esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Strengths Characteristics
Furthermore, in the DIC analysis at 20 Hz, no significant change in the principal stress was detected in the S7 specimen until just before failure, whereas a clear change in the maximum principal stress was observed at the expected crack location in the H7 specimen. The results indicate that the application of a 30% strontium nitrate solution, which penetrates the surface of the specimens, alters the mechanical characteristics of the cement hydrates up to the depth of penetration, as previously mentioned. Consequently, the load-displacement curve observed in the specimen treated with strontium ions reflects a condition of a non-uniform cross-section, which is attributed to the differential modification of hydrates throughout the cross-section. Therefore, the extent of improvement in mechanical properties is influenced by factors such as the depth to which strontium ions penetrate and the dimensions of the specimen. Figure 7a,b show the DIC analysis images for the compressive strength tests of specimens H7 and S7, respectively. These images depict the strain in the x-x direction just before failure, with red indicating areas of high strain. For the H7 specimen, a typical mode of compressive failure was observed, characterized by a 45-degree wedge fracture leading to collapse. In contrast, the S7 specimen exhibited a sudden delamination of the surface layer as a mode of failure. This difference in failure mode is attributed to the altered strength characteristics of the material up to the penetration depth, caused by the strontium nitrate solution that penetrated the specimen. Figure 7c displays the cross-section of S7, showing a penetration depth of 2 to 5 mm along the periphery. The failure occurred in the form of delamination at the boundary between the altered and unaltered regions. Figure 6: Flexural behavior and DIC analysis. Figure 7: Strain analysis at compressive failure via DIC.
## 3 Manufacturing
### Composite Layup and Infusion  
As mentioned in Section 2.2, we designed the composite layup concurrently with the additively manufactured mold to achieve the desired outer hydrodynamic profiles. A triaxial fiberglass fabric with an average ply thickness of 0.9 mm was used for all areas of the blade section. The root was designed to incorporate 14 plies tapering in length over the root section of the mold. The spar caps were designed to be four plies thick and tapered in both length and width toward the main foil section of the mold. Finally, a single skin ply was to be wrapped over the full length and chord of the mold. A structural flow fiberglass media was used as the first ply to encourage resin flow across the entire mold. As previously stated, this was not intended to be an optimized structural design, but rather a representative design for manufacturing purposes.  
Figure 7 shows the layup process. First, the structural flow media was applied to the mold and held in place with spray adhesive. The first half of the root plies could then be applied, followed by all 12 of the root inserts (see Figure 7a). The remaining 7 root plies were then placed over the top of the inserts (see Figure 7b). The high-pressure and low-pressure spar cap plies were then installed (see Figure 7c), followed by the final skin ply, which was wrapped tightly around the leading and trailing edges to ensure tight contours in those critical areas (see Figure 7d). The blade section was then vacuum-bagged with vacuum lines installed along the leading and trailing edges to ensure resin flow from the center of the mold over the entire surface and to control it during the infusion process.  
Figure 6: The 3D-printed mold sections after being sanded, sealed with epoxy, and fully assembled, ready for the composite manufacturing process. The root inserts were bolted in place for display purposes.  
For the resin infusion process, we used Hexion's Epikote RIMR135/Epikure RIMH1366 two-part epoxy resin system, which is commonly used in the manufacturing of wind turbine blades [23]. Figure 8 shows the VARIM process. The transition section wet out first, followed by the thicker root section. The main foil section was last to fully saturate because the epoxy resin had to flow around the shear web before reaching the main surfaces. Overall, the entire blade section became fully saturated with epoxy as intended.  
Figure 8: Photo taken mid-infusion, showing the epoxy resin saturating the fiberglass around the transition section and beginning to flow through the root and main foil sections.  
Figure 7: Showing the composite layup process with (**a**) the first half of the root plies and root inserts installed, (**b**) the second half of the root plies, (**c**) the spar caps, and (**d**) the final skin ply.
# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
###### Abstract  
To intelligentize the top-coal curing's process, many data-driven coal-gangue recognition techniques have been proposed recently. However, practical applications of these techniques are hindered by coal mine underground's high background noise and complex environment. Considering that workers distinguish coal and gangue by hearing the impact sounds on the hydraulic support, we proposed a novel feature extraction method based on an auditory nerve (AN) response model simulating the human auditory system. Firstly, vibration signals were measured by an acceleration sensor mounted on the back of the hydraulic support's tail beam, and then they were converted into acoustic pressure signals. Secondly, an AN response model of different characteristic frequencies was applied to process these signals, whose output constituted the auditory spectrum for feature extraction. Meanwhile, a feature selection method integrated with variance was used to reduce redundant information of the original features. Finally, a support vector machine was employed as the classifier model in this work. The proposed method was tested and evaluated on experimental datasets collected from the Tashan Coal Mine in China. In addition, its recognition accuracy was compared with other coal-gangue recognition methods based on commonly used features. The results show that our proposed method can reach a superior recognition accuracy of 99.23% and presents better generalization ability.  
top-coal curing; coal-gangue recognition; auditory nerve response; auditory spectrum; support vector machine +
Footnote †: journal: Applied Sciences
## 3 Results  
As a result of the conducted research, the biomechanical response of the connection between the superior sagittal sinus (SSS) and the bridging vein (BV) was determined. The response of the venous systems of a 28-year-old person and a 77-year-old person was compared. The results show higher values of bridging vein strains in the case of a 77-year-old person (Figure 9 and Table 2). The bridging veins were slightly deformed under the same mechanical loading in young adult. The lowest strain values were obtained in the  
Figure 8: Boundary conditions—linear and angular acceleration load of the skull centre of gravity (CoG) according to [PERSON] et al. test C755-T2 [37]—marker numbers depicted for the frontal, A (anterior), and rear columns, P (posterior), with its coordinates.  
occipital region of young adult. This explains why subdural haematomas are most often diagnosed in the frontoparietal region in younger people [38; 39].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**28-Year-Old Model** & **77-Year-Old Model** & **Effective Strain Criterion [-]** \\ \hline Frontal veins & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Bridging vein strain comparison for maximum values in each region for the 28-year-old and 77-year-old models.  
Figure 9: Maximum bridging vein strain for compared models.  
The brain tissue in the occipital region is the most densely packed, and there is the least space in the subdural space. Therefore, such large displacements of brain structures in this region in a 28-year-old person are not observed. This is the situation of so-called coup injury, i.e., the first contact of the brain with the inside of the skull. In a 28-year-old person, the greatest concentration of deformations was observed in the frontal-parietal region. For an older person, brain displacement is greater due to the shrinkage of the brain with age. Therefore, in the case of a 77-year-old person, damage to vascular structures in the occipital region may be observed. The study adopted mechanical parameters obtained from [PERSON]'s research [31]. The strain criterion for the destruction of bridging veins given by this author is 29.82 \(\pm\)13.26. Therefore, it can be concluded that with the same mechanical load, there was no damage to the bridging veins in a 28-year-old person. However, in an elderly person, the mentioned criterion was exceeded, which could cause a haemorrhage in the subdural space. Analysis of the results showed that the highest stress concentration occurs in the area of the junction of the superior sagittal sinus and the bridging veins (Table 2). Anatomically, this connection is called the outflow cuff segment. Clinical studies show that due to geometric conditions, this region is most frequently damaged [31]. It is worth highlighting that the anatomical structure strongly influences strain and stresses in BV.
## 4 Results Analysis
### Analysis of Calculation Results
#### 4.2.2 Medium Prevention and Controlled Zoning for Flash Floods  
The medium prevention and control zone for flash floods includes a total of 590 small watersheds, totaling 108,271.52 km\({}^{2}\). Among them, the medium prevention and control zones for flash floods in Kunming, Nujiang, and Xishuangbanna accounted for more than 40% of their respective planning areas, whereas the medium prevention and control zones in Chuxiong, Diqing, Pu'er, Yuxi, and Zhaotong accounted for more than 30% of their respective administrative areas. According to the analysis in Table 3, 60.19%, 66.12%, and 60.44% of the flash flood prevention and control villages in Diqing, Nujiang, and Xishuangbanna, respectively, had less flood prevention and control capacity than the 20-year return period. The results in Table 4 illustrate that the populations of the flash flood-dangerous areas in Diqing, Nujiang, and Xishuangbanna rank 16 th, 14 th, and 12 th in Yunnan, respectively, and the population in the at-risk areas is relatively small. However, the results in Table 3 demonstrate that among the analyzed and evaluated villages in Diqing, Nujiang, and Xishuangbanna, 60.19%, 66.12%, and 60.44% of the flash flood prevention and control villages had a current flood control capacity of less than once in 20 years, respectively. The sum of these two factors increased the area of the medium control zones in these three cities.  
Among the flash flood disaster prevention villages in Chuxiong, the proportion of villages with flood control abilities below the 20-year return period was 48.01% and the population in the flash flood danger area ranked eighth among all cities in Yunnan. A city with a similar situation to Chuxiong is Pu'er, where the number of villages with flood control capacity below the 20-year return period was 42.53% and the population in the flash flood danger area ranked 10 th among all cities in Yunnan. Under the combined effect of the village flood control capacity and the number of people in the flash flood danger area, the area of medium prevention and control zoning in these two cities is relatively large.
# A Critical Review of SCWG in the Context of Available Gasification Technologies for Plastic Waste
## 5 The Problem of Tar: Formation and Removal
The catalytic activity of olivine for tar elimination can be related to the magnesite (MgO) and iron oxide (Fe\({}_{2}\)O\({}_{3}\)) contents, where the latter is much higher in olivine than in dolomite [129]. Moreover, olivine is mechanically stronger than dolomite and for this reason generates fewer particulates in the gasification gas than dolomite. Alkali metals, such as lithium, sodium, potassium, and rubidium, have been widely used as catalysts in biomass gasification [130]. Despite the fact that these catalysts provide a considerable increase in initial reaction activity, they are susceptible to loss of activity at high temperature due to particle agglomeration [131]. Alternatively, transition metal-based catalysts, particularly nickel, are excellent for gasification due to their high activities in tar elimination and ability to improve producer gas quality. These types of catalysts can be used as the primary catalyst in the gasifiers but also in secondary methods in post gasification or post pyrolysis reactor [129].
###### Abstract  
In this study, the authors propose a method for evaluating the influence of ambient temperature (\(T_{\text{a}}\)) on the optical characteristics and power consumption of a LED lamp used for the automotive headlamp, which helps the designer to figure out the acceptable range of the ambient temperature for the LED lamps to work well in the practical case. The LED lamp was fixed on the test holder and placed in a fixed position in the air circulation temperature control oven to measure the illuminance, spectrum in three different directions, and power consumption under various \(T_{\text{a}}\). The experimental results indicate that \(T_{\text{a}}\) has little effect on the correlated color temperature (CCT), chromaticity coordinates, and angular distribution of the emitting light. In contrast, \(T_{\text{a}}\) has a significant effect on the lamp's resistance, thus affecting illuminance and power consumption. When the \(T_{\text{a}}\) increased from 30 \({}^{\circ}\)C to 60 \({}^{\circ}\)C, the illuminance of the low beam and high beam decreased on average by 16.6% and 21.7%, respectively, while the power consumption of the low beam and high beam decreased on average by 21.4% and 22.2%, respectively. The drop in the luminous flux output indicates the probability of originally qualified automotive LED headlamps becoming unqualified in high \(T_{\text{a}}\).  
headlamp; LED lamp; power consumption; thermal dissipation; color rendering Article
## 4 System Implementation
### CPS Twinning  
As already mentioned, at the core of the designed system, there is the concept of CPS and the exploitation of its _Digital Twin_ counterpart in (also virtually augmented) production scenarios. A tool can be any kind of manufacturing unit, an instrument, a device, a robot, a machine, a production line, a transport unit, or even a human worker. These tools are contained in (belong to) a CPS and the MESS Core takes care and manages the connections with all the CPSs. The MESS provides several ways to integrate CPSs and their tools into the system, according to the level of IT expertise and the communication control requested by their designers. Basically, it offers two interface solutions for this connection: by means of an externally served CPS (capable of one between OPC UA--the preferred solution--and HTTP/JSON equivalent service Application Programming Interface, in short API) or by the adoption of a lower-level wrapping Production Administration Shell (PAS) for CPS (part of this research work's contribution and described in detail in the following), respectively. The PAS represents the I4.0 compliant twinning counterpart of a generic production CPS, in which the latter is in control of the physical devices. The great advantage of this CPS Twinning solution is that, regardless of the specific method chosen by the designer for their CPS integration, once connected to the MESS Core, the overall system will seamlessly provide a standardized OPC UA-equivalent transformation of the CPS service and information model to the external world. It is worth recalling how the OPC UA is the technology sitting on top of the MESS (Core and CPS Twinning) standardization and semantic interoperability. It is the technological umbrella that is capable not only of transporting machine data but also semantically describing them in a machine-readable way. An example for the interoperability of a Twinned CPS is shown in Figure 5 through an external, commercial OPC UA client software.  
The externally served CPS solution provides the opportunity to the CPS designers to create their own CPS controller with one of the MESS supported communication methods. The advantage of this solution is that the CPS developer is in charge of the complete  
Figure 5: View of Twinned CPS instance via a commercial OPC UA client software.  
implementation and communication, either via an OPC-UA server or an HTTP client and server pair.  
The second solution is one of the peculiar outcomes of this research work and refers to the exploitation of the MESS PAS (details in the next section). Once its configuration is finalized, the PAS enables the construction of complex functionality thanks to the combination of the newly integrated tools' functions. The PAS internally models tools as devices, which can cover any kind of equipment, both individually or in a form of fleet (details in the demo use-case section).
## 5 Performance Evaluation
### Performance Analysis
#### 5.3.4 Impact of Packet Size  
In this set of experiments, we want to see the impact on the network performance while an increasing packet size is sent by the transmitting vehicle. Intuitively, since a larger packet constitutes more bits than a smaller packet, receiving a larger packet should improve the throughput performance. However, sending a larger packet may increase the overall latency due to a higher transmission time. Figure 7 shows the impact of packet size on different numerologies in terms of PIR, PRR, and throughput. Since increasing the packet size increases the packet processing time and packet transmission time, the overall PIR increases for the receiving vehicles. However, thanks to 5G NR V2X, with an increasing packet size, the vehicular network is still able to maintain high PRR, which results in higher throughput. Note that a bigger received packet (more bits) contributes more to achieving higher throughput than a smaller packet. Numerology-wise, there is no distinguished difference in the PIR result but, in the PRR and throughput results, \(\mu=2\) clearly outperforms \(\mu=1\) and \(\mu=0\).  
Figure 6: Impact of transmit power.  
Figure 7: \(Cont.\)
# Structure-Enhanced Prompt Learning for Graph-Based Code Vulnerability Detection
## 4 Experiments
### Feature Visualization  
#### 4.4.1 Graphon Visualization  
To demonstrate the effectiveness of prompts with task-related knowledge, we provide a graphical structure visualization using the existing dataset. In this study, the graphical structure is represented as a matrix, \(W=[w_{kk^{\prime}}]\in[0,1]^{K\times K}\), where each element \(W_{ij}\) denotes the probability of an edge existing between node \(i\) and node \(j\). In the visualization, a darker color indicates a higher probability of an edge being present at a given location.  
As shown in Figure 12, subfigures (a)-(f) compare structural patterns across the FFmpeg+Qemu, SVuID and Reveal datasets. Despite dataset differences, structural consistency is largely preserved, demonstrating that our graphon-based prompt generation captures class-specific topologies. Ablation results further confirm that removing graph-based prompts degrades classification performance, underscoring their contribution to model robustness.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{3}{c}{**FFmpeg+Qemu**} & \multicolumn{3}{c}{**SVuID**} & \multicolumn{3}{c}{**Reveal**} \\
**Metrics** & _Acc_ & _Pre_ & _Rec_ & _F1_ & _Acc_ & _Pre_ & _Rec_ & _F1_ & _Acc_ & _Pre_ & _Rec_ & _F1_ \\ \hline Devign & 56.89 & 52.50 & 64.67 & 57.95 & 73.57 & 9.72 & 50.31 & 16.29 & 87.49 & 31.55 & 36.65 & 33.91 \\ Reveal & 61.07 & 55.50 & **70.70** & 62.19 & 82.58 & 12.92 & 40.08 & 19.31 & 81.77 & 31.55 & 61.14 & 41.62 \\ \hline CodeBERT & 62.37 & 61.55 & 48.21 & 54.07 & 80.56 & 14.33 & 55.32 & 22.76 & 87.51 & 43.63 & 56.15 & 49.10 \\ CodeT5 & 63.36 & 58.65 & 68.61 & 63.24 & 78.73 & 14.32 & 62.36 & 23.30 & 89.53 & 51.15 & 54.51 & 52.78 \\ UnixCoder & **65.19** & 59.93 & 59.98 & 59.96 & 77.54 & 15.11 & 72.24 & 24.99 & 88.48 & 47.44 & **68.44** & 56.04 \\ EPVD & 63.03 & 59.32 & 62.15 & 60.70 & 76.75 & 14.26 & **69.58** & 23.67 & 88.87 & 48.60 & 63.93 & 55.22 \\ LineVul & 62.37 & 61.55 & 48.21 & 54.07 & 80.57 & 15.95 & 64.45 & 25.58 & 87.51 & 43.63 & 56.15 & 49.10 \\ \hline
**Ours** & 64.40 & **63.59** & 63.33 & **63.41** & **83.44** & **75.89** & 67.80 & **70.69** & **90.69** & **56.63** & 55.14 & **56.11** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison with state-of-the-art vulnerability detectors on datasets (metrics unit: %).
## 4 Experiments and Analysis  
[PERSON] and his cooperators proposed an RGB-NIR scene dataset [35], which contains 477 NIR images and 477 corresponding RGB images with image sizes of 1024 \(\times\) 680 pixels and this dataset was captured from nine categories of scenes. Thus, 763 pairs of NIR and RGB images were generated by splitting up images and then each image size is unified to 480 \(\times\) 480 pixels. As such, 57 pairs of images were randomly selected as the test set and the rest of the image pairs were used as the training set. It should be noted that image pairs in the dataset are correctly registered by using a global calibration method, so that a pixel-to-pixel correspondence is guaranteed for quantitative and qualitative evaluation.  
We train the network with an NVIDIA TITAN RTX GPU and use stochastic Adam optimizer, which prevents overfitting and leads to convergence faster [36]. Meanwhile, we save the optimal model within 300 epochs. The hyper-parameters were tuned during the training stage as follows: initial learning rate \(1\times 10^{-4}\), \(\beta 1=0.9\), \(\beta 2=0.999\), \(\epsilon=1\times 10^{-8}\), leak ReLU 0.2, batch size 16.
# Machine Learning Method for Prediction of Hearing Improvement After Stapedotomy
## 1 Introduction
The aim of this study was to optimize prediction accuracy of hearing recovery in order to provide sufficient support for the decision-making process. Supervised learning based on the surgeons' previous performance results in the generation of a bespoke prediction solution for future surgery candidates. Our approach allowed us to efficiently generate a postoperative audiogram estimate with readily available preoperative inputs. The impact of specific features was evaluated, and it was determined that preoperative bone conduction plays a significant role in achieving high accuracy.