esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The proposed smart transfer planer offers a promising solution to enhance network resilience and address the challenges associated with satellite communication. By serving as a signal transmission channel between indoor and outdoor environments, the STP improves the communication performance of nonlinear sight waves and mitigates the low penetration characteristics of centimeter waves. The symmetrical architecture and electromagnetic coupling mechanism of the STP enable efficient power transmission and signal reception. With the ability to change beam directions, the STP facilitates the reception of centimeter-wave signals with indoor terminal devices. This innovation contributes to the advancement of satellite communication technologies, particularly in the Ku-bands, and offers a potential solution for enhancing broadband applications in the future.  
Conceptualization, D.-B.L. and H.-T.C.; methodology, D.-B.L. and H.-T.C.; validation, H.-T.R. and H.-Y.L.; formal analysis, H.-T.R. and H.-Y.L.; data curation, H.-T.R.; writing--original draft preparation, M.-L.C.; writing--review and editing, M.-L.C.; visualization, H.-T.R.; supervision, D.-B.L.; project administration, D.-B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Taiwan University of Science and Technology.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Development and Control of Biofilms in Diabetic Foot Infections: A Narrative Review
## 15 Other Anti-Biofilm Agents  
_Ginkgo biloba_ extracts, along with ginkgolic acid, are effective anti-adhesion agents that prevent the binding of bacteria such as _E. coli_ O157. They also suppress the expression of curli genes and disrupt fimbrae-producing genes [143; 144]. Eugenol independently inhibits csgABDFG and fimCDH, thereby preventing adhesion by _E. coli_. Phloretin is another anti-adhesion molecule that inhibits the expression of _lsrACDBF_ in _E. coli_, which is essential for the uptake of autoinducer 2 molecules. It also suppresses curli genes _csgA_ and _csgB_ and prevents the expression of toxin genes _hyleE_ and _stx2_[143; 145].  
AMP 108 is a synthetic peptide that represses alarmone signals in bacteria such _as Acintebacter humannii_, _P. aeruginosa_, _K. pneumoniae_, and _S. aureus_. This peptide is effective in eradicating mature biofilms, as the absence of the ppGpp alarmone signal reduces antibiotic resistance, virulence, and disrupts the biofilm-forming capacity of the bacteria [146; 147; 148].  
Fewer case studies are presented to demonstrate the effectiveness of these strategies [149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160]. A study involving diabetic foot ulcer (DFU) patients confirmed that biofilm presence significantly contributed to chronic infections and poor healing outcomes [132]. Biofilm formation limited the effectiveness of antibiotics, necessitating additional interventions like enzymatic debridement [161; 162; 163; 164; 165; 166; 167]. Anti-biofilm technology, used in a clinical pilot study, showed improved outcomes when biofilm-targeting antibiotics were selected for treating DFUs. Patients with concordant antibiotic and antiboofilm susceptibility had an 80% wound improvement compared to 38.5% in discordant cases [168]. Titanium oxide nanoparticles derived from _Ochradenus arabicus_ were shown to significantly reduce biofilm density and promote healing in DFI patients. This novel therapeutic approach is particularly promising for multidrug-resistant infections [169]. A combination of vancomycin and gentamicin, coupled with debridement, showed efficacy in managing DFIs with biofilm involvement. The study demonstrated the importance of addressing biofilm-specific resistance to enhance treatment success [170].
## 5 Field Tests and Discussion on Results
### Field Tests for NB-PLC-Based Demand Side Management System
#### 5.1.4 NB-PLC Test across Transformer  
The signal is injected from Laptop 1 and is received at Laptop 2. Laptop 1 is injecting the data stream through the transceiver connected with phase A of transformer LV side and is received from Laptop 2 through the transceiver connected to the same phase on the MV side and vice versa. Figure 9a-c illustrate the SNR profiles of transformer bridge for residential, commercial, and industrial consumers, respectively. The variations in SNR gains are from \(-20\) dB to \(30\) dB for residential consumers. Two deep nulls at \(150\) kHz and \(350\) kHz are worth noticing, which are due to resonance phenomenon in the transformer.  
Figure 8: SNR profiles of NB-PLC. (**a**) Communication on hybrid channels for residential consumers. (**b**) Communication on hybrid channels for commercial consumers. (**c**) Communication on hybrid channels for industrial consumers.  
Figure 7: SNR profiles of NB-PLC. (**a**) Communication on MV channel for residential consumers. (**b**) Communication on MV channel for commercial consumers. (**c**) Communication on MV channel for industrial consumers.
# High-Efficiency Closed-Loop Control of a Robotic Fish via Virtual Musculoskeletal Methodology
###### Abstract  
Improving propulsion efficiency holds the promise of enabling the robotic fish to work for a long time with a limited battery in its small body. In this paper, for the swimming of a bionic robotic fish, we present a virtual musculoskeletal control method from the bionic model of the joint driven by agonist muscle and antagonist muscle. A closed-loop method composed of two loops is proposed as a rule of thumb for the speed control of the robotic fish. The outer loop adjusts the swimming speed using the speed deviation; the inner loop regulates the stiffness according to the virtual muscle spindle feedback to fit the water environment. Compared with the proportion control, the evaluation results show that the virtual musculoskeletal methodology increases the efficiency by 3.4% in the steady flow and 7% in the Karman-vortex flow. This algorithm provides a new idea for the joint-space control of the bionic robots that need to reduce the energy consumption of movements.  
robotic fish; virtual musculoskeletal; closed-loop control; propulsive efficiency
## 4 Improved ACO for Global Path Planning
### Initial Pheromone and Expectation Heuristic Function  
The reasonable distribution of the initial pheromone is beneficial to speed up the search efficiency in the early stages of the ACO algorithm. The goal of this paper is to determine the initial pheromone based on the obstacle information of the starting to the ending point. Without an obstacle, the optimal path is the line connecting from the starting  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{_Iter_} \\ \cline{3-5}  & & **S** & **M** & **B** \\ \hline \multirow{3}{*}{_Value_} & N & M/B & B/M & S/S \\  & Z & S/B & B/M & S/S \\  & P & S/M & M/S & S/M \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy rules of \(\alpha\) and \(\beta\).  
Figure 4: The function of the fuzzy input and output variable.  
Figure 5: Correlation between the input variables and the output variables.  
to the ending point. In case of obstacles, the optimal path is when the mobile service robot walks along the line and avoids obstacles. Therefore, in the initial state, the grids that are on the line and close to obstacles should be given more pheromone than other grids. This can greatly accelerate the searchability of the algorithm in the early stage.  
Figure 6 illustrates how to distribute the initial pheromone with different grid maps. In the map, 1 and 2 represent the values of the critical obstacle influence factor. A large value will lead to densely distributed initial pheromones. Two-point connection and the surrounding of obstacles crossed by the line produce the critical obstacle influence factors. The grids on the line are more important than around the obstacles. Consequently, it takes a larger value.  
The expectation heuristic function indicates that ants tend to move towards grids that are close to the ending point. The new heuristic function introduces the critical obstacle influence factor, as shown in Equation (9):  
\[\eta_{ij}(t)=\frac{1}{d_{ij}}+\frac{g_{i}}{A} \tag{9}\]  
In Equation (9), \(g_{i}\) is the critical obstacle influence factor at grid \(i\) and \(A\) is a constant.
## 2 Materials and Methods
### Apparatus and Conditions
#### 2.2.2 Chromatographic Method  
Mobile phase A (10 mM NH\({}_{4}\)OAc) and mobile phase B (MeOH) were used during the chromatographic separations in the UltiMate(r) 3000 Binary Rapid Separation liquid chromatography system equipped with a DAD-3000 RS Diode Array Detector (Dionex, Sunnyvale, CA, USA). The detection wavelength was \(\lambda\) = 260 nm. During the study, ACE Excel C18 (1.7 \(\upmu\)m, 100 \(\times\) 2.1 mm) column (Advanced Chromatography Technologies Ltd., Aberdeen, UK) was used. The temperature of the autosampler and the column was 30 \({}^{\circ}\)C. The Chromleeon 7 chromatography data system was used for data collecting. The injection volume was 2 \(\upmu\)L. The flow rate was equal to 0.15 mL min\({}^{-1}\). The chromatographic separations were carried out in gradient elution mode. To analyze the samples with one compound, the gradient elution program was linear: 5-60% of solvent B in 5 min. For the separation of DNA-20, DNA-18, and the DNA-16 mixture, a gradient elution program from 5 to 20% of solvent B in 5 min and from 20 to 25% of solvent B in 10 min was run.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Abbreviation** & **Type of Modification** & **Molecular Mass (g mol\({}^{-1}\))** & **Sequence 5\({}^{\prime}\)-3\({}^{\prime}\)** \\ \hline DNA-20 & unmodified DNA & 6063 & GCCCAAGCTGGCATCCGTCA \\ DNA-18 & unmodified DNA & 5461 & GCCCAAGCTGGCATCCGT \\ DNA-16 & unmodified DNA & 4827 & GCCCAAGCTGGCATCC \\ DNA-11 & unmodified DNA & 3342 & GCCCAAGCTGG \\ miRNA372 & unmodified RNA & 7609 & AAAUGUCUGGCGACAUUUGAGCGU \\ miRNA222 & unmodified RNA & 6910 & AGCUACAUCUGGCUACUGGGU \\ PS-20 & phosphorothioate & 6368 & GCCCAAGCTGGCATCCGTCA \\ PS-11 & phosphorothioate & 3503 & GCCCAAGCTGG \\ ME-20 & 2\({}^{\prime}\)-O-methyl & 6621 & GCCCAAGCTGGCATCCGTCA \\ MOE-20 & 2\({}^{\prime}\)-O-(2-methoxyethyl) & 7657 & GCCCAAGCTGGCATCGGTCA \\ LNA-11 & locked nucleic acid & 3706 & GCCCAAGCTGG \\ \hline \end{tabular}
\end{table}
Table 1: The oligonucleotides used in this study.
# Traffic-Predictive Routing Strategy for Satellite Networks
###### Abstract
Predicting the satellite's traffic load for the next moment in advance is achieved through a neural network model. Based on the predicted traffic load and a comparison with a traffic threshold, data packets are selectively forwarded to prevent heavily loaded nodes from being included in the final established route. This helps avoid potential network paralysis by mitigating the presence of overloaded nodes in the overall routing. 2. Add a path change mechanism during routing. In this mechanism, congestion control signals are designed. Nodes that may be congested inform upstream nodes of changing paths in advance to avoid increasing congestion and achieve load balancing. 3. Considering the dynamic characteristics of satellite nodes and unstable characteristics of satellite links, the routing table is improved, and the concept of node stability is introduced in the route maintenance stage. According to the location of the fracture, choose the source node repair or local repair. In the local restoration, the stability of the upstream and downstream nodes of the fracture was compared. Select more stable nodes for path repair to avoid secondary route breakage and improve route stability and reliability. The structure of this paper is as follows: Section 2 introduces the research status of satellite network routing at home and abroad. Section 3 describes the overall framework of the G-ADDV routing protocol. Section 4 introduces the improved route discovery phase. Section 5 introduces the improved route maintenance phase. Section 6 verifies the advantages of G-ADDV through simulation. Section 7 summarizes the full text. The abbreviations and terms mainly used in this article are shown in Table 1.
# Generating Low-Earth Orbit Satellite Attitude Maneuver Profiles Using Deep Neural Networks
###### Abstract  
To perform Earth observations, low-Earth orbit (LEO) satellites require attitude maneuvers, which can be classified into two types: maintenance of a target-pointing attitude and maneuvering between target-pointing attitudes. The former depends on the observation target, while the latter has nonlinear characteristics and must consider various conditions. Therefore, generating an optimal reference attitude profile is difficult. Mission performance and satellite antenna position-to-ground communication are also determined by the maneuver profile between the target-pointing attitudes. Generating a reference maneuver profile with small errors before target pointing can enhance the quality of the observation images and increase the maximum possible number of missions and accuracy of ground contact. Therefore, herein we proposed a technique for optimizing the maneuver profile between target-pointing attitudes based on data-based learning. We used a deep neural network based on bidirectional long short-term memory to model the quaternion profiles of LEO satellites. This model was used to predict the maneuvers between target-pointing attitudes. After predicting the attitude profile, it was differentiated to obtain the time and angular acceleration profiles. The optimal maneuver reference profile was obtained by Bayesian-based optimization. To verify the performance of the proposed technique, the results of maneuvers in the 2-68\({}^{\circ}\) range were analyzed.  
LEO satellites; reference attitude profiles; target-pointing attitude; data-based learning; deep neural networks +
Footnote †: journal: Sensors
## 5 Experimental Results
### Performance Metrics
#### 5.2.4 Solution Efficiency  
Two experiments have been conducted to determine the solution efficiency. In the first experiment, the solution efficiency of the basic cybersecurity tasks is determined. In the second experiment, the solution efficiency of the advanced cybersecurity tasks is identified. The solution efficiency of the basic tasks is calculated as  
\[S_{eb}=\frac{t_{b}}{n_{b}}\times 100\% \tag{41}\]  
where \(S_{eb}\) is the solution efficiency of the basic tasks, \(n_{b}\) is the number of basic cybersecurity tasks, and \(t_{b}\) is the wasted time to solve these basic cybersecurity tasks.  
Figure 4c shows the solution efficiency of the proposed WBCA and contending state-of-the-art programs. The results demonstrate that the solution efficiency of the proposed WBCA is found to be 100%, while the solution efficiency of the contending program is 98.06-99.65%. Based on the result, we conclude that the proposed WBCA program is working efficiently. On the other hand, the contending programs reduce the solution efficiency. The CKS program is highly affected as compared to other contending programs. When we conduct the second experiment with the advanced cybersecurity tasks, the performance of the proposed WBCA is slightly reduced despite the fact that it gets 99.34%, which is much higher than contending programs depicted in Figure 5a. Based on the results, we observed that the contending programs show a lower solution efficiency of 95.48-98.12%. The CKS is again producing lower solution efficiency.  
Therefore, the solution efficiency of the advanced tasks is calculated as  
\[S_{ea}=\frac{t_{a}}{n_{a}}\times 100\% \tag{42}\]  
where \(S_{ea}\) is the solution efficiency of the advanced tasks, \(n_{a}\) is the number of advanced cybersecurity tasks, and \(t_{a}\) is the time wasted when solving these advanced cybersecurity tasks.  
Figure 5: (**a**) The correlation between the number of users and the time consumed when solving advanced cybersecurity tasks with the proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS). (**b**) The solution efficiency of the proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS) using the basic cybersecurity tasks. (**c**) The solution efficiency of the proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS) using the advanced cybersecurity tasks.
## 3 Results and Discussion
### Morphology Analysis
#### 3.2.2 Brunauer, Emmett, and Teller (BET) Analysis  
BET was used to study the surface area, surface volume, and porosity of the MIP and NIP, as tabulated in Table 2. The surface area of polymers has a considerable impact on compound adsorption. According to the BET data shown in Table 2, the MIP had a higher surface area, pore volume (425 m\({}^{2}\)g\({}^{-1}\)), and total area in the pore (258 m\({}^{2}\)g\({}^{-1}\)) than the NIP with 347 and 201 m\({}^{2}\)g\({}^{-1}\) of surface area and total area in the pore, respectively. This is consistent with the desired imprinting. With the NIP, these areas are smaller because there were no target templates for specific binding; however, smaller cavities or voids are formed, possibly by solvent evaporation or cross-linking density, as mentioned earlier. The same pattern has been observed in a previous study of NSAID adsorption on similar polymers by [PERSON] et al. [39]. It was reported that the synthesized MIP and NIP surface area were 282 and 232 m\({}^{2}\)g\({}^{-1}\), respectively. [PERSON] et al. [32] reported surface area results with the ARV drug abacavir imprinted MIP and its NIP analog of 372 m\({}^{2}\)g\({}^{-1}\) for both polymer materials. However, the surface area and total pore volume values are significantly larger in this study. This might be because more target compounds are used in this study that are of different classes; therefore, a larger surface area is required to accommodate the six-target compared to the three-target and single-target they were investigating in their studies. Polymers with larger surface areas are more effective in adsorbing water pollutants. There are more binding sites that are scattered in the cavity, suggesting that there is a greater imprinting impact for the MIP compared to the NIP. Also, looking at the total pore areas of the polymers, the MIP has a larger pore size area suggesting that there are more cavity imprints in the MIP as opposed to the NIP.  
Figure 5: SEM surface morphologies: (**a**) NIP SEM image, with an expanded view in (**c**) MIP SEM with an expanded view in (**b**,**d**).
## 4 General Formulas For Common Layer Types
### Dropout Layers  
Dropout layers can be regarded as probabilistic regularizers. For an imposed constant rate, \(r^{*}\!\in\![0,1)\), a dropout layer allows a random subset of its input signals, approximately a  
Figure 5: Forward computations of max and average pooling for \(S=4\).  
fraction of \(1-r^{*}\), to pass through as outputs, whereas the remaining part, approximately \(r^{*}\), is dropped, i.e., becomes zeroed.  
Consider a tensor, \(R=(r_{j,k,q})\), with the same shape as the input--\(H\times V\times C\), whose entries are drawn randomly from a standard uniform distribution, \(r_{j,k,q}\sim U(0,1)\). Treating \(R\) as a tensor caters to the general case. Dropout layers can, in particular, also be applied after flat or dense layers with tensor \(R\) reduced to a 1D vector. During the fit stage, the forward computations are performed as follows:  
\[Y:=\frac{1}{1-r^{*}}\cdot[R>r^{*}]\circ\phi(X). \tag{48}\]  
Note that the distribution of entries in tensor \([R>r^{*}]\) complies with the Bernoulli distribution (having the success parameter set to \(1-r^{*}\)).  
The \(1/(1-r^{*})\) factor in (48) is introduced to increase the strength of signals passing through. E.g., for \(r^{*}\)=0.2, the sum of surviving signals constitutes approximately 8/10 of the original total (in the sense of the absolute value). Therefore, the multiplication by 10/8 preserves a roughly similar flow compared to the one occurring when the dropout layer is switched off--namely, at the prediction stage. Then, _all_ the signals are allowed to pass through unchanged. The complete justification behind the manner in which the dropout operates is provided in the regularization-related Section 7.3. Perhaps surprisingly, the mechanism described above is connected to _model averaging_ techniques known from ensemble methods.  
As regards the backward pass, the \(\delta\)s from the subsequent layer are propagated in the following way  
\[\delta_{X}:=\phi^{\prime}(X)\circ\frac{1}{1-r^{*}}\cdot[R>r^{*}]\circ\delta_{ Y}. \tag{49}\]  
Note that, at the implementation level, the whole tensor \(1/(1-r^{*})\cdot[R>r^{*}]\) used during the forward pass (including the batch dimension), can be memorized and reused during backward computations. Figure 6 (right-hand side) illustrates backward computations for a dropout layer.
## 3 Initial Configuration Analysis
### Aerodynamic Performance Analysis of Initial Configuration  
As shown in Figure 10a, the model is divided into the fuselage and wings. Figure 11 illustrates the aerodynamic forces on the fuselage, wing, and composite configuration. As shown, the wing provides more than 50% of the total lift, while the fuselage provides the rest. The fuselage generates more drag than the wing. Moreover, the fuselage and composite configuration are statically unstable, while the wing is statically stable. Figure 11d indicates that the angles corresponding to the cruise lift coefficient and the maximum lift-to-drag ratio (K) are almost equal, while the angle corresponding to the zero-lift pitching moment is considerably larger. Consequently, the configuration has a nose-down moment at cruise, Table 5 lists the zero-lift pitching moments (C\({}_{\text{m}0}\)) and the cruising pitching moments (C\({}_{\text{m},c}\)) of each component. The fuselage offers the main zero-lift moment of the configuration, while the wing offers a major component of the pitching moment during cruise. Notably, the values of C\({}_{\text{m}0}\) and C\({}_{\text{m},c}\) are negative, which implies that these moments result in a nose-down condition. Thus, the longitudinal pitching characteristics of the initial configuration in typical states are very poor. Therefore, the aerodynamic design must be conducted with the constraint of longitudinal trim.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grid Name** & **Cell Number** & **C\({}_{\text{L}}\)** & **C\({}_{\text{D}}\)** & **C\({}_{\text{m}}\)** & **Simulation Time (Hour)** \\ \hline Grid 1 & 1,064,960 & 0.03320 & 0.00583 & \(-\)0.01999 & 1.08 \\ Grid 2 & 2,075,008 & 0.03296 & 0.00579 & \(-\)0.01992 & 2.08 \\ Grid 3 & 4,013,056 & 0.03259 & 0.00571 & \(-\)0.01972 & 4.07 \\ Grid 4 & 8,160,896 & 0.03256 & 0.00569 & \(-\)0.01968 & 8.90 \\ Grid 5 & 15,883,677 & 0.03255 & 0.00567 & \(-\)0.01962 & 18.63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of different grids.  
Figure 9: The grid of the model. (**a**) Grid topology. (**b**) Mesh around the model.