esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Regeneration and Reproducibility of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\)  
Using the same batch of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) particles, the regeneration and stability of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) particles as Mag-dSPE adsorbents were studied through six adsorption-desorption experiments. Before adsorption, the adsorbed particles will be washed with a desorption agent three times to remove the residual desorption agent in the last desorption process. As shown in Figure S3, C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) can be reused up to five times and the extraction efficiency will not be significantly reduced, but, in the sixth adsorption process, the adsorption performance will be greatly reduced. In addition, the preparation reproducibility of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was evaluated, and the results showed that the RSD of the three batches of adsorbents for 11 food additives were all less than 8.8%. Therefore, C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) had good regeneration performance and stability, and its preparation process also had good repeatability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target** & **Linear Range** & **Linear Equation** & **R2** & **LOD** & **LOQ** \\  & **(ug/mL)** & & & **(ug/L)** & **(ug/L)** \\ \hline Acesulfame & 0.1–10 & Y = 22.86X + 7.068 & 0.9991 & 2.3 & 7.6 \\ Benzoic Acid & 0.1–10 & Y = 15.31X + 3.012 & 0.9994 & 2.0 & 6.6 \\ Sorbic Acid & 0.1–10 & Y = 20.27X + 5.462 & 0.9998 & 1.8 & 5.9 \\ Dehydroacetic Acid & 0.1–10 & Y = 6.585X + 8.065 & 0.9992 & 2.7 & 8.9 \\ Saccharin Sodium & 0.1–10 & Y = 7.042X + 1.530 & 0.9992 & 2.9 & 9.6 \\ Tartrazine & 0.1–10 & Y = 2.327X + 0.5170 & 0.9997 & 3.0 & 9.9 \\ Amaranth & 0.1–10 & Y = 15.720X + 1.708 & 0.9995 & 2.3 & 7.6 \\ Sunset Yellow & 0.1–10 & Y = 19.166X + 3.887 & 0.9995 & 1.2 & 4.0 \\ Allura Red & 0.1–10 & Y = 24.529X + 5.627 & 0.9993 & 0.6 & 2.0 \\ Brilliant Blue & 0.1–10 & Y = 18.27X + 1.075 & 0.9991 & 3.1 & 10.0 \\ Erythrosine & 0.1–10 & Y = 13.25X \(-\) 0.2473 & 0.9996 & 2.4 & 7.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Linear Equation, LODs, and RSD of 11 food additives (\(n\) = 6).  
Figure 12: Chromatographic separation diagram of 11 food additives. The wavelength of 240 nm (**a**); The wavelength of 410 nm (**b**). (1) Acesulfame; (2) benzoic acid; (3) sorbic acid; (4) dehydroacetic acid; (5) saccharin sodium; (6) tartrazine; (7) amaranth; (8) sunset yellow; (9) Allura red; (10) brilliant blue; (11) erythrosine.  
Furthermore, the preparative reproducibility of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was evaluated. The results show that the relative deviations of the recoveries for 11 food additives by using six batches of the sorbents are in the range of 3.7-9.4%. This indicates that the preparation procedure of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) has good repeatability and reproducibility.
# Study on the Position Deviation between the Iron Roughneck's Spin-Rollers and the Drilling Tool
## 3 Results and Discussion
### Experimental Results of the Spinner Mechanism with Single Follow-Up Roller
The rollers cannot fully contact the drilling pipe and cannot guarantee the effective spinning action. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Drill Pipe Inclination**} & \multicolumn{2}{c}{**Drill Pipe**} & \multicolumn{2}{c}{**Dimensional**} & \multicolumn{2}{c}{**Working Condition of**} \\ \multicolumn{2}{c}{**Angle (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Deviation Angle (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Deviation (mm)**} & \multicolumn{2}{c}{**Spinner Mechanism**} \\ \hline \(x\) **Direction** & \(y\) **Direction** & \(x\) **Direction** & \(y\) **Direction** & \(x\) **Direction** & \(y\) **Direction** & \\ \hline
89.90 & 89.45 & 0.10 & 0.55 & 3.52 & 19.36 & Unstable 1  \\ \hline
89.90 & 89.65 & 0.10 & 0.35 & 3.52 & 12.32 & Good 2  \\ \hline
89.90 & 89.75 & 0.10 & 0.25 & 3.52 & 8.80 & Good \\ \hline
89.90 & 89.85 & 0.10 & 0.15 & 3.52 & 5.28 & Good \\ \hline
89.80 & 89.90 & 0.20 & 0.10 & 7.04 & 3.52 & Average 3  \\ \hline
89.80 & 89.85 & 0.20 & 0.15 & 7.04 & 5.28 & Average \\ \hline
89.70 & 89.90 & 0.30 & 0.10 & 10.56 & 3.52 & Unstable \\ \hline
89.70 & 89.85 & 0.30 & 0.15 & 10.56 & 5.28 & Unstable \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Position deviation of the drill pipe and spin condition of the spinner mechanism with single follow-up roller.
## 3 Application and Results
### Pre-Processing for Data Analysis
Therefore, the significance of independent variables was considered when deriving the leakage management formulation according to the data analysis group. In this study, because the dependent and independent variables used different units, data standardization should be performed for correlation analysis and data analysis. Therefore, in this study, T-score was used, which was converted to an average of 50 and a standard deviation of 10. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multicolumn{2}{c}{**Standard of**} & \multicolumn{2}{c}{**PR**} & \multicolumn{2}{c}{**MR**} & \multicolumn{2}{c}{**LR**} & \multicolumn{1}{c}{**LD**} \\ \hline \multirow{8}{*}{Less than 60\%} & PR & 1 & - & - & - \\ \cline{2-7}  & MR & \(-\)0.191 & 1 & - & - \\ \cline{2-7}  & LR & 0.525 & \(-\)0.195 & 1 & - \\ \cline{2-7}  & LD & 0.710 & \(-\)0.157 & 0.664 & 1 \\ \hline \multirow{8}{*}{The revenue water ratio} & PR & 1 & - & - & - \\ \cline{2-7}  & MR & \(-\)0.136 & 1 & - & - \\ \cline{2-7}  & LR & 0.386 & \(-\)0.337 & 1 & - \\ \cline{2-7}  & LD & \(-\)0.373 & \(-\)0.165 & 0.151 & 1 \\ \cline{2-7}  & PR & 1 & - & - & - \\ \cline{2-7}  & MR & 0.026 & 1 & - & - \\ \cline{2-7}  & LR & 0.399 & \(-\)0.021 & 1 & - \\ \cline{2-7}  & LD & 0.113 & 0.023 & 0.431 & 1 \\ \hline \multirow{8}{*}{Higher than 80\%} & PR & 1 & - & - & - \\ \cline{2-7}  & MR & 0.106 & 1 & - & - \\ \cline{1-1} \cline{2-7}  & LR & 0.186 & 0.003 & 1 & - \\ \cline{1-1} \cline{2-7}  & LD & 0.240 & \(-\)0.065 & 0.671 & 1 \\ \hline \end{tabular}
\end{table} 
Table 4 shows the results of converting factors that have a correlation of 0.5 or more to a T-score in the correlation analysis between the dependent and independent variables to obtain the formulation of revenue water ratio of the leakage ratio of less than 10% data. According to the decrease in the leakage ratio, the variable values of PR converted to T-scores were 42.28-91.54, MR, 37.56-86.53; LR, 37.62-91.85; and LD, 40.16-89.58. T-score values of the independent variables were converted to a relatively similar range. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Study Area**} & \multirow{2}{*}{ \begin{tabular}{c} **Leakage Ratio** \\ **Reduction (\%)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Pipe Replacement** \\ **(PR)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Meter** \\ **(PR)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Leakage Repair** \\ **(LR)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Detection** \\ **(LD)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **T-Score** \\ **for PR** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **T-Score** \\ **for LR** \\ \end{tabular}  } & \multirow{
# Tensor Completion Based on Triple Tubal Nuclear Norm
## 3 The Triple Tubal Nuclear Norm
### Twist Tubal Nuclear Norm  
The twist tubal nuclear norm [16] is related to a pair of tensor operations named column twist and column squeeze as follows.  
**Definition 9** (Tensor column twist and column squeeze (here, the twist and squeeze operations in [1; 16] are renamed as column twist and column squeeze, respectively, since we will define the row twist and row squeeze) [16]).: _Let \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\), then the column twist tensor \(\mathbf{\mathcal{T}}_{1}=\overrightarrow{\mathbf{\mathcal{T}}}\) is a tensor of size \(n_{1}\times n_{3}\times n_{2}\) whose lateral slice \(\mathbf{\mathcal{T}}_{1}(:,k,:)=\mathbf{\mathcal{T}}^{(k)}\). Correspondingly, the column squeeze tensor of \(\mathbf{\mathcal{T}}_{1}\), i.e., \(\mathbf{\mathcal{T}}=\overleftarrow{\mathbf{\mathcal{T}}}_{1}\), can be obtained by the reverse process, i.e., \(\mathbf{\mathcal{T}}^{(k)}=\text{\rm{ColSqueeze}}(\mathbf{\mathcal{T}}_{1}(:,k,:))\). See Figure 2._  
Then, we give the definition of twist nuclear norm as follows:  
**Definition 10** (Twist tensor nuclear norm [16]).: _The twist tensor nuclear norm (t-TNN) based on the t-SVD framework is defined as follows:_  
\[\|\mathbf{\mathcal{T}}\|_{\overrightarrow{*}}:=\|\overrightarrow{\mathbf{\mathcal{T}}}\| _{*}=\|\text{\rm{bcirc}}(\overrightarrow{\mathbf{\mathcal{T}}})\|_{*}. \tag{10}\]  
From the above definition, t-TNN can be computed efficiently by first twisting the original tensor and then computing the TNN. Given a tensor \(\mathbf{\mathcal{T}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}\), the computation cost of \(\|\mathbf{\mathcal{T}}\|_{\overrightarrow{*}}\) is \(O(n_{1}n_{2}n_{3}(n_{2}+\log n_{2}))\). The t-TNN has attained significant improvement against the TNN in videoinpainting [16]. Since the t-TNN is essentially a TNN, the proximal operator of it can be derived from the proximal operator of TNN as follows:  
\[\text{ColSqueeze}(\mathfrak{S}_{\text{r}}(\overrightarrow{\mathbf{P}_{0}}))= \underset{\mathbf{\mathcal{T}}}{\text{argmin}}\ \frac{1}{2}\|\mathbf{\mathcal{T}}_{0}-\mathbf{\mathcal{T}}\|_{\text{F}}^{2}+\tau\|\mathbf{ \mathcal{T}}\|_{\overrightarrow{\mathbf{\mathcal{T}}}}.\]
# Therapeutic Potential of Polymer-Coated Mesoporous Silica Nanoparticles
## 10 Toxicity Aspects of Polymers  
One of the major parameters in selecting the polymer for coating purposes of MSNs is its inherent toxicity. Although in most cases polymers are known to reduce the toxicity of the wrapped material, e.g., PEG coatings, in other cases, the polymer might induce the overall toxicity. As discussed in this paper, many studies have utilized poly(dopamine) as a polymer for MSN coating. In one study, polydopamine coating was used to attenuate the in vivo toxicity of two well-known low toxic materials, i.e., quantum dots and poly(lactic acid) while in contact with blood or tissues, suggesting the highly biocompatible behavior of poly(dopamine) [76]. Another natural polymer used considerably for coating is chitosan, which is also known to be a biocompatible material and its toxicity is mainly based on the surface charge [77]. Hylauronic acid and poly(L-histidine) are usually considered as safe materials [78; 79] whereas high molecular weight poly (L-lysine) (>30 kDa) with LD\({}_{50}\) of 15 mg/kg is capable to produce undesired immunogenic responses [80].  
Being a cationic molecule, PEI poses cytotoxicity issues where high molecular weight branched PEI has been found to be more toxic than the linear or low molecular weight counterparts [81; 82]. However, the toxicity of PEI is concentration dependent and can be reduced by masking the polymer's cationic charge via derivatization, e.g., additional polymer coating. Although monomer NIPAM is toxic, poly(NIPAM) has been found to be less toxic in vitro and in vivo within working concentration ranges. The biodegradability of poly(NIPAM) can be tuned by making its copolymers, which also move the toxicity window of the copolymer to a higher concentration [83; 84]. All the other polymers reported in this article usually demonstrate concentration and time dependent toxicity, whereas the reported concentrations in the literature were well below the toxic level.  
Figure 14: Illustration of preparation of theranostic MSNs (**a**) graphene oxide coated and (**b**) EGRF antibody conjugated without graphene oxide coating, for targeted delivery and imaging in cancer cells. Reproduced with permission from reference [55].
## 2 Electronic Energy Transfer
This rate is determined from the Fermi rule [33], which is given by \(\Gamma\sim\left|M_{fi}\right|^{2}\) when omitting a proportionality constant that corresponds to the spectral overlap of the donor emission and acceptor absorption [7; 34]; the same constant of proportionality applies to all of the EET rate equations in our subsequent analysis. Assuming a system of two freely tumbling molecules, meaning that a rotational average is required [35], the following is found;  
\[\Gamma_{\text{EET}}\sim\frac{2\left|\mu^{\downarrow}(\text{D})\right|^{2}\left| \mu^{\uparrow}(\text{A})\right|^{2}}{3\left(4\pi\varepsilon_{0}R^{3}\right)^{ 2}} \tag{3}\]  
It should be noted here that EET might, in principle, also occur via couplings between a transition dipole (E1) and higher order transition multipoles of either electric or magnetic form, or even through the interactions of two such multipoles. Commonly, the efficiencies of such effects are negligibly small in comparison to EET via E1-E1 coupling [10; 36; 37]--though exceptions may arise, as for example, in recent work by [PERSON] et al. on ordered chiral materials, where magnetic dipole interactions come prominently into play [38]. When the process of energy transfer is modified by local fields, it is possible to approach theory by either of two alternative methods. One way is first to apply perturbation theory to correct the form of the donor and acceptor wavefunctions, taking into account the electromagnetic influence of these fields, then adopt these modified forms in evaluation of the correspondingly modified transition moments for direct inclusion in Equation (3). For example, under the influence of a static field all wavefunctions may be modified by a perturbation associated with the Stark effect; with throughput light the perturbation identifies with an AC Stark effect. In each case the alternative method, treating all the fundamental interactions in a single perturbation-based calculation, arrives at the results in a single step. Both methods yield the same results; we adopt the latter approach since it is correct, direct and the associated graphs lend clearer insights. Figure 1: Graphs for calculating the basic matrix element for electronic energy transfer, in which time is progressing upwards: (**a**) two separate time-orderings accounting for causality and (**b**) representation of the conflated result obtained from a near-zone approximation, in which \(R\ll\hat{\mathbf{\lambda}}\). In the vertical world-lines for D and A, red segments indicate an electronic excited state and black is the ground state. Between the two world-lines is a wavy green line that denotes virtual photon propagation; the horizontal dashed line indicates short-range dipole–dipole coupling.
# A Ku-Band Bi-Directional Transmit and Receive IC in 0.13 \(\upmu\)m CMOS Technology
## 2 Design of Ku-Band Bi-Directional Transmit and Receive IC
### Ku-Band 6-Bit Phase Shifter  
Figure 2 shows the circuit block diagram of a Ku-band 6-bit phase shifter with DPDT switches. The DPDT switch with a series-shunt circuit is designed to provide high port-to-port isolation. Figure 3 shows the schematic of the SPDT and DPDT switches. The DPDT switch is implemented with four 16-finger 64 \(\upmu\)m series transistors (T\({}_{1}\)-T\({}_{4}\)) and four 8-finger 40 \(\upmu\)m shunt transistors (T\({}_{5}\)-T\({}_{8}\)). The series inductors (L\({}_{1}\)) of 260 pH improve the input and the output return losses. The filter type phase shifting circuits are implemented in Figure 4. In the 11.25\({}^{\circ}\) and 22.5\({}^{\circ}\) phase states, BPF and LPF networks are used. Since a large shunt inductance is necessary to implement 11.25\({}^{\circ}\) and 22.5\({}^{\circ}\) phase states, the BPF with small series inductance is used to reduce the chip size. In the 45\({}^{\circ}\) and 90\({}^{\circ}\) phase states, HPF and LPF networks are used. To provide the wideband operation, two stages of HPF and LPF networks are used in the180\({}^{\circ}\) phase state. Table 1 summarizes the design parameters of the switched filter phase shifter. The 5.625\({}^{\circ}\) phase state and the 2.8\({}^{\circ}\) phase tuning bit are implemented with the switched LPF in Figure 5. The body floating technique is used in the switches to reduce the silicon substrate leakage. Vertically stacked spiral inductors are used to reduce the chip size. All the passive devices such as inductors, MIM capacitors, and interconnection lines were designed using the electromagnetic simulator.  
Figure 1: Block diagram of the Ku-band CMOS transmit and receive IC.  
Figure 2: Block diagram of the Ku-band 6-bit phase shifter.
# The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation
## 4 Discussion
### Survey Research  
A survey was conducted to verify the following hypothesis put forward by the authors: the use of the developed model of the communication process for an automatic communication system in maritime navigation will increase the effectiveness of communication by improving the exchange and clear interpretation of the transmitted information. A total of 32 people participated in the survey. The respondents, aged 31-50, held the rank of senior deck officer and were of various nationalities, such as Poland, Germany, Denmark, Spain, and the USA.  
This study aimed to compare communication taking place during collisions (through the current use of VHF) and communication implemented through automatic ontology-based communication. Some examples are given below (Figures 6-11).  
* Do you think that the current communication system at sea should be modernized?  
* Do you think that sending clearly understandable messages during the automatic communication process will increase the level of safety in maritime navigation?  
The analysis of the survey results showed that the following:  
* A total of 70% of the respondents believed that an additional method of communication between navigators is needed (beyond VHF);  
Figure 11: Answers regarding safety level enhancement for semiautomatic communication.  
Figure 9: Automatic communication talking time replies.  
Figure 10: Answers regarding the modernization of the communication system.  
* A total of 63.3% of the respondents would use communication based on exchanging messages with another ship;
* A total of 80% of the respondents stated that talking through an automatic communication system can shorten the time it takes for navigators to communicate, e.g., in a collision situation;
* A total of 83.4% of the respondents believed that sending clearly understandable messages during the semi-automatic communication process will increase the level of safety in maritime navigation.
# UAV Image Small Object Detection Based on RSAD Algorithm
## 5 Discussion  
The constructed ResNet-50 backbone network and retaining the multi-scale detection enhance the model's ability of extracting features from small objects in UAV images, and keep good robustness to scale changes. The SAFM feature fusion module reshaped, concatenated, and weighted non-parametric averaging for features to improve the model's recognition of small object features in UAV images and efficiently and fully utilize the semantic and geometric information in the deep and shallow layers. The introduction of Focal Loss function improves the detection accuracy of the algorithm on small objects in UAV images without affecting the detection speed of the model and increases the model's focus on small objects in UAV images.  
Through experiments we also found that when the input image size is 300 \(\times\) 300, the detection accuracies of many good object detection models show a substantial decrease [50; 51; 52], which is due to the fact that images with small sizes contain less feature information, and much of the feature information is gradually lost in the process of convolution. Many object detection models with high object detection accuracy often have large input image sizes, and the computational burden of large input image sizes increases, which is not conducive to the application of object detection models on UAVs. Real-time performance is one of the necessary conditions for object detection models to be applied on UAVs. Taking a 300 \(\times\) 300 image as the input to the model can significantly reduce the computational overhead and increase the detection speed, which is conducive to the deployment of object detection models on UAVs and achieving real-time detection.  
In the subsequent research on UAV image detection, for the problem of low detection accuracy of very small objects in UAV images, data augmentation and image super-resolution are considered to enhance the information contained in the image, which makes the backbone network better to extract its features. Meanwhile, the lightweighting of the model is further investigated, and the model is mounted on a UAV to realize a re-al-time object detection model for intelligent processing of UAV images in practical applications.
## 4 Discussion  
### Dependence of DER on Gold Nanoparticle Size  
In Figure 2a-c, the maximum DER values are found at the minimum proton beam energy (0.5 MeV) and minimum distance between the gold nanoparticle and DNA (30 nm). The maximum DER value is equal to 1.4 for the nanoparticle size of 15 nm (radius) as shown in Figure 2a. This value is smaller than that of the nanoparticle size of 25 nm radius (1.83 in Figure 2b) and larger than that of the nanoparticle size of 50 nm (1.29 in Figure 2c). It is seen that the DER value increases from a nanoparticle size of 15 nm to 25 nm and then decreases from 25 nm to 50 nm. When the nanoparticle size increases from 15 nm to 25 nm, the larger nanoparticle has a larger gold mass and particle volume for the interaction with the proton beam. This results in a larger yield of secondary electrons from the gold nanoparticle, and these electrons move to the DNA and cause dose enhancement. Therefore, the DER value increases. However, when the nanoparticle size further increases from 25 to 50 nm, the self-absorption effect of the nanoparticle becomes significant. This larger nanoparticle starts to absorb more secondary electrons generated within the nanoparticle [38]. Moreover, the larger nanoparticle would cause more proton beam attenuation, leading to a decrease of dose enhancement at the DNA. This dependence of DER on gold nanoparticle size is also observed by [PERSON] et al. [39] using a different simulation geometry.