esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontitis compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodondally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs.  
artificial intelligence; deep learning; diagnostic imaging; periodontitis; periodontal bone loss 2023
###### Abstract  
Prioritizing the heterogeneous traffic for Wireless Sensor Networks (WSNs) imposes an important performance challenge for Internet of Things (IoT) applications. Most past preemptive MAC schemes are based on scheduling the high priority packets earlier than those of lower priority. However, in a majority of these schemes, high priority traffic must wait for the ongoing transmission of lower priority traffic due to the non-availability of an interruption mechanism. This paper presents the design and high-level implementation details of a fragmentation scheme (FROG-MAC) for heterogeneous traffic in WSN. FROG-MAC aims at guaranteeing quick transmission of high priority/emergency traffic by interrupting ongoing on channel transmissions. High level implementation of FROG-MAC has been developed in MATLAB as a proof of concept. Traffic of two priorities was generated and a single hop star topology of 100 nodes was used for the experiments. Effect of the proposed fragmentation scheme has been evaluated on delay and Packet Drop Ratio (PDR) for both traffic types, by varying the packet size and fragment size. Simulation results have suggested that with the increasing packet size, the delay and PDR increase for both traffic types. When fragmentation was applied, the performance of high priority traffic significantly improved as compared to the low priority for both the parameters, delay and PDR. Furthermore, it has been found that decreasing the fragment size for low priority traffic results in reducing the delay for high priority traffic.  
FROG-MAC; fragmentation; heterogeneous; IoT; priority Article
# A High Compressibility Pressure--Sensitive Structure Based on CB@PU Yarn Network
## 3 Results and Discussion
### Pressure-Sensing Mechanism
\(R_{p}\) is the resistance of the sensor in the compression process and \(R_{p}\) satisfied Equation (3):  
Figure 4:6  
where \(\rho\) is the resistivity of the sensor, \(x_{0}\) represents the height of the sensor, \(x\) indicates the deformation and S is the cross-sectional area of the sensor. \(R_{c}\) is the sensor resistance correction coefficient used for correcting errors caused by yarn uniformity, vibration during compression, and the resistance of compressed layer. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Compression Amount** & **Compressed Layer Resistance (Kf)** & **Uncompressed Layer Resistance (Kf)** \\ \hline
0 & 0 & 130.9 \\
25\% & 0.028 & 99.0 \\
50\% & 0.060 & 64.3 \\
75\% & 0.085 & 32.5 \\
100\% & 0.110 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Resistance value of compressed layer and uncompressed layer under different compression amount. Figure 10: Schematic diagram of the structure of the pressure sensor. Due to the homogeneity of the yarn network, r remained constant during compression. Calculus idea was used to derive the relationships between the resistance value or the resistance change rate \(\varphi\) and the amount of compression. As shown in Equation (4) and Equation (5), respectively:  
\[\mathrm{R_{P}}=\int_{x}^{x_{0}}\mathrm{rd}x+\ \mathrm{R_{c}}=\mathrm{r(x_{0}-x) +\ R_{c}} \tag{4}\]  
\[\varnothing=\frac{\mathrm{R_{P}-R_{0}}}{\mathrm{R_{0}}}+\ \frac{\mathrm{R_{c}}}{ \mathrm{R_{0}}}=-\frac{\mathrm{rx}}{\mathrm{rx_{0}}}+\ \varnothing_{c}=\mathrm{GF_{0}}\cdot\mathrm{x}+\ \varnothing_{c} \tag{5}\]  
where \(\mathrm{GF_{0}}\) is the theoretical gauge factor of the sensor, \(\mathrm{GF_{0}}=-\frac{1}{x_{0}}\). \(\varnothing_{c}\) represents the correction factor of change rate of the resistance.
## 3 Results and Discussion  
Data from the Food and Agriculture Organization of the United Nations (FAO) show that more than 100 countries around the world are cultivating mushrooms, with China being responsible for more than 70% of global production [16]. Incorporating mushrooms in meat analogues could offer an opportunity to develop more sustainable and healthier meat-like products. Therefore, SPI and different edible mushrooms (_Lentinus eodes_, _Pleurotus ostreatus_, _Coprimus comatus_ and a mixture of equal proportions) were mixed and converted into edible mushroom-based meat analogues using extrusion technology. To explore the applications of this meat analogue, it was further processed to develop value-added products. Figure 1 shows that the changes in formulation and processing greatly affected the organoleptic characteristics and consumer acceptability in terms of the texture, sensory and flavor profiles.
## 3 Results and Discussion
### MES Operation and Biofilm Morphology of Biocathodes  
The bare and modified graphite cathodes were integrated with the microbial electrosynthesis (MES) reactor for CO\({}_{2}\) reduction to bioproducts (Figure 2). In this study, the CO\({}_{2}\) conversion efficiency was estimated by measurement of \(R\). _spharoides_ growth because the CO\({}_{2}\) fixed in microorganisms triggers a direct increase in their biomass via  
\begin{table}
\begin{tabular}{c c} \hline \hline  & \(\boldsymbol{\zeta}\)**-Potential (mV)** \\ \hline _Rlodobacter spharoides_ & \(-31.88\pm 3.31\) \\ Bare graphite felt & \(-10.13\pm 2.72\) \\ Graphite felt/chitosan & 1.81 \(\pm\) 0.61 \\ Graphite felt/chitosan/carbodiimide & 1.74 \(\pm\) 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Zeta-potential results for \(R\). _spharoides_, bare graphite felt, chitosan-modified graphite felt, and chitosan/carbodiimide compound-modified graphite felt in 0.1 M sodium phosphate buffer (pH 7.0).  
Figure 4: Evidence of electrostatic interaction enhancement between modified electrode and redox active species. (**a**) Cyclic voltammograms of bare and modified glassy carbon electrode (GCEs) in 0.1 M sodium phosphate buffer (pH 7.0) containing 1 mM of [Fe(CN)\({}_{6}\)]\({}^{3-}\). Scan rate, 20 mV s\({}^{-1}\) (versus Ag/AgCl). Bare indicates bare GCE. Chit indicates chitosan-modified GCE. Chit/CDI indicates chitosan/carbodiimide compound-modified GFE. (**b**) Schematic illustration of the redox reactions of diffusing and confined [Fe(CN)\({}_{6}\)]\({}^{3-}\) ions in a chitosan layer. (i) Bare electrode, (ii) chitosan-modified (GFE/Chit), and (iii) chitosan/carbodiimide compound-modified electrode.  
growth [23; 24]. The production of succinic acid, which is a robust organic product of CO\({}_{2}\) conversion in _R.__shareoides_, was also considered the target product in this MES reaction [25; 26]. The MES reactors were operated for 14 d under supplementation with 5% CO\({}_{2}\) at an applied potential of \(-\)0.6 V (vs. Ag/AgCl). Charge transfers from the working electrodes (cathodes) to the catholyte materials (_R.__shareoides_ and medium) reached 0.78, 0.93, and 0.94 mWh cm\({}^{-1}\) for bare, chitosan-modified, and chitosan/carbodiimide compound-modified GFE, respectively.  
Figure 5 shows scanning electron microscope (SEM) images of bare (a,d) and modified GFEs (b,c,e,f) before (a-c) and after MES reactions (d-f). Sheath-like thin layers were observed at chitosan-coated GFE cathodes (b,c). _R.__shareoides_ cells attached to the cathode surfaces were remarkably increased following the order: bare < chitosan-modified < chitosan/carbodiimide-modified GFE. The results support the hypothesis that the modified cathode, especially chitosan/carbodiimide-modified GFE, facilitated the migration of _R.__shareoides_ (Figure 3), due to their positive and covalent functionality [13].
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 7 TN Domain  
In the TN domain, the technology radar requires the analysis from two separate dimensions: transport technologies and transport SDN fabric. The _transport technologies_ dimension captures the protocol encapsulation and data plane solutions across the different network segments. On the other hand, the _transport SDN fabric_ dimension refers to the control and management plane aspects, discussing the application of programmability and automation through SDN.  
These two dimensions are not coupled but complementary, in the sense they can evolve at a different pace and they do not necessarily need to be used together. The transport technology dimension includes all the Layer 1/2/3 solutions that allow segregating connectivity resources and enforcing traffic separation at the WAN infrastructure, therefore enabling TN slicing realization. The sole use of these transport technologies is enough to have a sliced WAN infrastructure, but not to get it provisioned and operated in a dynamic way. The latter would require implementing programmability and automation capabilities atop these technologies, something that can only be brought by the SDN paradigm. The complementarity of the transport SDN fabric lies in the ability to operate with these technologies using a set of SDN controllers instead of traditional, siloed TN management systems (which typically lead to rather static, manual configurations). This requires equipping these SDN controllers with programmatic interfaces on the southbound side, towards the underlying technology devices. These interfaces shall be developed in such a manner that SDN controllers can inject configuration actions and retrieve collected data following a model-based approach.
# Predictive Current Control for Switched Reluctance Motor Based on Local Linear Phase Voltage Model
###### Abstract  
In this paper, a predictive phase current control (PCC) scheme based on a local linear phase voltage model for a switched reluctance motor is proposed. The current is controlled by regulating the average voltage through PWM, ensuring a fixed switching frequency. A linear model is proposed to approximate the relationship between the voltage and the current slope in a short period. By using the voltage and current slope information in the previous control cycle, the intercept and slope of the model can be identified online. In the previous control cycle, the phase voltage changes from zero to positive or negative DC-link voltage, and then the identified model is used to predict the average voltage required in the next PWM cycle for the actual current so as to accurately track its reference. The effectiveness of the proposed PCC was verified experimentally. The results demonstrate that the proposed control scheme can significantly reduce the current and torque ripples compared to hysteresis control with the same sampling rate. The proposed PCC is easy to implement, does not need to obtain the motor characteristics in advance and is not sensitive to the changes in characteristic parameters caused by motor aging, etc. It is relatively suitable for applications that need to accurately track the given current curve.  
switched reluctance motor; predictive control; hysteresis control; local linear phase voltage model +
Footnote †: journal: Applied Sciences
# Interfacial Shear Properties of Red Clay and Polyurethane with Different Densities
###### Abstract  
Polyurethane reinforced slopes have been widely used in practical engineering, for the stability of the composite structure, the interfacial shear performance between polyurethane and soil plays a crucial role. This paper through the indoor interfacial shear test, study the relationship between shear displacement and shear stress at the polyurethane-red clay interface under the different polyurethane densities. At the same time, based on the bilinear finite element cohesive model, using ABAQUS finite element software to establish a numerical model of polyurethane-red clay composite specimens to simulate the indoor shear test process, and to study the shear stress and shear strength changes at the interface between polyurethane and red clay; verify the feasibility of the numerical model by comparative analysis. The test results show: The incorporation of polyurethane has a significant promoting effect on the interfacial shear strength, there is a linear positive correlation between the interfacial shear strength and the density of polyurethane; The shear strengths of the composites with densities of 0.2 g/cm\({}^{3}\), 0.5 g/cm\({}^{3}\), and 0.8 g/cm\({}^{3}\) are 1.53 times, 1.60 times, and 2.24 times that of pure red clay respectively, the optimal polyurethane density is between 0.5 g/cm\({}^{3}\)-0.8 g/cm\({}^{3}\); The bilinear finite element model can effectively simulate indoor shear tests, and the average error of the simulated shear strength result is within 9.4%, which provides an effective method for understanding the interfacial shear performance of the composite body.  
Keywords:polyurethane; red clay; shear characteristics; ABAQUS finite element method; numerical simulation +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Random Forest
Furthermore, XGBoost offers a flexible learning mechanism that allows you to optimize the model by changing various parameters. Moreover, overfitting may be avoided, and visualization is easier than with a neural network, making it easier to comprehend intuitively. Above all, XGBoost outperforms existing models and is one of the most often utilized machine learning models in recent research. LightGBM  
The present Gradient Boosting Decision Tree (GBDT) has a time-consuming issue in that it must explore all data instances in order to estimate the information gain of all feasible split points for each feature. When the dimension of the feature is enormous, and the amount of data is large, it is difficult to obtain the appropriate results, especially in terms of efficiency and scalability. Gradient-based One Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) are the two main methods proposed to overcome these problems. LightGBM is a newly developed GBDT model based on GOSS and EFB that achieves almost similar performance to standard GBDT while training 20 times quicker [33]. In GBDT, data entities do not have a default weight, but data entities with varying gradients perform diverse roles in computing information gain. Subjects with large gradients should be retained, and subjects with small gradients should be dropped randomly to maintain the accuracy of the information gain estimation when down sampling data objects, because, by definition, an individual with a larger slop (undertrained) will contribute more to information gain. Those with large gradients are maintained by GOSS, while objects with smaller gradients are sampled at random. It provides a constant multiplier for data objects with small slopes while computing information gain in order to rectify the influence on data distribution in the random sampling process. When the fraction of identical target samples is the same, the approach described above can result in a more accurate gain estimate than equally random sampling, particularly when the range of information gain values is large. This strategy, known as GOSS, has been demonstrated to work. The variable space of high-dimensional data with a large number of characteristics is often quite sparse. This allows for the creation of a practically lossless method for reducing the number of variables. Many variables are virtually exclusive, especially in the sparse variable space. The optimum bundling issue was reduced to a graph coloring problem, and the altered graph coloring problem was solved with a greedy method with a specific approximation ratio. These distinct variables may be securely grouped into a single variable, EFB (Exclusive Feature Bundling) [33]. GOSS and EFB are strategies for dealing with large data objects and large variables, respectively, and they demonstrate that these two methods outperform XGBoost in terms of computing speed and memory utilization. It also has the advantage of allowing for automated and optimum category feature division.
## 3 Quality Control of Silicon Carbide Printed Part Formation
### Densification Post-Processing Techniques

Table 6: Precursors for the PIP process in SiC-based component fabrication. Finally, for the CVI process, the long preparation cycle and high cost limit its application. However, ceramics prepared by CVI exhibit high purity, high modulus, and high strength, making them primarily suitable for high-end and extreme environments where material purity is critical. It is generally recognized that ceramics fabricated via CVI still retain an approximate porosity of 10% [145]. Therefore, current research focuses on integrating CVI with other processes to manufacture high-end products that demand high strength, high purity, and high densification. [PERSON] et al. [163] combined spray drying, binder jetting, and chemical vapor infiltration (CVI) to fabricate high-purity SiCV/SiC components, achieving a flexural strength of up to 200 MPa and a fracture toughness of 3.4 MPa\(\cdot\)m\({}^{1/2}\). [PERSON] et al. [164] integrated PIP, CVI, and CVD (chemical vapor deposition) to fabricate SiC ceramic components for solar energy receivers, resulting in a 100% increase in apparent density and an approximately 80% reduction in open porosity. Comprehensive analysis indicates that BJ 3D printing technology combined with optimized densification post-processing can significantly improve the overall performance of SiC components. However, systematic comparison of key performance parameters between these and SiC samples prepared by other methods is required to fully evaluate the advantages and limitations of this technology in terms of material properties and industrial feasibility. Firstly, among the improved techniques for traditional SiC component manufacturing reported in recent years, SiC samples fabricated by modified gelcasting and solid-state sintering exhibited a flexural strength of 128.0 MPa [165], while those prepared via pressureless solid-state sintering achieved a flexural strength of 443 \(\pm\) 27 MPa [166]. Samples produced using microwave sintering assisted with a heat molding process demonstrated a bending strength of 492.7 \(\pm\) 36.8 MPa [167], and those fabricated by spark-plasma sintering reached a relative density of 97.5 \(\pm\) 0.6% of the theoretical value [168]. As evidenced by the comparative literature data in Section 3.2 of the manuscript, the flexural strength and densification rate of BJ 3D-printed SiC samples are generally lower than those obtained by improved traditional SiC manufacturing techniques (though the flexural strength exceeds that of samples fabricated via modified gelcasting and solid-state sintering). The density and bending strength of SiC samples produced by BJ 3D printing still fall short of the levels achieved by conventional SiC manufacturing methods and their improved variants. However, the advantages and potential of binder jetting 3D
# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
###### Abstract  
In the context of the rapid popularization of clean energy, the precise identification of surface defects on photovoltaic modules has become a core technical bottleneck limiting the operational efficiency of power stations. In response to the shortcomings of existing detection methods in identifying tiny defects and model efficiency, this study innovatively constructed the EER-DETR detection framework: firstly, a feature reconstruction module WDBB with a differentiable branch structure was introduced to significantly enhance the feature retention ability for fine cracks and other small targets; secondly, an adaptive feature pyramid network EHFPN was innovatively designed, which achieved efficient integration of multi-level features through a dynamic weight allocation mechanism, reducing the model complexity by 9.7% while maintaining detection accuracy, solving the industry problem of \"precision--efficiency imbalance\" in traditional feature pyramid networks; finally, an enhanced upsampling component was introduced to effectively address the problem of detail loss that occurs in traditional methods during image resolution enhancement. Experimental verification shows that the improved algorithm increased the average precision ([EMAIL_ADDRESS]) on the panel dataset by 1.9%, and its comprehensive performance also exceeded RT-DETR. Based on the industry standard PVEL-AD, the detection rate of typical defects significantly improved compared to the baseline model. The core innovation of this research lies in the combination of differentiable architecture design and dynamic feature management, providing a detection tool for the intelligent operation and maintenance of photovoltaic power stations that possesses both high precision and lightweight characteristics. It has significant engineering application value and academic reference significance.  
defect detection; RT-DETR; photovoltaic panels; lightweighting; small target IEEEexample:BSTcontrol