxylic acid-base superplasticizer.  
The fresh CF-incorporated cement composites were cast into cubic molds with dimensions of \(50\times 50\times 50\) mm\({}^{3}\), and subsequently, compacted using a shaking table. The hardened cement composites were demided after two days of casting. Thereafter, they were cured at room temperature (temperature: \(20\pm 2\) \({}^{\circ}\)C and relative humidity: \(65\pm 5\)%).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Binders** & **CaO** & **SiO2** & **Al2O3** & **Fe2O3** & **MgO** & **Na2O** & **K2O** & **SO3** \\ \hline OPC & 65.30 & 18.80 & 4.18 & 3.72 & 2.43 & 0.15 & 1.10 & 3.28 \\ SF & 0.67 & 92.60 & 0.07 & 0.49 & 1.80 & 0.02 & 1.13 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of ordinary Portland cement (OPC) and silica fume (SF).  
Figure 1: Carbon fiber (CF) bundles with different lengths and their length verifications; (**a**) 4 mm CF (L04), (**b**) 6 mm CF (L06), (**c**) 10 mm CF (L10), and (**d**) probability density of the different CF lengths obtained by image analysis.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 8 Conclusions  
Clustering and sink mobility technology as effective methods to improve the performance of WSNs have drawn much research attention recently. In this paper, we first divided the whole sensor field into several sectors with the same size, and each sector selected a CH according to its members' weight. Cluster members calculated the routing path with the optimal energy consumption to transmit data to their corresponding CHs via single or multi hops communication. Then, the CHs adopted a greedy algorithm to form a chain for intercluster communication. The latest routing schemas, such as CCMAR and ECDRA, were compared with our presented algorithm regarding aspects of energy consumption and network lifetime by simulation. The simulation results proved our presented algorithm outperformed the other two algorithms. Additionally, we explored the influence of different parameters on the performance of the network and further improved its performance.  
[PERSON] and [PERSON]-[PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data and helped to revise this paper, and [PERSON] wrote this paper. This work was supported by the National Natural Science Foundation of China (61772454, 61811530332, 61811540410, U1836208). Professor [PERSON] is the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
The results of this study demonstrated that none of the four growth promoters tested had any negative effects on the development of the tested bacterial strains. However, CSN significantly increased the efficiency of the tested bacteria's denitrification process, increas  
Figure 9: GO enrichment analysis classification statistics chart (The color indicates the significance of the enrichment, and the redder the default color indicates the more significant enrichment of the GO term, where FDR & LT; 0.001 is marked with ***, and FDR & Lt) & LT.  
ing the removal rate at 72 h by 13.08%. The clearance rate of strain Q1 was increased by 25.88% at 72 h by the addition of 15 mg/L CSN. The transcriptome of Nitrobacter revealed 1664 DEGs (573 upregulated genes and 1091 downregulated genes) in the strain Q1 treated with CSN. Nitrate reductase and nitrate transporter, two important enzymes related to the nitrate reduction pathway, displayed upregulated expression during the denitrification process. In addition, it was shown that several proteins' translation and synthesis processes--including those of glutamyl-tRNA(Gln) aminotransferase, tRNA lysine synthase, and tRNA-specific adenosine deaminase--were upregulated. The aforementioned findings imply that strain Q1 increased the strain's denitrification efficiency under CSN treatment by, on the one hand, increasing the expression of genes involved in the nitrate reduction pathway. On the other side, it might boost protein synthesis and translation to enhance the production of enzymes, improving the strain's capacity for denitrification. The addition of CSN significantly improved the treatment efficiency of COD of the effluent when compared with the control, with the highest number of genes annotated in significant pathways such as amino acid metabolism, carbohydrate metabolism, and other classifications. GO genes were primarily enriched in cytoplasmic large ribosomal subunit, ribosome, rRNA binding, and other pathways. Our research establishes a foundation for using strain Q1 and the growth stimulant CSN to treat high-salt, nitrogen-containing wastewater.  
Conceptualization, methodology, and writing--original draft preparation, N.Y.; data curation and resources, K.W. and F.T.; visualization and investigation, L.Z.; writing--reviewing and editing, Q.L., J.L. and M.L.; funding acquisition and project administration, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFC2805100), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the Nitrobacteria research project (KH21020).  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 2 Proposed Framework
### Note Recognition
Additionally, the inter-harmonic spacings are constant for different pitches so that the CNN can learn pitch-invariant information. We trained a CNN of one output unit as an onset detector, giving binary labels to distinguish onsets from non-onsets. The architecture of this CNN is shown in Figure 3. The CNN takes a spectrogram slice of several frames as a single input, and each spectrogram excerpt centers on the frame to be detected. All of the spectrograms are extracted along the music signal, with a hop size of one frame. Feeding the spectrograms of the test signal to the network, we can obtain an onset activation function over time. The frame whose activation function is greater than the threshold is set to be the candidate onset. The onset detector is followed by another CNN for multi-pitch estimation (MPE), which has the same architecture except for the output layer. Its input is a spectrogram slice centered at the onset frame. The CNN has 88 units in the output layer, corresponding the 88 pitches of piano. To make sure the multiple pitches can be estimated at the same time, all the outputs are transformed by a sigmoid function. For each training sample, the onset time is annotated accurately in advance. In the testing procedure, the input is a spectrogram slice centered at the candidate onset, which is detected by the previous CNN. A set of probabilities of 88 pitches is estimated through the network. Finally, the candidate pitches at candidate onsets are obtained by applying a threshold to the output.
## 3 Realization of \"Touch-and-Away\" Behavior
### Moving Away from the Player and Emergency Stop  
When the robot enters area \(D\), the robot decides that it can touch the player and switch to the \"moving away\" state.  
We examined two strategies for moving away from the player. One strategy is \"onward.\" Here, the robot moves straight for two seconds at velocity \(V_{0}\) and then stops. During the motion, the robot does not consider the position of the player.  
However, after the robot touches a player, the player becomes \"it\" and starts to chase the robot; the robot needs to rapidly move away from the player. Thus, we designed another strategy, called \"parallel,\" in which the robot moves towards the other side of the player.  
There is an issue with designing this behavior. The robot cannot suddenly change its moving direction because the robot is driven by two wheels. Thus, we designed the behavior of moving away from the player, as shown in Figure 8.  
We consider two points, \(\mathbf{H}_{L}\) and \(\mathbf{H}_{R}\), at the \(r_{\text{touch}}\) left and right side of the robot. Then, we observe the angles from the two points to the player, \(\theta_{L}\) and \(\theta_{R}\), as follows:  
\[\theta_{L}=\tan^{-1}\biggl{(}\frac{x_{P}}{r_{\text{touch}}-y_{P}}\biggr{)} \tag{12}\]  
\[\theta_{R}=\tan^{-1}\biggl{(}\frac{x_{P}}{r_{\text{touch}}+y_{P}}\biggr{)} \tag{13}\]  
Then, we calculated the robot's direction of movement. When \(\max(\theta_{L},\theta_{R})>90^{\circ}\), it means that the player is on the left or right side of the robot; thus, the robot just moves straight. The same applies when the angles \(\theta_{L}\) and \(\theta_{R}\) are negative. Otherwise, the robot moves to the other side of the player (angle \(\theta_{W}\)) as follows:  
\[\theta_{W}=\left\{\begin{array}{ll}\theta_{L}-90^{\circ}&\text{if }\theta_{L}> \theta_{R}\\ 90^{\circ}-\theta_{R}&\text{if }\theta_{L}<\theta_{R}\end{array}\right. \tag{14}\]  
Then, the angle (in degrees) is directly used as the angular velocity (degree/s) of the robot:  
\[\omega=\theta_{W} \tag{15}\]  
The translational velocity is set to \(\min(V_{0},0.8d_{P})\) (m/s).  
In addition, if the player enters area E, the robot stops immediately for safety.  
Figure 8: Moving away from the player.
# A Novel Decomposed Optical Architecture for Satellite Terrestrial Network Edge Computing
## 5 Scalability and Discussion  
The decomposed architecture prototype has been experimentally validated in Section 4, consisting of four hardware cubes. To better investigate the scalability of decomposed optical computing architecture, the physical and network performance of decomposed architecture are evaluated in this section under different network scales. For the physical performance evaluation, the output OSNR and power penalty for error-free operation are experimentally assessed as a function of the NOS port count. Meanwhile, exploiting the experimentally measured parameters, the network latency of decomposed computing architecture is numerically investigated under different network scales and bandwidths.
# Atomic Spectroscopy-Based Analysis of Heavy Metals in Seaweed Species
## 2 Atomic Spectroscopy-Based Heavy Metal Analysis Methods for Seaweed
### Atomic Absorption Spectrometry (AAS) Techniques
#### 2.2.3 Solid Sampling Zeeman Atomic Absorption Spectrometry (SS-ZAAS)  
As mentioned in the sections above, wet digestion with strong acids as the pretreatment and sample decomposition stage is one of the most crucial steps in conventional AAS analytical procedures. As an alternative to methods using wet digestion, direct solid sampling Zeeman atomic absorption spectrophotometry (SS-ZAAS) serves as a very fast and precise method where chemical sample pre-treatment is not needed [67]. Minimum chances of contamination and very low sample consumption are among the advantages of this technique. Since a chemical pretreatment step is not included and the sample is used directly, in general, solid sampling techniques achieve lower limits of detection than wet digestion methods.  
[PERSON] et al. developed an SS-ZAAS method to determine Hg, Cd, Pb, and Mn levels in seaweed [68]. The proposed method was validated by analyzing certified reference materials (CRMs) with similar matrices. No chemical modifiers were used, and no matrix effects were observed under the optimized conditions. For Hg determination, mussel tissue BCR CRM-278, cod mussel BCR CRM-422, and human hair BCR CRM-397 were used for calibration, and a 0.008 mg/g limit of detection was achieved. The 228.8 nm and 326.1 nm resonance lines were used in two-field and three-field modes, respectively, with rice flour NIST SRM-1568 and mussel tissue BCR CRM-278 references and achieved 0.0013 mg/g detection limits for Cd content determination. Similarly, Pb concentration was measured at two different wavelengths: 283.3 nm (two-field mode, bovine muscle BCR CRM-184 and bovine liver BCR CRM-185 as references) and 261.4 nm (two-field mode, pepperbush NIES-1 and Olea europaea BCR CRM-62 as references). The limit of detection was estimated to be 0.008 mg/g Pb. Due to the high Mn contents in all of the investigated samples, a less sensitive resonance line of 403.1 nm and the dynamic measurement mode were applied. A calibration with mussel tissue BCR CRM-278R, plankton BCR CRM-414, and tea leaves NIES-7 were used to create the calibration curves. Overall, the study showed a good agreement between the experimental and certified values. From the collected data, it was concluded that by avoiding sample dilution and chemical modifiers, direct SS-ZAAS is a reliable, rapid, and low-cost technique for the determination of trace elements in aquatic plants.
# Downlink Training Design for FDD Massive MIMO Systems in the Presence of Colored Noise
## 5 Asymptotic Analysis of the SINR with RZFBF Precoding  
This section provides an asymptotic analysis expression that tightly approximates the SINR, and hence DL achievable sum rate, of RZFBF precoding for an FDD MaMi system based on the random matrix theory method [10; 39; 50]. To this end, an asymptotically approximation of the SINR, denoted by \(\overline{\gamma}_{\rm h}\), can be obtained as given in (34) when the number of BS antennas \(N\) and the number of users \(K\) grow asymptotically as  
\[\gamma_{\rm h}-\overline{\gamma}_{\rm h}\xrightarrow[N\to\infty]{}0. \tag{34}\]Although the asymptotic analysis in this paper is derived under an assumption that \(N\to\infty\), consistent with conventional research simulations of asymptotic system analysis [10; 39; 51; 52; 53], numerical results show that this asymptotic analysis provides an accurate approximation for the true SINR and the achievable sum rate, of the RZFBF precoder, even with the finite parameter of \(N\). This would allow the SINR \(\gamma_{\rm h}\) term in (6) with extensive Monte Carlo simulations to be replaced with the asymptotic approximation given in this section, and thus, straightforward system design evaluation can be achieved. Furthermore, the asymptotic approximation of the SINR of the RZFBF precoder, and hence the sum rate, allows the numerical results to be readily reproducible. The following proposition elaborates the analytical result of the RZFBF precoder using a random matrix theory. **Proposition 2**. : _Let \(\gamma^{\rm RZFBF}\) denote the \(\rm SINR_{\rm h}\) of RZFBF precoding, as defined in (6). An asymptotic SINR approximation with RZFBF precoding of an FDD MaMi system under consideration is provided as_  
\[\overline{\gamma}_{\rm h}^{\rm RZFBF}=\frac{KN\xi\delta_{\rm h}^{2}}{\sigma_{ \rm d}\big{(}1+\delta_{\rm h}\big{)}^{2}+\bar{\xi}K^{2}\hat{\mu}_{\rm h}}, \tag{35}\]  
_where the parameter \(\xi\in\mathbb{R}\) is the deterministic equivalent of the RZFBF precoding and \(\delta_{\rm h}\in\mathbb{R}\) are parameters that arise from the random matrix theory. These parameters can be obtained through the use of an iterative fixed-point algorithm. As such, let a recursion on integer \(t\) be \(t=1,2,\dots\),_  
\[\delta_{\rm h}^{(t)}=\ {\rm tr}\Bigg{(}\mathbf{\Phi}_{\hat{\rm f}}\Bigg{(} \frac{K\mathbf{\Phi}_{\hat{\rm h}}}{\big{(}1+\delta_{\rm h}^{(t-1)}\big{)}}+N \zeta{\bf I}_{N}\Bigg{)}^{-1}\Bigg{)}, \tag{36}\]  
_with an initial value \(\delta_{\rm h}^{(0)}=1/\zeta\) and the parameter \(\delta_{\rm h}\in\mathbb{R}\) is determined as_  
\[\delta_{\rm h}=\lim_{t\to\infty}\delta_{\rm h}^{(t)}. \tag{37}\]  
_Solving the fixed-point equations in (36) and (37) leads to obtain_  
\[{\bf T}=\Bigg{(}\zeta{\bf I}_{N}+\frac{K\mathbf{\Phi}_{\hat{\rm h}}}{N(1+ \delta_{\rm h})}\Bigg{)}^{-1}, \tag{38}\]  
_and to determine \({\bf T}\in\mathbb{C}^{N\times N}\).
# Contributions of TOR Signaling on Photosynthesis
## 2 TOR: A Key Regulator in Photosynthesis
### TOR Regulates Leaf and Chloroplast Development
These results suggested the reciprocal regulations between TOR and ABA signaling to balance plant growth and stress responses, consistent with the role of TOR and ABA in seedling development and greening of cotyledons and leaves [39]. Scientists revealed that inhibition of TOR increased the expression level of a nuclear-encoded chloroplast RelA-SpoT homolog (RSH) gene _CmRSH4b. CmRSH4b_ encoded the guanosine 3\({}^{\prime}\)-diphosphate 5\({}^{\prime}\)-diphosphate (ppGpp) synthetases and regulated chloroplast rRNA transcription in the unicellular red alga _Cyanidioschyzon merolae_[65]. Researchers examined changes in oxidation during TOR inhibition in green alga _Chlamydomonas reinhardtii_. A reduction in photosynthetic electron flow and twenty proteins related with photosynthesis were identified with altered oxidation status under TOR inhibition [66].
# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multicolumn{2}{c}{**Methods**} & \multirow{2}{*}{**Modality**} & \multirow{2}{*}{**Modality**} & \multirow{2}{*}{**Dataset Contrast**} & \multicolumn{4}{c}{**Dice Index [\%]**} \\  & & & & **Contrast** & & **Conlases** & & **Liver** & **Kidney** & **Spleen** & **Pancreas** \\ \hline [PERSON] et al. & & & & & & & & & \\ (2015) [7] & Atlas & LOO & D-CT & CE & 150 & 94.9 & 93.6 & 92.5 & 71.1 \\ \hline [PERSON] et al. & & & & & & & & & \\ (2017) [10] & 3D CNN & 5-fold CV & D-CT & Mixed & 140 & 96.0 & 95.4 & 94.2 & — \\ \hline [PERSON] et al. & & & & & & & & & \\ (2017) [12] & 3D FCN & Testing & D-CT & CE & 331 & 95.4 & — & 92.8 & 82.2 \\ \hline [PERSON] et al. & & & & & & & & & \\ (2018) [20] & DenseVNet & 9-flod CV & D-CT & Mixed & 90 & 95 & 93 * & 95 & 75 \\ \hline [PERSON] et al. & & & & & & & & & \\ (2019) [13] & Multi Atlas & LOO & PET/CT & NCE & 69 & 88 & 79 & 74 & — \\ \hline [PERSON] et al. & & & & & & & & & \\ (2022) [14] & VNet & Hold out & PET/CT & NCE & 175 & 90.7 & 89.9 & 89.1 & 60.3 \\ \hline Proposed method & DenseUNet & 5-flod CV & PET/CT & NCE & 88 & 94.1 & 93.9 & 91.3 & 75.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with existing studies using the Dice index.
## 6 Geometry Optimization
### Constraints  
An engineering design problem, such as this one, often has some constraints such as, for example, manufacturing limitations. For the optimization process, these limitations must be, in some way, taken into account.  
The first optimization constraints are the upper and lower bounds of the proposed geometric parameters. It is easier to find a solution for a smaller search space. For this specific problem, the boundaries could be determined based on allowable overall dimensions of the flight simulator and dimensions of the payload. Even when bounds are known, often there is a standard set of values to choose from. This type of problem is known as a mixed integer problem. Table 2 shows predefined ranges of geometric parameter values and possible steps for values between bounds. If the precision of the manufacturing process is 0.01 mm, then the step can be 0.01 mm. If a larger step is chosen, the search space is smaller and if a smaller step is chosen, it is not possible to produce obtained geometry with that precision. These values for steps are chosen in order to lower search space. In this way, search space and the standard set for values of geometric parameters are defined.  
The next step is the definition of nonlinear constraints; two constraints of this type are given with Equations (22) and (23). If these inequalities are satisfied, then the desired position and orientation of the platform are achievable in terms of joints motion. In addition, there must be no interference between the legs of the mechanism. This type of constraint for lower levers can be defined as follows:  
\[\min\Bigl{(}\left\|\mathbf{D}_{i}\mathbf{D}_{j}\right\|_{2}\Bigr{)}>r_{min},\ i\
eq j, \tag{43}\]  
where \(D_{i}\) and \(D_{j}\) are any points on closed line segments (from \(B_{i}\) to \(A_{i}\) and from \(B_{j}\) to \(A_{j}\), respectively) of two lower levers that are part of different legs. This ensures that the minimum distance (expressed with Euclidean norm) between any two lower levers is always larger than the predefined value \(r_{min}\), and this can be adapted for any part of a mechanism.  
The following constraints ensure that the selected actuator can be used. Nonlinear constraints based on necessary torque and allowed forces for actuators are given with the following inequality:  
\[\left|\mathbf{M}_{\mathbf{m},\mathbf{i}}\right|<M_{m,max},\quad\left|\mathbf{Fr}_{\mathbf{R}} \right|<Fr_{R,max},\quad\left|\mathbf{Fat}_{\mathbf{R}}\right|<FaR_{R,max}, \tag{44}\]  
where \(M_{m,max}\) is maximum allowable torque while \(Fr_{R,max}\) and \(FaR_{R,max}\) are maximal allowable radial and axial force for gearbox.