esa-sceva/satcom-chunk-collection huggingface dataset:
# Neutralization Dynamics of Slow Highly Charged Ions in 2D Materials
## 3 Discussion on Ion Interaction with 2D Materials
### Single Layer Graphene
is the ICD rate for interatomic distances \(<\)1 A [62] and \(d\) the target thickness), ICD wins over and the ion is measured as a neutral particle in the detector. If the ion has a finite velocity in the range of a few \(10^{5}\) m/s (\(>\)1 A/fs, i.e., \(>\)0.04 a.u. ), ICD can only partly succeed and some electrons remain in highly excited states after the ion passed the 2D target. The excited electrons will now continue to deexcite leading to auto-ionization and subsequent recharging of the ion. As a result, an ion in an intermediate charge state \(0<Q_{\text{exit}}<Q_{\text{in}}\) is measured. The statistical nature of the ICD process, i.e., which levels are populated, and the Auger-type auto-ionization lead to the width of the exit charge state distribution. Whether electrons emitted by ICD (from target atoms) and those from partly deexcited ions (at a later stage) can be distinguished by their energy (or angular distribution) needs to be clarified in future experiments. Electronic excitations in the surface produced by ICD of hollow atoms can later drive nanostructure formation at insulating surfaces (see Figure 3a-c) and perforation of 2D insulators (see Figure 5) due to electron-phonon coupling or Coulomb explosion [64]. At metallic surfaces or metallic 2D materials, electronic excitations are highly mobile and dissipate the excitation energy on a time scale much faster than atomic motion at surfaces. Before atoms can be displaced, i.e., electron-phonon coupling can set in, the (electronic) energy density gets small.
###### Abstract  
Diabetic retinopathy (DR), a consequence of diabetes, is one of the prominent contributors to blindness. Effective intervention necessitates accurate classification of DR; this is a need that computer vision-based technologies address. However, using large-scale deep learning models for DR classification presents difficulties, especially when integrating them into devices with limited resources, particularly in places with poor technological infrastructure. In order to address this, our research presents a knowledge distillation-based approach, where we train a fusion model, composed of ResNet152V2 and Swin Transformer, as the teacher model. The knowledge learned from the heavy teacher model is transferred to the lightweight student model of 102 megabytes, which consists of Xception with a customized convolutional block attention module (CBAM). The system also integrates a four-stage image enhancement technique to improve the image quality. We compared the model against eight state-of-the-art classifiers on five evaluation metrics; the experiments show superior performance of the model over other methods on two datasets (APTOS and IDRiD). The model performed exceptionally well on the APTOS dataset, achieving 100% accuracy in binary classification and 99.04% accuracy in multi-class classification. On the IDRiD dataset, the results were 98.05% for binary classification accuracy and 94.17% for multi-class accuracy. The proposed approach shows promise for practical applications, enabling accessible DR assessment even in technologically underdeveloped environments.  
attention; CNN; diabetic retinopathy; knowledge distillation; transformer
## 5 Application Examples
### Pneumatic Arm  
This example describes the application of the complete workflow of Figure 3 for the synthesis and implementation of a regulation controller for the electro-pneumatic arm with three degrees of freedom (3-DoF), as shown in Figure 7, which belongs to a small-scale manufacturing cell that assembles wood parts, named caps. According to the workflow, the first step is to obtain the Plant model. For this, the methodology of [19] was used, obtaining the _IPN_ depicted in Figure 4, which consists of four submodels, one for each double-effect pneumatic actuator and another for the suction cup.  
The arm must perform four different tasks:  
1. If there is a cap at the conveyor's _In_ position without a radio-frequency identification (RFID) tag, then the arm moves it to the RFID station (to attach a RFID tag to it);
2. If the cap has a RFID tag, then the arm moves it to the computer numerical control (CNC) station (to drill 10 holes on it);
3. If the RFID station has collocated a tag on a cap, then the arm moves the cap to the CNC station;
4. If the CNC has drilled a cap, then the arm moves it to the conveyor's _Out_ position. This Specification was written in the tables depicted in Figure 5. For instance, the first task _In_2_RFID_ starts from the _HomeArm_ state, characterized by \(\{XA,YA,ZA,VB\}\). This task is enabled by the signals \(S1 On\) (the cap is at the conveyor's _In_ position), _TagOff_ (the cap does not have a RFID tag) and \(RFIDIdle\) (the RFID station is available). When these are active, the subtask _TakenIn_ must be executed, defined as the operation sequence _InDownReleased_, _InDownTaken_ and _InUpTaken_, characterized by \(\{XA,YA,ZB,VB\}\) (i.e., at the _In_ position, the suction cup is down and inactive), \(\{XA,YA,ZB,VA\}\) (i.e., at the _In_ position, the suction cup is down and active) and \(\{XA,YA,ZA,VA\}\) (i.e., at the _In_ position,  
Figure 7: Small-scale manufacturing cell. The pneumatic arm has 3-DoF controlled by the same number of double-effect pneumatic actuators and a suction cup. This arm moves caps between the conveyor, the RFID station, and the CNC station (see the schematic from top view at the right).  
the suction cup is up and active), respectively. The \(IPN\) Specification depicted in Figure 5 was automatically computed with the app.  
Next, the Controller, the control program, and the closed-loop behavior were computed with the \"Controller tab\". The obtained control program is depicted in Figure 6 and described at the end of Section 4.4, which was translated to an IL PLC code with the \"PLC Code tab\", obtaining 554 code lines. This code was used to implement the program in a PLC connected to the actual pneumatic arm, obtaining the desired closed-loop behavior, depicted in Figure 8. Let us remark that the engineer only provided the model of Figure 4 and the Specification tables of Figure 5, from this point, the app automatically transformed the tables to the \(IPN\) model, automatically computed the \(IPN\) control program of Figure 6, and automatically translated it to PLC code.
## 5 Simulation Results  
This paper will evaluate the effect of coordinated preheating from indicators such as energy consumption cost, user bills, renewable energy consumption, and iteration rounds.  
The demo is a regional power system including 20,000 households, in which the installed capacity of wind power is 30 MW. All users are divided into ten groups to ensure that the adjustment of a single group has less impact on the system. \(T_{lb,t}\) and \(T_{ub,t}\) in Equation (20) are related to the user status, which includes awake at home, out of home and sleeping. The user status is divided by the time points shown in Table 2, where \(t_{up}\) denotes wake-up time, \(t_{t_{t_{t}}}\) denotes departure time, \(t_{t_{t}}\) denotes home time, \(t_{t_{t}}\) denotes bedtime. In addition, the relationship between these time points is as follows: \(t_{up}=t_{t_{t_{t_{t}}}}-d_{mor}\), \(t_{t_{t}}=t_{t_{t}}+d_{eve}\), in which \(d_{mor}\) and \(d_{eve}\) are the awake time spent at home in the morning/evening respectively. This paper assumes that \(t_{t_{t}}\) and \(t_{t_{t}}\) follow truncated normal distribution, and \(d_{mor}\) and \(d_{eve}\) follow uniform distribution, as shown in Table 3. The value of the time step is an integer between 0 and 96, and the time step is the scheduling interval, which is 15 min.  
The generation cost function required by the central controller can be in any form, including expressions, step function curve and even calculation programs. It is worth mentioning  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Status** & **Time Ranges** & **Temperature Range** \\ \hline Status 1: Awake at home & \([t_{up},t_{t_{t_{t}}}]\), \([t_{t_{t}},t_{t_{t}}]\) & [21 \({}^{\circ}\)C, 25 \({}^{\circ}\)C] \\ Status 2: Out of home & \([t_{t_{t_{t}}},t_{t_{t}}]\) & / \\ State 3: Sleeping & \([t_{start},t_{up}]\), \([t_{t_{t_{t}}},t_{end}]\) & [18 \({}^{\circ}\)C, 24 \({}^{\circ}\)C] \\ \hline \hline \end{tabular}
\end{table}
Table 2: User status division.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Distribution** & **Range** \\ \hline Time to leave home/(15 min) & \(t_{t_{t}}\)\(\sim\mathcal{N}(31,3^{2})\) & [25, 37] \\ Time to get home/(15 min) & \(t_{t_{t}}\)\(\sim\mathcal{N}(76,3^{2})\) & [70, 82] \\ Activity duration at home in the morning/(15 min) & \(d_{mor}\)\(\sim\mathcal{U}(2,9)\) & / \\ Activity duration at home at night/(15 min) & \(d_{eve}\)\(\sim\mathcal{U}(12,21)\) & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distribution of parameters.  
Figure 7: Interactive process of coordinated preheating.  
that the central controller only needs to perform a limited number of marginal cost calculations based on the generation cost during iteration, which can be extended to any user scale and network topology. As in [24; 25; 26], this paper sets the cost function in the form of quadratic function, that is, \(C_{total}=ax^{2}+bx+c\). The coefficient of the quadratic term is \(5\,\mathrm{\SIUnitSymbolMicro}/\mathrm{MW}^{2}\) h, the coefficient of the primary term is \(200\,\mathrm{\SIUnitSymbolMicro}/\mathrm{MWh}\), and the constant term is \(800\,\mathrm{\SIUnitSymbolMicro}\).
# Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis
## 3 Materials and Methods  
Many computational and physical models often require the numerical integration of polynomials times step functions. As a significant example, one can consider the stiffness matrix computation of a mechanical system in the framework of XFEM for the mechanical behaviour prediction of solids and structures containing one or more cracks [9; 10; 11]. As pointed out in Sections 1 and 2, introducing an integration algorithm to exactly integrate polynomials times double step functions in quadrilateral domains is useful since it significantly simplifies the numerical integration process in problems involving double discontinuities within an enriched element, removing the need for domain splitting, which is normally performed in such eventualities. Moreover, the proposed formulation facilitates the coding of the integration procedures in a wide range of applications, reducing the implementation and computational time required to solve such integration problems. In addition to computational mechanics, various fields, such as computer graphics, the evaluation of geometric region properties and computer simulations in general, could benefit from the proposed algorithm. In fact, similar integration problems have been recently addressed in different studies [55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75]. The proposed integration algorithm is described in detail in the following subsections by means of a practical example in which a quadrangular element of a generic body discretisation crossed by two discontinuities is considered. The need to integrate a polynomials times double step function emerged as the element stiffness matrix had to be computed.
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 5 Results and Discussion
### Message Loss Ratio (MLR)  
We evaluated and analyzed the loss warning messages or safety messages due to an unstable link connection. The TARS protocol transmits messages successfully at the specified destination. In this way TARS shows less loss ratio as compared to other similar studies. The warning and beacon safety message must be delivered to each neighboring vehicle without delay. A single delay or loss of any packet could result in loss of life. However, if the number of vehicles increases then the chances of lost packets also increase, as result causing instability of the network. Therefore, the TARS protocol shows less loss packets as compared to DBSR and POVRP. It means the proposed protocol provides the stability of the network and more packets or messages are delivered up to the specified destination. So, TARS provides a better and reliable routing scheme as per shown in Figure 15.  
Figure 14: Confidence Interval w.r.t Message Delivery Ratio in the highway scenario.  
Figure 15: Message Loss Ratio w.r.t. the Varied Vehicle Density in the highway scenario.
## 4 Conclusions  
1. We have significantly contributed with a speedy and efficient way of performing the designs, calculations, and planning of 5G NR networks for business/dense urban areas in compliance with coverage, quality, and capacity objectives. In our case, this was accomplished by applying the existing 3 GPP standards for 5G through the use and selection of the correct criteria, adequately applying the propagation models, urban density scenarios type, topography, LOS/NLOS criteria, service use cases, bandwidths for the user, link budget parameters, and frequency spectrum, and following best practices from commercial 5G network deployments.
2. The link budget calculations and coverage, quality, and capacity simulations obtained with the Atoll tool were highly accurate and consistent with the theoretical calculations obtained. This can be verified and confirmed by the average levels achieved for the coverage SS-RSRP (>90 dBm) and quality SS-SNR (>10 dB), which ensures a good data experience, good coverage for the target area, and outstanding data throughput levels (>50 Mbps per user), with all these being observed in both planned scenarios (3.5/28 GHz).
3. The results obtained from our contribution can make it easier for local mobile network operators to plan their 5G networks. This allows them to approach their designs more effectively, conducting their commercial deployments with successful commercial frequency bands in 5G networks.  
Conceptualization, V.F. and J.D.V.S.; methodology, J.D.V.S. and H.C.M.; software, V.F.; validation, J.D.V.S.; formal analysis, V.F. and H.C.M.; investigation, V.F. and J.D.V.S.; resources, V.F.; data curation, V.F. and J.D.V.S.; writing--original draft preparation, V.F. and J.D.V.S.; writing--review and editing, J.D.V.S. and H.C.M.; visualization, V.F; supervision, J.D.V.S.; project administration, H.C.M.; funding acquisition, V.F, J.D.V.S. and H.C.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
Figure 6: DL Throughput data rate plot with ACP: 3.5 GHz (**left**) and 28 GHz (**right**).
###### Abstract  
There are deviations between the radio telescope antenna finite element (FE) model, founded on the design stage, and the actual working antenna structure. The original FE model cannot accurately describe the antenna structure deformation characteristics under the environmental load, thereby compromising the accuracy of the active structural compensation. This article proposes an antenna FE model updating method founded on parameter optimization with a surrogate model. The updating method updates the modulus of elasticity parameters of different components of the antenna backup structure (BUS) to obtain finite element analysis (FEA) results consistent with the actual measurement of the antenna reflector surface shape. The surrogate model founded on the multi-quadratic radial basis function (RBF) improves the computational efficiency of FE model updating, replacing the complex and time-consuming finite element analysis and calculation process. This method is implemented on a radio telescope antenna with an aperture of 25 m. The results show a significant reduction in the mismatch between the antenna and the updated FE model. This method's calculation time is significantly reduced compared with the updating method without using the surrogate model, with the RBF surrogate model taking 1% of the time of the finite element model in the FEA calculations. The proposed method can improve the antenna FE model calculation accuracy and significantly enhance the efficiency of FE model updating calculations. Thus, it can offer a reference for antenna engineering practice.  
antenna; finite element; model updating; sensitivity analysis; surrogate model +
Footnote †: journal: Applied Sciences  
Article
## 2 Results
### Optimization of the Extraction Method
#### 2.1.2 Extraction Time  
Extraction time also affects extraction efficiency. The proso millet wine samples were extracted for 10, 20, 30, 40, and 50 min, respectively (Figure 1B). Results showed that the total peak area increased  
Figure 1: Effects of (**A**) sample volume, (**B**) extraction times, (**C**) extraction temperatures, and (**D**) NaCl addition on the extraction effects. (**A**) extraction time: 40 min, extraction temperature: 40 \({}^{\circ}\)C, NaCl: 1 g; (**B**) sample volume: 8 mL, extraction temperature: 40 \({}^{\circ}\)C, NaCl: 1 g; (**C**) sample volume: 8 mL, extraction time: 40 min, NaCl: 1 g; (**D**) sample volume: 8 mL, extraction time: 40 min, extraction temperature: 60 \({}^{\circ}\)C.  
with extraction time extension. Little change was found when it was prolonged from 40 to 50 min. Therefore, 40 min was chosen as the optimal extraction time. Extraction time is the time required for an analyte to reach equilibrium between the sample matrix and the stationary phase [19], which is one of the most important parameters in the HS-SPME process because it influences the equilibrium of the analytes between the headspace and the fiber coating.
# Study on Stability of Elastic Compression Bending Bar in Viscoelastic Medium
## 3 Results and Discussion  
According to the rheological test data obtained from soft soil samples in the engineering site selection area, the Burgers model was employed as the viscoelastic constitutive model for the bar-side medium in theoretical calculations. The constitutive model parameters were determined to be \(E_{M}=116.2270\) MPa, \(E_{K}=7.0203\) MPa, \(\eta_{M}=511.5671\) MPa\(\cdot\)d, and \(\eta_{K}=8.6031\) MPa\(\cdot\)d. The soil sample utilized in this rheological test was extracted from a depth of 9-11 m below ground level, with a median value of 10 m being selected. Consequently, in the phase space formulation, \(\overline{k}_{0}(s)\) represents the elastic ground reaction coefficient at a depth of 10 m below ground level. As per the definition of the ground reaction coefficient by the matlock method, the ground reaction coefficient increases linearly from 0 at ground level to the underground depth. Based on this definition, it was determined that the value of \(A\) in Equation (25) is 0.1. All solution is solved by MATLAB based on extension of the Burgers model's solution in the Section 2 above, by transforming \(K\) in Equation (25) into a basic burgers ground reaction coefficient which is treated under Laplace transform as \(\overline{k}(s,x)=A\frac{E_{1}\eta_{2}(E_{3}+\eta_{3})s}{\eta_{2}(E_{3}+\eta_ {3})s+E_{1}(E_{3}+(\eta_{2}+\eta_{3})s)}(L-x)\). Detail is shown in Appendix C.  
To enhance the accuracy of the calculation results, a solution accuracy of a seventh-order triangular series was employed. At this level of accuracy, the form of the coefficient \(\overline{w}_{n}(s)\) solved according to Equation (23) is highly complex and obtaining an analytical solution for \(w_{n}(t)\) through inverse Laplace transform is not straightforward. [PERSON] and [PERSON] summarized existing research on Laplace numerical inverse transform in literature [44] and proposed a unified theoretical framework for Laplace inverse transformation numerical inversion. In this study, a MATLAB program plug-in developed by [PERSON] based on this theoretical framework was utilized to write a calculation program and complete subsequent case calculations.  
To investigate the compressive stability of longitudinal and transverse bending elastic bars in viscoelastic media, the value of bar end displacement \(w(L)\) under long-term axial force and lateral loading was taken as a key parameter. This study examines the compressive stability of elastic compression bending bars in viscoelastic media.
## 4 Multi-Angle Analysis of RGN Predicted Results
### Structure Comparison Standard of RGN Predicted Results  
In the process of protein structure comparison, the similarity evaluation standard is to measure whether the predicted structure and the real structure similarity meet the reference value expected by researchers. In addition to _dRMSD_, we also introduce two other similarity comparison standards used in the process of multi-level structure comparison, namely Template Modeling Score (_TM-Score_) [34] and Global Distance Test-Total Score (_GDT_TS_) [35].  
In Section 4.1.3, only _dRMSD_ is used to evaluate the similarity between the predicted structure and the real structure. In the prediction process of some protein chains, due to coordinate deletion or low prediction accuracy at a residue or residue segment, the similarity at this residue is too low, and the positions of other residues are predicted well, and the overall _dRMSD_ value is too high. This is because _dRMSD_ is calculated under the global chain, and a large deviation will lead to a small overall similarity, thus judging the poor similarity of the two structures. Therefore, _dRMSD_ is sensitive to large deviation in the structure.  
Therefore, we also introduced another similarity metric-_TM-Score_, which requires the structure to be aligned in advance, then introduced the Kabsch algorithm [36] to achieve alignment. _TM-Score_ are defined by the following equation:  
\[TM-Score=\text{max}[\frac{1}{L_{target}}\sum_{i}^{L_{aligned}}\frac{1}{1+ \left(\frac{d_{i}}{d_{0}(L_{target})}\right)^{2}}] \tag{5}\]  
where \(L_{target}\) and \(L_{aligned}\) are the length of the full protein and the alignment region respectively, \(d_{i}\) is the distance between the \(i^{th}\) residue after the predicted structure and the actual structure are aligned, and the \(d_{0}(L_{target})=1.24\sqrt[3]{L_{target}-15}-1.8\) is the normalize scores. _TM-Score_ requires the structure to be aligned in advance, and the value range is (0, 1), with a score of \(<\)0.17 corresponding to random unrelated proteins, and \(>\)0.5 is usually applied to the same protein folding multiple.  
Because _TM-Score_ is not sensitive to some large deviations, the _GDT_TS_ is present. It is often used as the main evaluation criterion of CASP results, mainly used to indicate the similarity of two protein structures, which are defined by the following equation:  
\[GDT\_TS=(GDT\_P1+GDT\_P2+GDT\_P4+GDT\_P8)/4 \tag{6}\]where \(GDT\_Pn\) denotes the percent of residues under distance cutoff \(\leq n\)A, the similarity between the two structures is proportional to the value of \(GDT\_TS\), we use \(GDT\_TS\) as the main evaluation criterion.