esa-sceva/satcom-chunk-collection huggingface dataset:
# Surgical Wound Repair with Innovative Hemostatic Glue: Clinical Case Report
###### Abstract  
Background: The labial frenulum may be associated with traction on the marginal gingiva of the incisors with resulting gingival recession, a condition known as \"pull syndrome\". In this case, performing a frenuelectomy at least 6 months before root covering surgery is necessary. Recently, tissue adhesives have been studied as a good alternative to conventional sutures. Methods: A 34-year-old female affected by pull syndrome of the central lower incisors underwent frenulectomy, and an n-hexyl-cyanoacrylate tissue adhesive was used to ensure wound closure. Results: The patient was instructed in hygiene maneuvers and rinsed with 0.12% chlorhexidine-based mouthwash for 15 days. The glue was partially removed at 7 days, and for the remaining, natural disposal time was expected. The wound healing process was complete after 15 days with minimal postoperative discomfort. Conclusion: This n-hexyl-cyanoacrylate surgical glue could be proposed as a viable alternative to conventional sutures during surgical procedures involving wide flaps with the impossibility of first-intention healing. Moreover, it could be indicated to reduce operative time and patients' discomfort, even if these hypotheses should be confirmed by future clinical studies.  
frenulectomy; pull syndrome; tissue adhesive; n-hexyl-cyanoacrylate
## 2 Method
### Data and Processing
#### 2.2.1 Data Origin
of barges, local port unloading/loading barges, actual container volume, local port unloading/loading container volume, and other relevant information. The main issue with these data is data noise;
* _Ship violation inspection information_ comprises data such as the supervision and inspection number, inspection content code, inspection content, inspection results, violations or problems found, and description of violations or problems. The main issues with these data include missing data and data ambiguity;
* _Ship AIS information_ includes MMSI, Chinese and English ship names, heading, course, speed, longitude, latitude, draft, received time, and other relevant data points. The main issues with these data include data noise and missing data. Duplicate ship names and MMSI mainly characterize the problem of duplicate and redundant maritime ship data. The problem of ship data noise includes attribute data noise and spatial data noise. Attribute noise mainly refers to the problem of unclear data features caused by the complexity of field design. In contrast, spatial data noise mainly refers to the problem of data features not being displayed and data spatial anomalies caused by large amounts of data. The problem of missing ship data mainly includes features such as MMSI and trajectory being missing. The ambiguity problem of ship data is mainly caused by inconsistent information such as the ship's ownership company, ship contact person, and communication address. Based on the knowledge graph of ships, identifying illegal behavior of ship deck rigging may pose a threat to the safety of ships and may also pose a threat to the safety of crew members. The behavior judgment process requires collecting and processing a large amount of ship and crew information, which may involve personal privacy and require measures to protect it.
# Photocatalytic Lithography
## 4 Applications of Direct and Remote Photocatalytic Lithography
### Patterning of Graphene and Graphene Oxide
The superiority of the photocatalytic approach, compared to conventional photolithography and electron-beam lithography, was demonstrated by the development of an all-carbon field effect transistor (FET) array, a result which suggested the possibility of using photocatalytic lithography for the fabrication of graphene-based devices and circuits (Figure 8d-i). The vulnerability of reduced graphene oxide (RGO) towards \({}^{\bullet}\)OH has been demonstrated by [PERSON] et al. in 2014, whose study showed that prolonged (up to 100 min) exposure of aqueous suspensions of graphene oxide and titania nanoparticles to UV light from a xenon lamp (250 mW cm\({}^{-2}\)) first results in the formation of RGO and then leads to complete mineralization (i.e., degradation into smaller fragments and eventually to carbon dioxide) of the latter [56]. The proposed mechanism involves different steps, starting with the photoexcited generation of electron-hole pairs, direct transfer of electrons to graphene oxide, and production of reactive oxygen species, which then leads to graphene mineralization. Titanium dioxide is not the only photocatalyst by which degradation of graphene could be attained; as shown by [PERSON] et al. [57], zinc oxide (ZnO) is also suitable. In this work, a single-crystal ZnO was contacted directly with graphene deposited on a quartz substrate. Irradiationwith UV light (60 mW cm\({}^{-2}\)) was performed from the graphene side in ambient conditions for very long times (from 2 to 24 h), and the electrical and optical properties of graphene were measured after the treatment. Transmittance was found to have increased after 5 h of photocatalysis, and after irradiation for 12 h, the sheet resistance of graphene in contact with ZnO was found to be approximately 20 times higher than that of graphene irradiated without ZnO. [PERSON] et al. [58] exploited the photocatalytic oxidation of graphene to generate graphene/graphene oxide (G/GO) photosensitive heterostructures, which could be used as memristors. Photoactive ZnO nanoparticles were deposited on multilayer graphene, which was then irradiated with UV light (\(\leq\)365 nm, 0.03 J min\({}^{-1}\) cm\({}^{-2}\)) under a flow of humid air for different times (from 5 to 90 min) at different temperatures (room temperature or 80\({}^{\circ}\)C). Eventually, the ZnO nanoparticles were removed by dissolution with dilute hydrochloric acid. An irradiation time of 30 min at room temperature was found to give satisfactory results. The selective formation of graphene oxide in the zones where the photoactive particles were deposited, and the corresponding formation of G/GO heterojunctions, was confirmed by electrical measurements.
# Optimization of Brake Calipers Using Topology Optimization for Additive Manufacturing
## 3 Brake Calipers
### Brake System Kinetics  
The main job of an automotive brake system is to slow down a vehicle by applying a friction force. Hence, the kinetic energy, Ekinetic, of the vehicle is converted to thermal energy, Ethermal, which in turn is absorbed by the brake system. A simplified formula describing the relation between a vehicle's mass with a given velocity and the difference in temperature of the brake system is the following [24]:  
\[E_{kinetic}=E_{thermal},\ \ \ \frac{1}{2}m_{v}v_{v}^{2}=m_{BS}c_{h,BS} \Delta T_{BS} \tag{4}\]  
where  
\(m_{v}\): mass of the vehicle  
\(v_{v}^{2}\): velocity of the vehicle  
\(m_{BS}\): mass of the brake system  
\(c_{h,BS}\): specific heat capacity of the brake system  
\(\Delta T_{BS}\): temperature change in the brake system  
In a disc brake, the clamping force of the calipers to the brake discs is translated to a friction force opposing the disc's rotational direction and thus can be expressed by the equation:  
\[F_{friction}=F_{clamping}*\mu\left(=\frac{M_{b}}{\mu*r_{eff}}*\mu\right) \tag{5}\]  
where  
\(F_{friction}\): friction force on one wheel  
\(F_{clamping}\): clamping force  
\(\mu\): friction coefficient between pad and disc  
\(M_{b}\): brake torque on the wheel  
\(r_{eff}\): effective pad radius  
\(n\): number of friction faces  
The friction coefficient will be varied according to the applied pressure and temperature surface roughness connected to the wear. In addition, the moment of the disc is assumed constant throughout the rotating wheel assembly. In order to exploit the capabilities of a braking system, the tire should be the limiting factor, and both the front and rear axes should be on the verge of locking. A mathematical expression of that is  
\[m_{v}*g*\mu_{tire}=T_{limited}=m_{v}*g*z \tag{6}\]  
where  
\(T_{limited}\): max wheel torque, given by the tire-ground friction coefficient and vehicle weight \(\mu_{tire}\): friction coefficient between tire and ground  
\(g\): gravitational acceleration constant  
\(z\): deceleration proportional with \(g\)  
The Formula (6) describes the maximum negative acceleration without exceeding \(\mu_{tire}\). In addition to the wheel torque, other parameters such as the aerodynamic drag, the drivetrain losses, the gear meshing, the oil viscosity, and the rolling resistance can contribute to the braking force [24].
## 4 Experimental Results and Discussion
As depicted in Figure 12, the resulting images from the proposed method provided better performance in terms of subjective image quality compared with the standard CIIR method. Therefore, the proposed method achieved both objective and subjective improvements in the image quality of computational integral imaging systems. Figure 12: Experimental results with CIIR methods.
# Design of Multi-Chain Traceability Model for Pepper Products Based on Traceability Code
## 4 Construction of Multi-Chain Traceability Model for Pepper Products
### Smart Contract Design under Multi-Chain Traceability Modeling  
Smart contracts in the multi-chain traceability model mainly include data uplinking smart contracts; quality and safety standard monitoring smart contracts; mapping smart contracts; and query smart contracts. The smart contract specifies the format of the data, the process and rules for executing transactions, etc., and these rules together form the business model for transactions conducted by both parties.  
When each node in each link carries out data uplinking, the uplinking smart contract is invoked to first uplink all the data of the link to the side chain of the link, and then uplink the link traceability code and its corresponding transaction address of each link to the main chain.
## 5 Simulation Results and Discussion
### Discussion and Comparison of the Proposed PFL Approach with Existing Works  
In Table 3, we have compared our proposed solution approach using PFL with the previous FL and DRL approaches. Whereas earlier state-of-the-art FL and DRL methods achieved an approximate 50% reduction in model convergence time, our method based on PFL achieved an approximate reduction of 40% in the number of transmitted local model hyper-parameters from the vehicles to the UAV for a similar convergence time and similar number of training episodes. The proposed PFL approach also reduced the communication overhead between the vehicles and the UAV. However, unlike existing FL approaches, where joint learning from multiple datasets leads to higher model convergence time, our proposed PFL approach reduces the UAV's power consumption in a TTI. This is attributed to the fact that in PFL, rather than communicating with each vehicle, the UAV only communicates with select vehicles in a subset in each TTI. Another phenomenon observed for PFL with attention mechanism was that the average latency and model convergence time was lower by approximately 20% as compared to FedAvg and FedSGD. This also implies that PFL is suited to scenarios that consist of non-i.i.d. training data. To fine-tune the performance of our model, we introduced a personalization parameter and noted that utilizing PFL consistently improved the latency and the UAV's power consumption by 10-15% as compared to FedAvg and FedSGD.  
Figure 19: Variation in normalized MSE losses with number of training episodes for varying personalization parameter \(\varphi\).  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Proposed Method** & **Objectives** & **Cost Function** & **Reported Results** \\ \hline
[96] & \(\bullet\) & Alternative scheduling mechanisms & \(\bullet\) & Low packet drop ratio Packet retransmission & \(\bullet\) & Percentage of dropped messages \(\bullet\) & Achieved lower network latency \\ \hline
[97,98] & \(\bullet\) & Proposed deep Q-networks & \(\bullet\) & Enhanced agent mobility Improved decision making on centralized server & \(\bullet\) & System’s energy consumption Model complexity Task completion & \(\bullet\) & Improved system performance \\ \hline
[99] & \(\bullet\) & FL for network performance optimization & \(\bullet\) & Lowering model convergence time for FL Lowering model training loss for FL & \(\bullet\) & Optimal user selection for task association for task association & \(\bullet\) & Lowered FL model convergence time by 56\% Improvement in FL model accuracy by 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed PFL approach with recent applications of different deep learning and federated learning methods in vehicular communications.
## 3 Methodology
### HMM Foundations for Proficiency Assessment
#### 3.1.2 Algorithmic Solutions for HMM Problems in Proficiency Modeling  
HMMs provide a framework for addressing three fundamental problems, each of which has direct relevance to proficiency assessments:  
* Likelihood Evaluation: Given an HMM defined by \(\lambda=(A,B,\pi)\) (Equations (1)-(3)) and a sequence of observations, determine the probability of observing this sequence:\(P(O\mid\lambda)\). This is crucial for comparing how well different proficiency models (e.g., an HMM trained on expert data versus one trained on novice data) explain a new operator's observed performance sequence. The Forward Algorithm, a dynamic programming approach, efficiently solves this problem [59].
* Decoding: Given an HMM, \(\lambda=(A,B,\pi)\) (Equations (1)-(3)), and a sequence of observations \(Q=(q_{1},q_{2},\ldots,q_{T})\), find the likeliest sequence of hidden states that could have generated these observations. This is arguably the key problem for real-time proficiency assessment, as it allows the inference of an operator's proficiency trajectory (e.g., Novice \(\rightarrow\) Novice \(\rightarrow\) Intermediate) over the duration of a task or series of tasks. The Viterbi Algorithm is the well-studied method for finding this optimal state sequence (In everyday terms, the Viterbi Algorithm acts like a GPS for hidden states: it traces the single most likely \"route\" an operator's proficiency could have taken, step-by-step, to produce the observations you recorded). Importantly, the output of the Viterbi Algorithm is an inference of the most probable state sequence, not an absolute ground truth; its accuracy is contingent upon the quality and validity of the learned HMM parameters [60].
* Learning (or Training): Given a sequence of observations \(Q\) (and potentially the number of hidden states \(N\)), estimate the HMM parameters \(\lambda=(A,B,\pi)\) that maximize the probability of the observed data \(P(O\mid\lambda)\). This is how the proficiency models are constructed from the training data. The Baum-Welch Algorithm (In everyday terms, Baum-Welch is like tuning a radio: you keep nudging the dial until the signal comes in clearest, iteratively adjusting the model's probabilities so they best explain the data you hear), a specific instance of the Expectation-Maximization (EM) Algorithm, is commonly used for this unsupervised learning task. The nature and quality of the training data used with the Baum-Welch Algorithm are paramount [64]. The quality of training data is important; distinct proficiency models (e.g., Novice, Expert) require observation sequences from clearly categorized individuals to ensure model validity [65].
## 3 Experimental Observation of Various Soliton Molecules in Different Fibre Lasers
### Anomalous Dispersion Regime
#### 3.1.2 2-Micron Wavelength Regime  
Standard silica-based optical fibres generally feature a large anomalous chromatic dispersion at a wavelength around 2 \(\upmu\)m, so that passively mode-locked thulium (Tm) fibre lasers usually operate in large net anomalous dispersion regime. Combined with intense pumping, this situation is conducive to multiple pulsing at 2 \(\upmu\)m such as noise-like pulse generation [36], pulse bunching [37], soliton rains [38], harmonic mode locking [39] and soliton molecules [23; 24; 40].  
A Tm fibre laser based on NPE mode-locking technique is shown in Figure 9. The laser is backward pumped by a 1550 nm laser diode (LD) seeded erbium-doped fibre amplifier (EDFA). Two PCs and a polarization-dependent isolator (PD-ISO) are utilized for NPE mode-locking. 2.3-m-long thulium-holmium-doped silica fibre (TDF) is used as active fibre. The total cavity length is 4.7 m, with net dispersion of \(-0.329\) ps\({}^{2}\). The 50/50 output is monitored by an optical spectrum analyser, a second-harmonic autocorrelator, a 2 GHz oscilloscope via a 1 GHz photodetector and an RF signal analyser.  
Figure 8: (**a**–**c**) Optical spectra of different loosely bound states (insets: partial magnification); (**a**,**b**) 90/10 coupler; (**c**) 50/50 coupler; (**d**) Autocorrelation trace in correspondence with the bound state in (**c**). Inset is magnified view of the central peak fitted by sech\({}^{2}\) profile. (© 2013 IEEE, Piscataway, NJ, USA. Reprinted, with permission from [31]).  
Figure 9: Scheme of the NPE-based mode-locked thulium-holmium co-doped fibre laser; LD: laser diode; EDFA: erbium-doped fibre amplifier; WDM: wavelength division multiplexer; TDF: thulium-holmium-doped fibre; PC: polarization controller; PD-ISO: polarization-dependent isolator; OC: optical coupler. (Reproduced with permission from [23]. © The Optical Society of America (Washington, DC, USA), 2016).  
Under appropriate cavity parameters, stable soliton molecules are obtained, as shown in Figure 10. The optical spectrum, in conjunction with the optical autocorrelation trace, demonstrates phase locking of the two bound solitons. The symmetric structure of the spectrum with a central dip indicates that the soliton pairs are out of phase. The autocorrelation trace has three peaks with the intensity ratio of 1:2:1 and the same width, implying that the two bound solitons have the same intensity, pulse duration and a constant separation. The oscilloscope trace as well as the RF spectrum confirm again high stability.
## 5 Discussion  
There are a few limitations to be addressed when the suggested method is applied in realistic scenarios. For the measurements made during the five stages shown in Table 2, we placed the box perpendicular to the radar's boresight. However, it is also important to study measurement results when a box is placed at several different angles. We found that  
Figure 6: Materials used for range-profile capture: (**a**) bubble-wrap; (**b**) clothes; (**c**) water; (**d**) much; (**e**) soil; and (**f**) granite pebbles.  
Figure 7: The range-profile plot of the following materials: (**a**) bubble-wrap; (**b**) clothes; (**c**) water; (**d**) much; (**e**) soil; and (**f**) granite pebbles.  
orientation changes of more than about 15 degrees lowered the magnitudes close to the noise levels in measurements as the RCS became small. Accordingly, the proposed concept is valid when the incident angle of the radar wave is near 90 degrees.  
The use of this technique will be limited if the box is partially filled with materials. In principle, there will be two returns that arrive at the Rx of the radar; one that passes through the air and the other that passes through the material. If the time delay difference between the air and the material is not significant, the two returns will be co-located in the range profile, which makes it hard to discern them. In the range profile, the return that passes air will be dominant, as its amplitude is higher.  
In addition, there are cases where the reflection from the box is not strongly related to the mass of the material, for example, an empty box covered by aluminum foil. While aluminum foils will produce a strong return, the mass is small. The suggested method may cause an erroneous decision as to whether or not to collide with an object.  
It is also noted that scattering and penetration characteristics are a function of the carrier frequency. With the increase in frequency, the amount of penetration decreases while reflection/scattering becomes more significant. Therefore, the lower frequency is preferable in this scenario as the suggested approach uses the penetration characteristic.
## 3 Methodology
### Overall Framework of BPNN Fusion Model
#### 3.1.1 Selection of Polynomial Transformation Feature  
In robotic grinding, the workpiece often has specific contour requirements after grinding. The shape of the workpiece is, therefore, a key consideration. Cutting depth, as it can help control the surface contour to some extent, is typically used as one of the input parameters. However, in the robotic grinding scenario discussed in this paper, the target workpieces are irregularly shaped small-batch parts. The goal is not to transform these parts into specific shapes but rather to reduce surface roughness and improve surface quality without significantly altering their original appearance.  
Grinding force, as a control parameter, offers greater adaptability and reduces the risk of over-grinding. Through stable force-position hybrid control, the robot's grinding end-effector maintains constant contact with the workpiece surface, ensuring good surface quality and consistency while preserving the original contour. Furthermore, during the actual grinding process, the grinding force provides a more stable and controllable way to assess the grinding state. By monitoring the grinding force, it is easier to identify issues such as under-grinding or over-grinding, which could lead to surface defects. On the other hand, monitoring cutting depth requires additional measurement tools and technical support. Therefore, in this paper, cutting depth is removed as an input parameter and replaced by grinding force.  
Based on the above theory, polynomial features are generated by performing a polynomial transformation on four selected variables. An Analysis of Variance(ANOVA) was performed on the data collected from the preliminary experiment to determine whether the various features significantly affect surface roughness. The \(F\) statistic is calculated as:  
\[F=\frac{\text{MS}_{\text{between}}}{\text{MS}_{\text{within}}}=\frac{\frac{ \text{MS}_{\text{between}}}{\text{MS}_{\text{within}}}}{\frac{\text{MS}_{ \text{within}}}{\text{M}_{\text{within}}}} \tag{1}\]  
where:  
* \(\text{MS}_{\text{between}}\) and \(\text{MS}_{\text{within}}\) represent the between-group mean square and within-group mean square, respectively,
* \(\text{SS}_{\text{between}}\) and \(\text{SS}_{\text{within}}\) represent the sum of squares for between-group and within-group variances, respectively,
* \(\text{df}_{\text{between}}\) and \(\text{df}_{\text{within}}\) are the corresponding degrees of freedom. The \(F\)-test is used to compute the \(p\)-value: \[p=P(F>F_{\text{calculated}})\] (2)  
If the \(p\)-value is smaller than the predetermined significance level (\(\alpha=0.05\)), the null hypothesis of \"no significant effect\" (\(H_{0}\)) is rejected. It is concluded that the polynomial features have a significant effect on the outcome.