esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Study  
### Model Design and Production  
The concrete box girder model, as illustrated in Figure 1a, was fabricated for in situ testing in the high-latitude region of northern Altay, Xinjiang, taking into consideration the structural characteristics of practical bridge designs. The dimensions of the box girder model are as follows: it measures 200 cm in length and 220 cm in height, with a top plate width of 400 cm and a bottom plate width of 220 cm. Both the top and bottom plates have a thickness of 28 cm, while the web thickness is 40 cm. To ensure natural ventilation at the bottom, the box girder is supported by four piers and placed outdoors. It is important to note that the longitudinal direction of the model aligns with north-south orientation [23]. The box girder model was designed based on actual bridge structures, considering the limited length of the model (200 cm) and its primary purpose in this study, which is to observe the temperature field distribution within the cross-section of the box girder. Therefore, no prestressed reinforcement has been incorporated within the model.  
After completing the fabrication of the model, thermal insulation and sealing treatment were applied to both ends of the beam to simulate negligible wind velocity conditions inside the bridge cavity under actual engineering conditions. The model utilized C55 concrete [24] with a water-cement ratio of 0.31, as well as an added water-reducing agent. Experimental tests revealed that at 7 days and 28 days, the compressive strengths of the standard cubic specimens were 59.8 MPa and 65.9 MPa, respectively.
## 2 Materials and Methods
### Data Processing and Modeling Methods  
#### 2.3.1 Spectra Data Processing  
When utilizing the hyperspectral system for coal imaging, the data will inevitably be affected by factors such as instrument noise, ambient stray light, and uneven coal particles size, which might reduce the accuracy and stability of models. Therefore, processing the spectral data to mitigate these interferences and enhance detection reliability is crucial. In this study, Savitzky-Golay (S-G) convolutional smoothing, first-order derivative (FD), and multiplicative scatter correction (MSC) were mainly used to process the spectra. The S-G can effectively eliminate the instrumental noise, correct the baseline drift, and reduce the interference of the background environment on the spectral data [38]. MSC can eliminate the effect of surface scattering caused by the uneven size of solid particles and the influence of reflective optical path changes on the spectrum, which has a better processing effect in some studies [39].
## 2 Materials and Methods  
### Improving the Resolution of Low Cost 3D Printers  
The resolution of 3D printers of the FDM type is conditioned by the manner of manufacturing the objects, by means of stacked layers of molten material. The molten PLA comes out of a nozzle that is normally 0.4 mm, which allows for obtaining a resolution on the x, y (width and length) of 0.4 mm (400 microns). The resolution of the z axis depends on the motors and screws used to move the nozzle on the z axis. Usually in these printers, the precision can range between a maximum of 0.05 mm and a minimum of 0.3 mm. This comes to give the height of each layer that is built, so the layers have a resolution of between 50-300 microns.  
DLP printers do not deposit a thread, but they manufacture by a light that passes through an LCD screen and hardens a photosensitive resin. In this case, the resolution of the x and y axes depends on the resolution of the screen. Low-cost 3D printers of this type usually have a resolution of 2560 \(\times\) 1440 pixels for a 5.5-inch screen (often referred to as 2K screens). This comes to give a detail of the axes x and y of 40 microns. The resolution of the z axis, as in the PLA printers, depends on the motors and screws used to move the printing table on the z axis. Most low-cost printers are usually between 35-50 microns (Table 1).  
Figure 1: Examples of figures made by PLA (polylactic acid) and resin molds.  
In order to increase these resolutions, it is proposed to create a printer that improves, on the one hand, the quality of the LCD panel, which influences the resolution X, Y, and on the other hand, the mechanical characteristics of motors and screws that directly influence the resolution on the z axis. In this research work, we intend to design a DLP printer that can have 30% more resolution in all three axes. This will be possible in the x and y axes, changing the LCD screen with a resolution of 2K for a screen with more resolution, in this case 3940 \(\times\) 2160 pixels (they are called 4K screens). This provides a resolution of 30 microns on the x and y axis. On the other hand, if a motor with a lower rotation step (0.9 degree Nema17 stepper motor) and a higher quality lead screw is selected (Ball screw), the layer height (z axis) can be improved to 20-25 microns. It is expected that with these resolutions, the problem of the laminar texture will be solved and parts smaller than two centimeters can be printed (Table 2).
## 3 An Improved TSA Based on Cosine Mutation and Adaptive Grouping
### Algorithmic Optimisation Strategy
#### 3.2.3 Cosine Mutation Strategy  
This paper, which was inspired by the literature [32], proposes a sinusoidal mutation strategy based on the cosine function to guide the current optimal agent to make a local jump in order to improve the ability of the agents after TSA grouping to jump out of the local optimal solution at a later stage. When dealing with the boarding gate assignment problem, it also prevents the TSA from being limited to the optimisation performance of one objective while ignoring the solving ability of the other objectives and it moves closer to the theoretical optimum. For the cosine function, its value on the range interval [0, 2\(\pi\)] is [\(-\)1, 1], which can make the grouped algorithm perform a small search for the current global optimum position. Thus, it can make the algorithm tend to search better for the optimum in a certain search space. The TSA position update formula with the introduction of the cosine mutation is as follows.  
\[P_{best}^{t+1}=\cos(2\pi\times rand(1,30))\cdot P_{best}^{t} \tag{26}\]  
\[P_{best}^{t}=\left\{\begin{array}{l}P_{best}^{t+1},if(P_{best}^{t})<T\\ P_{best}^{t},\textit{otherwise}\end{array}\right. \tag{27}\]  
where \(P_{best}\) is the global optimal position as shown in Equation (26) and Equation (27). If the adaptation of the agent after cosine sinusoid is better, the mutation result is selected. Conversely, the original position is chosen to be kept.
# (Bio)Tribocorrosion in Dental Implants: Principles and Techniques of Investigation
## 4 Dental Implant
### Literature Overview in Dental Tribocorrosion
#### 4.2.3 The Impact of Specific Surface Treatments  
An alternative approach could be driven by contact surface treatment. Regarding this, [PERSON] et al. in 2013 [78] stated that, by applying an anodizing treatment on titanium surfaces, a homogenous oxide film capable of protecting the sample was formed. From a chemical point of view, the anodizing was performed in an electrolyte containing \(\beta\)-glycerophosphate and calcium acetate. The latter, in terms of concentration, was evaluated as relevant for tribocorrosion behavior because of microstructural changes. The porous structure, obtained after the treatment, not only resulted in lower mechanical damage but also can be crucial for better osteointegration allowing, the bone to build inside the pores. In any case, to achieve complete acceptability, the modified implants should be inserted into real systems where, more likely, other aspects like bacterial attack (Streptococcus Mutans) or cellular response, adhesion and proliferation, should be also considered. [PERSON], in another article published in 2015 [79], confirmed the beneficial effects of calcium and phosphorus Ti-oxide films again for wear contrast and osteoblast interactions. The same was formulated by [PERSON] et al. [80] regarding the acid etching technique adopted for improving sample properties, by [PERSON] et al. [81] for the Selective Laser Melting Technique, which showed lower passive current density and by [PERSON] et al. [82] about the pulsed Plasma Nitriding Process and its direct consequences on the lower coefficient of friction, higher OCP and lower wear volume loss. It is almost clear that a direct correlation between sample treatment and wear effects does not occur as follows: Some conditions impact chemical wear, in correspondence to lower current values, others on mechanical wear and on the friction of the tribosystem. Moreover, the type of wear established (abrasion, adhesion, fretting, three-body) is also crucial for correct tribocorrosion estimation. Overall, the surface preparation, in terms of topography, roughness and coatings, becomes crucial for a long-life implant, precluding postoperative complications and expenses [83] since tribocorrosion was considered one of the most possible failure causes [84] since the long-term production of toxic ions and local/global inflammation in the oral cavity cumulate their impact on the implant and the surrounding bone, causing, in the worst cases, the failure.
## 4 Results and Discussion
### Kinetic Analysis
The lag phase is crucial as it characterizes the adaptation of yeast strains to the fermented media. Notably, the genetically modified strains (W_M4_533 and W_M4_558) \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \(E_{\alpha}\) (KJ/mol) & \\ \hline
**Yeast Strain** & **Lag Phase** & **Log Phase** & **Stationary Phase** \\ \hline W303 & 142.7 \(\pm\) 9.9 & 132.1 \(\pm\) 12.1 & 102.7 \(\pm\) 6.0 \\ W\_M4\_558 & 109.4 \(\pm\) 5.8 & 113.4 \(\pm\) 7.9 & 26.9 \(\pm\) 2.2 \\ W\_M4\_533 & 75.3 \(\pm\) 1.8 & 82.9 \(\pm\) 1.5 & 54.6 \(\pm\) 3.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Activation energies \(E_{\alpha}\) with their corresponding standard deviation (\(\sigma\)) for each phase of alcoholic fermentation for each yeast strain, computed from Arrhenius equation plots. exhibit smaller activation energies during the lag phase compared to the wild-type strain. This suggests that the genetic modifications have an impact on the yeast strains' ability to adapt to the fermented media during this phase. Moreover, in the two additional phases under consideration, the genetically modified strains exhibit diminished activation energies in comparison to the wild-type strain. This implies that the genetic modifications have a consistent effect of reducing activation energies in these phases, indicating potential improvements or alterations in the metabolic processes of the modified strains.
## 5 Results
### Application  
In this study, our objective was to establish a more robust turbulence identification model. To further evaluate the effectiveness and applicability of the model, we applied the proposed turbulence model to a new flight route to identify turbulence during flight: the QAR data from Jinan to Harbin, dated 10 July 2023.  
This flight route was entirely different from the two previously selected routes. Consistent with the previous data processing methods, an identical number of feature parameters composed the feature subset, as shown in Figure 12(a). The proposed turbulence identification model achieved an accuracy of 98%, remaining superior to other algorithmic models, and further enhancing the identification accuracy of the stable CatBoost model, as shown in Figure 12(b). The number of correctly labeled turbulence instances by the model, i.e., the true positives and true negatives were 7727, The generated TPR was 92% and the FPR was 0.03%, as shown in Figure 12(c). The proposed model could identify most of the turbulence occurrence times when EDR was greater than 0.1, as shown in Figure 12(d). Our approach further confirmed its effectiveness and general applicability in accurately identifying turbulence between different flight routes on new data. This suggests that the proposed model may be generally applicable to all flights, comparable to the EDR method in characterizing turbulence.  
Figure 13: Evaluation and process of turbulence identification by the model using QAR data from Jinan to Harbin. (**a**) Workflow of the identification process; (**b**) ROC curves of different algorithms for new data; (**c**) confusion matrix for new data; (**d**) identification results and EDR values for new data.
## 3 Discussion of the Experimental Results
### Damages Observed and Failure Mode  
Concerning the failure mode of the SSM specimens, shown in Figure 6, they were characterized by the concentration of the deformation in the middle-height of the panel, observing the first cracking (sometimes in the \(\frac{3}{4}\) or \(\frac{1}{4}\) of the panel height) and consequently the development of another one. Flexural failure was observed until the crushing of the bricks in the backside of the panel (without strengthening), as shown in Figure 6. It was observed in two specimens (specimen 1, Figure 6a and specimen 2, Figure 6b) shear failure, which justify the lower difference between the SSM group and SWM group results when compared with the differences of their meshes tensile strength. In the situations, in which the shear failure did not occur, and the specimen was in conditions of reaching higher OOP displacements.  
Concerning the SWM group, the failure mode was characterized by the development of many cracks along with the panel height and higher out-of-plane deformation along the bed joints alignment, shown in Figure 7a. However, in specimens 3 and 5 it was observed shear failure, which limited the conclusions from those specimens (see Figure 7b).  
From the obtained results, it is clear that an additional testing campaign needs to be performed in panels with larger height/thickness ratio. It also requires the clarification of the OOP behaviour of infill walls in its as-built condition.  
Figure 6: Damages observed (**a**) Specim 1 and (**b**) Specim 2.
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 3 Experiments and Results
### Analysis of Implementation Results
#### 3.2.2 Model Validation Based on Massachusetts Building Dataset  
The Massachusetts building dataset consists of 151 aerial images of the Boston area, each of 1500 \(\times\) 1500 pixels, covering an area of 2.25 square kilometers. In the Massachusetts building dataset, there are only two types: one is buildings, and the other is background. The whole dataset covers about 340 square kilometers [37]. The Massachusetts dataset is divided into 137 image training sets and 14 image verification sets. Compared with the LandCover dataset, the biggest feature of the Massachusetts building dataset is that the image pixels are only 1500 \(\times\) 1500, while the image pixels of the LandCover dataset are 9000 \(\times\) 9500 or 4200 \(\times\) 4700. In the landcover dataset, there are three categories: vegetation, construction crops, and water body. The pixels of the two datasets are quite different. The training and prediction effects of the analysis model on different pixels are of great significance for studying the extraction of buildings in remote sensing images. Due to the different geographical locations of Massachusetts and Poland, there are big differences in geographical features and architectural styles. Figure 7 shows the comparison of the two datasets on the topography, in which the residents' area, rural area, and seaside area in the two datasets are selected, respectively. Among them, the difference between the houses in residential areas is not great. In the rural area, Massachusetts has a higher terrain, not the flat terrain of Poland, which will also cause some interference to the feature extraction of the neural network. Poland, in the seaside area, is mainly composed of some factory buildings, which are scattered and can be easily distinguished. In contrast, the seaside in Massachusetts is mostly residential buildings, which are dense and difficult to partition.
## 2 Basic Theoretical Parameters of the FE-SEA Method
### Coupling Loss Factor  
#### 2.3.1 Coupling Loss Factor between Structures  
The coupling loss factor between structures represents the strength of coupling between different subsystems and represents the energy loss generated when energy is transferred in each subsystem. All subsystems transfer energy through the coupling effect of the system. When one subsystem is excited by the external system, the energy of the subsystem is transferred to the other subsystem through the coupling effect so as to achieve energy transfer and balance. The coupling effect of different subsystems is mainly investigated via the following methods: surface connection, point connection, and line connection.  
There is a wire connection mode between different thin plates, and the coupling loss factor can be expressed as:  
\[\eta_{jk}=\frac{lc_{g}}{\pi\omega A_{j}}\tau_{jk} \tag{23}\]  
where \(l\) is the plate connection between the curve length, \(c_{g}\) represents the velocity of the bending wave of the excitation plate, \(A_{j}\) represents the area of the subsystem \(j\), \(\omega\) represents the center frequency of the research frequency band, and \(\tau_{jk}\) represents the propagation coefficient of sound waves when they are transmitted from subsystem \(j\) to subsystem \(k\).
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 6 Simulation Results and Discussion
### Spatial Modulation  
Figure 3 shows the BER performance comparison of P-CSI with the RLS channel estimator using the different number of pilots for SM without Doppler-scaling factor over time-varying UWA-MIMO channel. The performance of RLS channel estimator using 2% of the transmitted information bits as a pilot is totally degraded as compared to higher-order pilot numbers. In Figure 3, the performance gap between the P-CSI and RLS channel estimator using 5% of pilots at BER \(10^{-2}\) is 1.1 dB. Therefore, a greater number of pilots are required to achieve the same BER performance as in the P-CSI. Furthermore, the performance gap between P-CSI and RLS channel estimator using 10% and 15% pilots is 0.18 dB and 0.09 dB at BER \(10^{-2}\). Figure 3 revealed the BER performance of the RLS channel estimator approaches to P-CSI by increasing the number of pilots.  
In Figure 4 shows the BER performance of SM by RLS estimator method using a different number of pilots compared with a P-CSI over time-varying UWA-MIMO channel with normalized 0.15 Doppler-scaling factor. In Figure 4 at BER \(10^{-2}\) P-CSI demonstrates 10 dB, 1.22 dB, 0.2 dB, and 0.11 dB better performance than RLS channel estimation using 2%, 5%, 10%, and 15% pilots, respectively. From Figures 3 and 4, it is concluded that the SNR gain difference using RLS channel estimation with different number of pilots compared with P-CSI is almost the same for both with and without Doppler-scaling factor scenario.  
Figure 3: BER performance of the 4 \(\times\) 4 underwater SM-MIMO without Doppler-scaling factor.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 5 Simulation Performance and Analysis  
### Dataset Description  
We construct a LEO mega-constellation with 1584 satellites deployed in 72 orbits on the NS 3.31 simulation platform to evaluate the effectiveness of the proposed method. The simulation platform is deployed on Ubuntu 20.04. Each satellite has four ISLs, including two inter-plane ISLs and two intra-plane ISLs. All ISLs are optical links, which mitigate the influence of interference. The up/downlink rates and bandwidths of the ISLs are both 5 Mbps [33]. The simulation time is 100 s per run. The other parameters of the constellation and network are shown in Table 3.  
According to the satellite network traffic forecasting model in Figure 8, the source satellites alternate between the sending/non-sending states according to the ON/OFF model to generate network traffic. After that, the traffic generated by terrestrial sources will be overlaid to the mega-constellation network through ground stations, obtaining the network traffic of corresponding satellites. The distribution of satellites and ISLs in the simulated constellation is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Orbital parameters} & Number of orbits & 72 \\  & Satellites per orbit & 22 \\  & Orbital altitude (km) & 550 \\  & Inclination (\(\circ\)) & 53.8 \\ \hline \multirow{4}{*}{Network parameters} & Buffer queue size & 100 \\  & Minimum elevation of & 30 \\ \cline{1-1}  & gateway (\(\circ\)) & 5 \\ \cline{1-1}  & Uplink/downlink rate (Mbps) & 5 \\ \cline{1-1}  & Inter-satellite link rate (Mbps) & 5 \\ \cline{1-1}  & Packet size (byte) & 512 \\ \hline \end{tabular}
\end{table}
Table 3: Parameter settings of the LEO mega-constellation network.  
Figure 9: Inter-satellite links of a LEO mega-constellation network.  
We set eight flows (source and destination pairs) distributed between 40\({}^{\circ}\)S and 60\({}^{\circ}\)N (most of the global population is distributed within this latitude). The specific sources and destinations of the constellation network traffic are demonstrated in Table 4. The network topology changes over time with the movement of satellites. During the experimental cycle, sampling is performed every minute; \(T\) = 1450 traffic data for the whole cycle are selected as the dataset.
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
## 3 Results and Discussion
### Water Absorption  
The durability of epoxy mortar is greatly influenced by water absorption. The important parameters that influence water absorption are the size and texture of the fillers. Porous fillers tend to have a higher water absorption, while the other waste fillers, i.e., steel slag, marble dust, and sand, fill the cavities in the epoxy mortar, leading to a reduction in water absorption. Figure 7 shows the water absorption of epoxy mortars for 3, 7, and 14 days. It can be noticed that the typical water absorption of the epoxy steel slag (ESS) is 0.03%, epoxy marble dust (EM) is 0.05%, and epoxy sand is 0.042%. The cement mortar (C) water absorption capacity decreases with respect to time. The water absorption value is mainly influenced by various factors including the type of cement, the curing standards, and the surrounding environment [37; 38]. The content of water absorption is very low in these epoxy mortars because the epoxy resin is a waterproofing material [16]. Water absorption in epoxy PVC (EP) waste mortar was greater than that in epoxy resin because due to the existence of pores in the epoxy PVC mortar. It has been observed that air voids in PVC mortar could be linked to hollow infill particles [39]. The existence of air cavities is due to the porous nature of EP which increases water absorption. Figure 7 shows the water absorption of epoxy PVC mortars was 11% for 3 days, 16% for 7 days, and 33% for 14 days. From these results, it is observed the water absorption in epoxy PVC mortar increased due to an increase in exposure to water duration.  
Figure 6: Density of cement mortar (C), epoxy PVC waste (EP), epoxy sand (ES), epoxy marble dust (EM), and epoxy steel slag (ESS). Data represents mean \(\pm\) standard deviation (indicated by vertical error bars), n = 3. Values with the different superscript letter (a, b, c, and d) are significantly different (\(p<0.05\)).