esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### EDOF CL Use Induced a Controlled Increase in High Order Aberrations (HOAs)  
With the use of the CL, a controlled increase in most aberrometric coefficients was observed, as shown in Table 4. Specifically, for 3 and 5 mm pupils, statistically significant changes were detected in ocular HOA root mean square (RMS), primary coma RMS, primary spherical aberration Zernike term and secondary astigmatism RMS (\(p\)\(\leq\) 0.005) with the CL wear. In contrast, no significant changes were detected in trefoil RMS (3 mm, \(p\) = 0.060; 5 mm, \(p\) = 0.146). The significant changes in ocular HOA RMS, primary coma RMS, primary spherical aberration Zernike term and secondary astigmatism RMS were detected on the first day of CL wear (\(p\)\(\leq\) 0.020), with no significant changes during the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Mean \(\pm\) SD**} & \multicolumn{1}{c}{**Pre-Fitting**} & \multicolumn{1}{c}{**30 Days of CL Use**} & \multicolumn{1}{c}{\(p\)**-Value**} \\ \multicolumn{2}{c}{**Median (Range)**} & & 8.53 \(\pm\) 0.46 & 8.61 \(\pm\) 0.57 & \\ \multirow{4}{*}{Far NFV (\(\Delta\))} & Break & 8.00 (6.00 to 12.00) & 8.00 (6.00 to 14.00) & 0.480 \\  & Recovery & 6.53 \(\pm\) 0.46 & 6.61 \(\pm\) 0.57 & \\ \multirow{4}{*}{Far NFV (\(\Delta\))} & Blur & 12.13 \(\pm\) 1.73 & 12.46 \(\pm\) 1.86 & \\  & 12.00 (0.00 to 12.00) & 14.00 (0.00 to 20.00) & 0.595 \\ \multirow{4}{*}{Far PFV (\(\Delta\))} & Break & 20.60 \(\pm\) 1.94 & 20.54 \(\pm\) 1.72 & \\  & Recovery & 20.00 (6.00 to 35.00) & 24.00 (10.00 to 30.00) & 0.888 \\ \multirow{4}{*}{Near NFV (\(\Delta\))} & Blur & 15.73 \(\pm\) 1.25 & 17.70 \(\pm\) 2.00 & \\  & 18.00 (4.00 to 20.00) & 18.00 (8.00 to 35.00) & 0.292 \\ \hline \multirow{4}{*}{Near NFV (\(\Delta\))} & Blur & 8.23 \(\pm\) 1.35 & 9.08 \(\pm\) 1.70 & \\  & 8.00 (0.00 to 16.00) & 10.00 (0.00 to 18.00) & 0.531 \\ \multirow{4}{*}{Near NFV (\(\Delta\))} & Break & 13.73 \(\pm\) 0.91 & 16.38 \(\pm\) 1.26 & \\  & 12.00 (8.00 to 20.00) & 16.00 (10.00 to 25.00) & 0.106 \\ \multirow{4}{*}{Recovery} & 11.47 \(\pm\) 0.74 & 13.23 \(\pm\) 1.29 & \\  & 10.00 (6.00 to 16.00) & 14.00 (4.00 to 20.00) & 0.124 \\ \hline \multirow{4}{*}{Near PFV (\(\Delta\))} & Blur & 24.93 \(\pm\) 4.17 & 31.00 \(\pm\) 4.27 & \\  & 20.00 (0.00 to 40.00) & 40.00 (0.00 to 40.00) & 0.066 \\ \multirow{4}{*}{Near PFV (\(\Delta\))} & Break & 34.20 \(\pm\) 1.96 & 36.92 \(\pm\) 1.65 & \\  & 40.00 (18.00 to 40.00) & 40.00 (25.00 to 40.00) & 0.066 \\ \multirow{4}{*}{Near PFV (\(\Delta\))} & Recovery & 31.60 \(\pm\) 2.46 & 36.15 \(\pm\) 2.05 & \\ \cline{1-1}  & 35.00 (14.00 to 40.00) & 40.00 (20.00 to 40.00) & 0.066 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fusional vergence data at a distance and near obtained before the fitting and 30 days after contact lens wear. Abbreviations: SD, standard deviation; CL, contact lens; NFV, negative fusional vergence; PFV, positive fusional vergence.  
remaining post-fitting follow-up visits (\(p\geq 0.120\)), except for the change in secondary astigmatism RMS between the 1-day and 15-day CL wear visits (\(p<0.001\)).
## 4 Simulations
### Results
#### 4.3.3 Comparison of Routing Algorithms  
This simulation is based on several contact tracks to evaluate our proposed ECRF by comparing with several classical routing algorithms. From Tables 1 and 2, it can be seen that ECRF had the best performance among the routing schemes in different situations. In simulation of Infocom 2006, compared to S-W, SimBet, ESR, and FCNS, ECRF is, respectively, 47%, 30%, 1.4%, and 2.3% higher in the delivery ratio, 38%, 27%, 7.5%, and 8.1% lower in the delivery delay, and 15.8%, 7.7%, 0.8%, and 1% lower in the number of forwarding. In simulation of synthetic trace, ECRF is, respectively, 5.4%, 3.4%, 1.8%, and 2.3% higher in the delivery ratio, 9.4%, 3.6%, 0.9%, and 1.1% lower in the delivery delay, and 17%, 9.1%, 2.9%, and 4% lower in the number of forwarding. Obliviously, the community structure and message ferry can remarkably improve the routing performance in OppNets for mHealth.
## 4 Discussion  
As demonstrated in the previous sections, the proposed DKU-Net provides a novel approach to segmenting intricate shadow details, which is particularly valuable in remote sensing images. Shadows in these images often vary in shape, size, and texture within the same region, making accurate segmentation challenging. The results, as illustrated in Figure 11, showcase how DKU-Net shows notable performance in identifying these fine details, resulting in improved segmentation quality.  
One notable advantage of DKU-Net is its ability to detect real shadows that are not included in the ground truth. This is illustrated in Figure 12, where the model successfully identifies shadowed areas omitted by the annotated dataset. This highlights the method's capability to generalize beyond the training data, offering an improvement in shadow detection. Furthermore, the architecture of DKU-Net incorporates dynamic kernel adjustment, combined loss weighting, and optimizer switching, which make the model adaptable to a wide range of applications. These features not only enhance segmentation accuracy but also reduce training time despite the added architectural complexity. The model demonstrated optimal performance in as few as 60-75 epochs, significantly accelerating the training process.  
Despite its strengths, DKU-Net has limitations. The intricate detail extraction sometimes results in over-segmentation. Additionally, the model's effectiveness diminishes in close-range images, which often lack the context provided by remote sensing. Moreover, exceeding the optimal number of training epochs can lead to overfitting, further reducing generalization. In conclusion, while DKU-Net represents a significant advancement in shadow segmentation for remote sensing, future work should focus on addressing these limitations, and exploring strategies to mitigate over-segmentation and improve performance on close-range images.  
Figure 11: Achieved result obtained through the SDKU-Net segmentation in complex areas, showcasing its ability to capture intricate shadow details.
## 3 Results and Discussion
### Damage Severity Identification of Hinge Joint Based on Artificial Bee Colony  
#### 3.2.1 Damage Identification Process  
Damage identification is formulated as an optimization problem in the ABC algorithm. By this way, Euclidean distance between the actual measured LLD influence line and the result calculated by MHPM is defined as the objective function, which is expressed in Equation (19). Meanwhile, the function can be used to evaluate the error of damage identification. The smaller the objective function value is, the more precise the damage identification result is. In consequence, the error goal was set to be \(10^{-20}\). If the error of damage identification result was less than the error goal, the algorithm will be terminated immediately; otherwise, it runs until the maximum number of iterations. To be specific, the flowchart of damage identification for a hinged-slab bridge is shown in Figure 11.  
\[f=\sqrt{\sum_{i=1}^{n}\left(\eta_{i,jI}-\eta_{i,jA}\right)^{2}}, \tag{19}\]  
where \(\eta_{i,jI}\) and \(\eta_{i,jA}\) represent the identified and actual \(i\)th vertical value of LLD influence line of slab \(j\), respectively.  
Figure 11: The flowchart of damage identification of hinge joint damage based on the artificial bee colony (ABC) algorithm.  
In ABC, ABCG, ABCLGII, and HABCDE algorithms, basic parameters are chosen to be the same, so that a fair comparison can be made. Damage identification accuracy will be improved with the increase of bee colony size, but it will also greatly increase the time cost of the calculation process [28; 29]. According to the literature [21; 33; 34; 35], the bee colony size \(BN\), which is equal to the number of food sources, is set to be 50 in all calculations. The control parameter _limit_ is _limit_ = \(BN\)\(\times\)\(D\)[29], where D is introduced in Section 3.1. And the maximum number of iterations is set to be 500. As for the parameters of GA and PSO, the crossover and mutation probabilities of GA are 0.7 and 0.05. The inertia weight and two acceleration coefficients in PSO are 0.5, 1.5, and 2.5, respectively. The specific theory and process of GA and PSO can be seen in References [43; 44].
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 4 Photonic Applications Based on 2D Bismuth  
Bismuthene has been applied in diverse photonic applications because of its narrow bandgaps, high carrier mobility, strong light response, and fine biocompatibility [4; 24]. In this section, we provide a comprehensive overview regarding nonlinear photonic devices, photodetection devices, and photocatalytic applications based on 2D bismuth. The architecture, performance, and mechanisms of the devices will be illustrated and discussed in detail.
## 2 Materials and Methods
### Biospeckle Contrast and Optical Coherent Tomography (OCT) Experimental Setup  
In this study, biospeckle is a very important phenomenon. When a biological object is exposed to coherent light, such as a laser light, the light that is scattered by the object interferes to create a phenomenon known as biospeckles [17]. Due to the continual movement within a biological object caused by biological processes, such as cytoplasmic streaming, organelle migration, cell development, cell division, and biochemical reactions, a dynamic speckle pattern can be seen [18]. However, the intensity of the speckle pattern remains constant throughout time for a static object. As a result, the magnitude of change in a speckle pattern can be used to observe interior biological activity.  
The temporal variation of the obtained OCT structural images under the exposure of different alumina exposure during different time intervals were evaluated using a measure called biospeckle contrast (\(\gamma\)). The biospeckle contrast was defined as the ratio of the standard deviation of the intensity to the mean value at each pixel along the temporal axis. Equation (1) can be used to calculate this biospeckle contrast:  
\[\gamma\ (x,y)=\frac{1}{<I_{OCT}(x,y)>}\bigg{[}\frac{1}{N}\sum_{j=1}^{N}\{I_{OCT }\big{(}x,y;\ t_{j}\big{)}-<I_{OCT}(x,y)>\}^{2}\bigg{]}^{\frac{1}{2}}, \tag{1}\]  
\[<I_{OCT}(x,y)>=\frac{1}{N}\sum_{j=1}^{N}I_{OCT}\big{(}x,y;\ t_{j}\big{)}\]  
where \(j\) is the scan number, \(x\) and \(y\) are the coordinates of the pixels, and \(N\) is the total number of scans. It can be seen that the seeds have undergone considerable variations over time by looking for greater temporal variation or speckle contrast. As a result, alterations in speckle contrast magnitude may be a useful measure of seed biological activity when exposed to external stimuli [12; 13].  
A schematic diagram of the customized, fiber-based spectral domain OCT system developed in an optical bench is shown in Figure 1. This system was used to observe the internal biological activity of biological objects after just a few hours of exposure to varyingtreatments and environmental conditions. For the scope of this paper, a brief description of the system is given, and a detailed explanation of the experimental system and procedures can be found in our previous studies [12; 13; 15].
# A Low-Autogenous-Shrinkage Alkali-Activated Slag and Fly Ash Concrete
## 3 Results and Discussion  
### Autogenous Shrinkage of Paste  
The autogenous shrinkage curves of the paste mixtures are shown in Figure 3. The autogenous shrinkage of AASF paste reached more than 2000 um/m at 1 day and around 4000 um/m at the age of 7 days. This magnitude is higher than the autogenous shrinkage of common OPC-based systems irrespective of the presence of supplementary materials [29; 30]. The shrinkage mechanism was discussed in a previous study [19]. It can be seen from Figure 3 that both the additions of SAPs and MK resulted in lower autogenous shrinkage of AASF paste. In particular, the addition of SAPs greatly mitigate the autogenous shrinkage of AASF paste after the first day. By contrast, MK was more effective on the first day; afterward, the effect of MK became less evident.  
When SAPs and MK were added together into AASF, the autogenous shrinkage of the paste was the lowest among all the four mixtures in the whole week. The mitigating effect of the combination of SAPs and MK was more evident than when they were applied individually. Both the early-age and later-age autogenous shrinkage were significantly mitigated compared to those of the plain AASF paste. For example, the 1-day and 7-day autogenous shrinkage of AASFICMK paste was only 30% and 40% of that of AASF paste, respectively. This result indicates that SAPs and MK complement each other in mitigating the autogenous shrinkage of AASF.
# Online Monitoring of Torpedo Car Shells Based on Equal Angle Scanning
## 2 Theory and Method
### System Design  
As is shown in Figure 1, we design a high-speed line scan infrared temperature measurement system, which uses synchronous motor scanning and real-time image processing to realize the reconstruction and visualization of the two-dimensional temperature field of the moving target. It is composed of a refrigerated infrared temperature measurement module and a DC brushless motor synchronous scanning module, high-speed signal acquisition and processing module, computer real-time image splicing, and other technical components. The infrared sensor mainly receives the radiant energy emitted by the measured object, focuses it on the detector through the receiving lens, completes the conversion of the optical signal to the electrical signal, and then obtains the voltage value by low noise amplifier, filtering, and analog-to-digital conversion.  
In Figure 2, the SAR ADC sampling module obtains the voltage signal of the infrared detector [\(-5\) V, \(+5\) V] and converts it into absolute voltage in the range of [0-5].  
\[\mathrm{V_{o}}=\frac{\mathrm{V_{in}}+5}{2}-\mathrm{V_{ref}}=\mathrm{adc_{\_}data }-\mathrm{V_{ref}},\mathrm{Vin}\in[-5,+5] \tag{4}\]  
where \(\mathrm{V_{o}}\) is the digital output voltage, \(\mathrm{V_{in}}\) is the AD sampling output voltage, and \(\mathrm{V_{ref}}\) is the reference voltage of ADC.  
Figure 2: ADC sample based on equal angle scanning.  
Figure 3a is the proposed infrared scanner based on equal angle scanning. As is shown in Figure 3b, in order to compensate for the influence of background radiation, we carry out real-time temperature correction through background sampling. The key technical index is listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline wavelength range & 3–5 \(\mathrm{\SIUnitSymbolMicro m}\) \\ FOV & 180\({}^{\circ}\) \\ focal length & 100 mm \\ pixel number/line & 2048 \\ sampling rate & 500 kSPS \\ scan speed & 10–100 Hz \\ precision & \(\pm 1\)\({}^{\circ}\)C \\ spatial resolution & 2.5 mrad \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key technical index of infrared scanner.  
Figure 1: System block.
## 3 Numerical Model  
### Establishment of a 3D Numerical Model  
Given that the influence range of the excavation stress was 3-5 times that of the tunnel radius, the boundary dimensions of the model were 200 m in the X direction, 110 m in the Z direction, and 160 m in the Y direction. The buried depth of the tunnel was 100 and 60 m away from the bottom boundary. The numerical model was established using the software Abaqus. The stratum, tunnel, primary lining, secondary lining, and temporary support were simulated using the eight-node reduced integration solid element which was defined as C3D8R in Abaqus. The thickness of the primary lining solid unit was 20 cm, the temporary support was mainly carried by I-steel, the thickness of the solid unit was 18 cm, and the thickness of the secondary lining solid unit was 40 cm. The two tunnels were mutually angled, the main section was along the \(Z\)-axis direction, and the ramp section was along the \(Z\)-axis horizontal with an angle of 8.28\({}^{\circ}\). The minimum clear distance in the model was 1.6 m. The two tunnels, from a small clean distance, gradually transition to a separate tunnel. The model was divided by a hexahedral mesh, and local mesh encryption was carried out for important positions, such as the tunnel excavation section and surrounding rock. The model was divided by a hexahedral grid, and the local grid of the tunnel excavation section was densified. With the double wall heading method and CD method as examples, the overall numerical model was built and is shown in Figure 3a. A total of 117,355 units were generated by the model, among which the solid element grids, such as excavated rock mass, primary lining, secondary lining, and temporary support, are shown in Figure 3b. The front, back, two sides, and bottom surfaces of the model were constrained by normal displacement, and the front excavation face and top surface were not constrained.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{2}{c}{**Name of the Supporting**} & \multicolumn{1}{c}{**Support Method**} \\ \hline \multicolumn{2}{c}{Ramp section} \\ \hline \multirow{2}{*}{Primary lining support} & Concrete spraying layer & C25 shoterete 20 cm \\  & Rock bolt & \(\Phi\)22 mortar bolt L2500 cm \\  & Reinforcing mesh & \(\Phi\)8 reinforcing mesh 25 cm \(\times\) 25 cm \\ \hline \multirow{2}{*}{ Temporary support} & I-steel truss & I18 I steel frame, longitudinal spacing 50 cm \\  & Concrete spray layer & C25 concrete spray layer 22 cm \\  & Reinforcing mesh & \(\Phi\)8 reinforcing mesh 20 cm \(\times\) 20 cm \\ \hline \multirow{2}{*}{Secondary lining support} & Arch wall & C30 Waterproof reinforced concrete 40 cm thick \\  & Inverted arch & C30 Waterproof reinforced concrete 40 cm thick \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: Numerical calculation model: (**a**) Overall numerical model; (**b**) Tunnel lining.
## 2 Related Works
### Lossless Compression Algorithms  
While lossy compression algorithms for capsule endoscopy are in abundance, their lossless counterpart is only a few. In [20], our group proposed a lossless image compressor based on YUV color space. However, YUV color space is computationally expensive due to the presence of floating point numbers during conversion from RGB and it consumes significant area and power when implemented in hardware. Besides, the practical limitation of the work is that the architecture assumed that the image sensor must have built-in RGB to YUV color space converter which limits the robustness. Moreover, the performance was validated using only image simulation without any deployment into hardware platform or any capsule endoscopic system. Another choice for lossless compression is to use the JPEG-LS algorithm [21, 22, 23]. No work was found that uses JPEG-LS for capsule endoscopy application; as a result, we have implemented it and applied to our dataset. The results are added later in this paper. Lastly, the work in [24] proposed a modified JPEG-LS algorithm for endoscopic image compression which is not entirely lossless. The work provides simulation results only without any _in-vivo_ trial for performance validation.  
In contrast, here the proposed lossless compressor can work with any elementary RGB based image sensor that outputs image pixels in raster scan fashion which eliminates the need of a memory buffer. The compressor consists of a low complexity color space, known as YEF, designed by analyzing the unique properties of endoscopic images [5]. As the compressor is lossless, it produces reconstructed images without any distortion and thereby reduces the possibility for inaccurate diagnostics. The performance is validated using a complete CE system developed in our lab with _ex-vivo_ trials with live pig.
## 2 Denoising Autoencoder Based Noise Reduction
### Effect of Depth (Layer by Layer Pre-Training)  
A deep neural network is usually more potent than a shallow neural network in order to reconstruct the nonlinear relationship between the input and output. In most deep learning studies, the general conclusion is that increasing the depth of the neural network pattern always helps performance either for pattern classifications or for encoding. Similarly, we increased the system's depth by stacking several AEs from a DAE and carrying out speech denoising experiments. However, with the number of hidden layers, the training of the network becomes more difficult due to the local minima. Therefore, as a way of overcoming this issue, layer by layer pre-training is adopted [24], which is an unsupervised learning process (Figure 3). The initial values of the weight matrix and the deviation vector are pre-trained and computed layer by layer where the input layer and the first hidden layer consider as the DAE and training. Then, the first hidden layer is considered as the input layer for the second DAE, which constructs the second DAE with the second hidden layer and so on, until the \(i\)-\(th\) DAE is trained. After the neural network training is completed, the weight matrix and deviation vector of a single neural network are taken to be the initial value of DDAE, and the calculated value of the DDAE sequence (fine-tuning) [25], respectively. More details can be found in [24].  
Figure 3: Layer-by-layer pre-training method for the composite-DDAEs. Each layer is pre-trained for the next layer and provides it with some of the initial weights.