esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Dataset  
[PERSON] et al. [3] released a fundus image dataset called DDR (dataset of diabetic retinopathy) for diabetic retinopathy screening. We used the DDR as our dataset. The DDR is an image dataset, consisting of 13,673 samples divided into six classes, including no diabetic retinopathy, mild diabetic retinopathy, moderate diabetic retinopathy, severe diabetic retinopathy, proliferative diabetic retinopathy, and ungradable data. In our research, we discarded the ungradable samples and merged all the diabetic retinopathy samples into one class to obtain a balanced dataset. Finally, the dataset consisted of 6263 samples without diabetic retinopathy and 6256 samples with diabetic retinopathy. The dataset was categorized into a training set, validation set, and testing set at a ratio of 5:2:3.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Bibliography** & **Task** & **Dataset** & **Model** \\ \hline
[26] & Binary Classification & Kaggle & CNN \\ \hline
[27] & Binary Classification & Private data & Inception, ResNet \\ \hline
[18] & Binary Classification & Private data &
\begin{tabular}{c} WP-CNN (weighted path \\ convolutional neural network) \\ \end{tabular} \\ \hline
[23] & Binary Classification & Kaggle & Zoom-in-Net \\ \hline
[12] & Multiple Classification & Kaggle & BiRA-Net (bilinear attention net) \\ \hline
[28] & Multiple Classification & Kaggle & AlexNet, VggNet, GoogleNet, ResNet \\ \hline
[24] & Multiple Classification & IDRiD & CANet (cross-disease attention network) \\ \hline
[25] & Multiple Classification & DDR & CABNet (category attention block net) \\ \hline
[3] & Multiple Classification & DDR &
\begin{tabular}{c} VGG-16, ResNet-18, GoogLeNet, DenseNet-121, \\ SE-BN-Inception \\ \end{tabular} \\ \hline
[19] & Multiple Classification & Kaggle & DenseNets \\ \hline
[20] & Multiple Classification & Private data & AlexNet, GoogLeNet, Inception, ResNet \\ \hline
[21] & Multiple Classification & EyePACS & Inception V3 \\ \hline
[22] & Multiple Classification & Kaggle & VGG-16 \\ \hline \end{tabular}
\end{table}
Table 1: Related work. Binary classification includes a normal class (fundus images without diabetic retinopathy) and an abnormal class (fundus images with diabetic retinopathy). Multiple classification includes five classes (normal, mild, moderate, severe, and proliferative diabetic retinopathy).  
Data Preprocessing  
The image sizes of the raw data ranged from 5184p \(\times\) 3456p to 702p \(\times\) 717p. To fit the model, all images were resized to 224p \(\times\) 224p. Several commonly used data augmentation methods were applied to the original data, including random horizontal flip, random vertical flip, random rotation, and color jitter. To obtain the robustness of the supervised contrastive learning, different combinations of the above data augmentation methods were tested.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 3 Metal Selenide Thin Films
### Bismuth Selenide Films  
The growth of bismuth selenide (Bi\({}_{2}\)Se\({}_{3}\)) films onto fluorine-doped tin oxide (FTO) glass [98] at 27 \({}^{\circ}\)C (Immersion time = 50 s, rinsing time = 30 s, 150 deposition cycles). Experimental results showed n-type semiconductor and film thickness was 0.92 \(\upmu\)m. The current voltage (I-V) characteristics for n-Bi\({}_{2}\)Se\({}_{3}\)/polysulfide photoelectrochemical cell in the dark (poor rectification) and illumination conditions (moved to the fourth quadrant, indicated photoactive) were described (Figure 5). The conversion efficiency (0.032%) and fill factor were very small values because of low shunt resistance. The single crystalline wafer of Si (111) and glass substrate were used to produce thin films as stated by [PERSON] and co-workers [99]. Film thickness was 1.2 \(\upmu\)m and presented homogeneous morphology. Bismuth nitrate, sodium selenosulfate, and the TEA (complexing agent) were used to prepare thin films at 300 K, (immersion time was 60 s, rinsing time was 40 s) [100]. The obtained materials could be used in solar energy-related applications.
## 2 Research Background
### The Technology Acceptance Model
The validity of the TAM has been confirmed not only in information systems applications but also to assess the adoption of wearable technologies [30], health [31], energy services [32], financial services [33], and e-learning [34], among many others. [PERSON] et al. [35] pointed out the complexity of e-learning systems due to the diversified nature of projects, the stakeholders, and system features and highlighted the importance of specific methodological approaches for an accurate design, development, and assessment. This perspective is very pertinent considering the intricate context of the e-learning platform FARMER 4.0 and the associated _learning by doing_ training model in a relatively low technological sector like farming. For this reason, [PERSON] et al. [18] conducted a literature review on TAM applications in the three sectors that converge in the e-learning tool--agriculture, e-learning, and virtual reality--aiming at identifying the external variables to include in a TAM extension to assess the adoption of FARMER 4.0. Using the Quality Function Deployment (QFD) as organizing framework, the authors identified the research problematics and the most commonly used external variables included to assess technology acceptance. One of the research categories and the one that fits the context of FARMER 4.0 is _\"Technology acceptance when the technology is a platform or system\"_. Findings suggest that the most common external variables included in this type of TAM extensions were anxiety, content quality, experience, facilitating conditions, individual innovativeness, perceived enjoyment, self-efficacy, and social norm. Table 1 presents the definitions of these variables. The TAM extension proposed by [PERSON] et al. [18] and later validated in the present study, is shown in Figure 4.  
Figure 3: Technology Acceptance Model.
## 5 Discussion
### Geo-Stress Factors  
With the depth of underground engineering increasing, the geo-stress levels are also increasing rapidly [31; 32; 33; 34]. The geo-stress is an unignored factor affecting the occurrence of rockbursts, especially for non-coal rock engineering. As shown in Table 5, whether in a deep coal mine or in a shallow coal mine, the geo-stress values all exceed the uniaxial compressive strength of coals, and rockbursts occur in coal mines with buried depths of 230-1089 m, while in non-coal rock engineering, the non-coal rocks with a high uniaxial compressive strength do not experience rockbursts under low geo-stress levels. With the increase of the geo-stress levels, rockbursts occur in non-coal rock engineering, but the geo-stress values do not exceed the uniaxial compressive strength of non-coal rocks. In the Jinping II Hydropower Station, a rockburst did not occur in the marble with \(\sigma_{c}\) of 44.6-150.6 MPa under geo-stress of 11.2-22.3 MPa, and a rockburst occurred in the same marble under a geo-stress of 38.6-44.8 MPa, so the influence of geo-stress on rockbursts in non-coal rocks is worthy of being studied in depth.
# Analytic Hierarchy Process and Multilayer Network-Based Method for Assembly Line Balancing
## 3 Simulated Annealing-Based Line-Balancing Optimization
### Handling Precedence Constraints  
In addition to these three objectives of the simulated production line, a so-called soft limit is also defined, which is the amount of the unaccomplished precedence of the activities (\(A^{\prime}\)). This limitation of the order with regard to the activities is stored in the multilayer network.  
The completion of a task is a precondition for the start of another because tasks depend on other tasks. The \(\pi\) sequence has some constraining condition and cannot be entirely arbitrary. The precedence graph is used to represent these dependencies in SALBP [1,35,36]. Figure 4 shows a problem from a well-known example by [PERSON] [37] with \(N_{a}=11\) tasks, where task 7 requires tasks 3-5 to be completed directly (direct predecessor) and task 1 indirectly (indirect predecessor). The precedence graph can be described by matrix \(A^{\prime}(i,j)\), \(i,j=1,2,\ldots,N_{a}\), where \(A^{\prime}(i,j)=1\) if task \(i\) is the direct predecessor of task \(j\), otherwise, it is 0 [32]. The precedence graph is partially ordered if tasks cannot be performed in parallel. It must be determined whether a permutation \(\pi=(\pi_{1},\pi_{2},\ldots,\pi_{N_{a}})\) is feasible or not according to the precedence constraint.  
Based on the transitive closure \(A^{*}\) of \(A^{\prime}\), \(\pi\) is feasible if \(A^{*}(p_{j},p_{i})=0,\forall i,j,i<j\); otherwise, \(\pi\) is infeasible [32]. A sub-sequence \((\pi_{i},\pi_{i+1},\ldots,\pi_{j})\), where \(i<j\) of \(\pi\), can be defined by \(\pi_{(i)j}\). For example, a feasible sequence \(\pi\) of the precedence graph in Figure 4 is \(\pi=(1,4,3,2,5,7,6,8,9,10,11)\) and \(\pi_{(2:4)}=(4,3,2)\) is a sub-sequence of \(\pi\).  
As will be presented in the next subsection, the key idea of the algorithm is that it determines the interchangeable sets of activity pairs and uses these in the guided simulated annealing optimisation.  
Figure 4: Precedence graph of the example problem taken from Jackson [32,37].  
Figure 3: Illustration of the sequencing method. The activities are separated into the different groups of activities that are assigned to different operators.
## 4 Parametric Study
### Installation Depth of the SFT  
In this section, the installation depth of the SFT was set to 40 m, 70 m and 100 m from the sea level to analyze the effect on the behavior of the SFT. The common variables of the analytical model included a wave condition of 6 m (8 s), a BWR of 1.2, an inclination angle of the main cables of 12 degrees and a tube external diameter of 23 m.  
Figure 17a illustrates the absolute maximum motion of the SFT, according to the installation depth (draft). The deeper the installation depth, the less the influence of waves, and the less the motion of the SFT. Compared with the sway motion, Type 1 and Type 2 had a similar behavior at a depth of 40 m, and the motion of Type 2 was reduced above a depth of 70 m, compared to that at 40 m. The cause of this can be determined by analyzing the heave motion of Type 2, as shown in Figure 17b. In Figure 17b, it can be seen that slack occurred at an installation depth of 40 m, and this phenomenon can greatly affect the acceleration, section force, reaction force, and so forth.  
Figure 16: Behavior of the SFT, according to the diameter of the SFT: (**a**) maximum motion of the SFT; (**b**) maximum acceleration of the SFT; (**c**) maximum reaction force on the foundation of the SFT; (**d**) maximum section force of the SFT; (**e**) maximum section moment of the SFT.  
Figure 17c expresses the absolute maximum acceleration of the SFT model, according to the motion with drafts. It can be seen that the acceleration in the buoyancy acting direction (z-axis) at a depth of 40 m was 6 m/s\({}^{2}\) due to the slack, but it decreased rapidly above a depth of 70 m. A directional acceleration of 70 m and an installation depth of 100 m were shown to have nearly similar values.  
Figure 17d displays the absolute maximum reaction force on the foundation of the SFT, according to the draft. It can be seen that the reaction force of Type 2 at a depth of 40 m was 240 MN, which is larger than that of Type 1, with 170 MN. However, at a depth of 70 m, the force of Type 2 was much lower than that of Type 1. It can be seen that Type 2 was more affected by the installation depth.  
Figure 17e,f exhibit the maximum section force and section moment of the SFT model, according to the draft. In the case of the maximum section force, the shear force in the wave acting direction (SFy) was the largest. Moreover, the deeper the installation depth, the less the axial force and the directional shear forces. It can be found that Type 2 was more susceptible to the depth due to slack, as shown in Figure 17b. In the case of the maximum section moment, it can be seen that the torsion was dominant, as in the case of other variables. Since the section moment of Type 2, where slack occurred, rapidly decreased, it can be seen that it is necessary to prevent the slack.  
Figure 17: _Cont._
## 2 Spectral UAV Sensors
### Point Spectrometers  
Point spectrometers (referred to as spectroradiometers if they are spectrally and radiometrically calibrated) capture individual spectral signatures of objects. The spectrometer's field of view (FOV) and the distance to the object define the footprint of the measurement.  
Already in 2008, point spectrometers were mounted on flying platforms (e.g., [21]). Recently, spectrometers have been miniaturized such that their size and the payload capacity of UAVs converged. In 2014, [PERSON] et al. [22] developed an ultra-lightweight UAV spectrometer system based on the compact Ocean Optics STS [23] for field spectroscopy. It recorded spectral information in the wavelength range of 338 nm to 824 nm, with a full width at half maximum (FWHM) of 3 nm and a FOV of 12\({}^{\circ}\), and later it was used as a flying goniometer to measure the bidirectional reflectance distribution function of vegetation [24]. [PERSON] et al. [25] presented a system to measure sun-induced fluorescence in the oxygen A absorption band with the high-spectral resolution spectrometer Ocean optics USB4000 with an FWHM of 1.5 nm and a FOV of 6\({}^{\circ}\). Furthermore, UAV point spectrometers have been used to investigate the impact of environmental variables on water reflectance [26] and for intercomparison with data from a moderate resolution imaging spectroradiometer (MODIS) in Greenland [27]. Besides, UAV point measurements have been fused with multispectral 2D imaging sensors [28] and applied from fixed-wing UAVs [29], e.g., to monitor phytoplankton bloom [30]. Additionally, first attempts have been made to build low-cost whiskbroom systems for UAVs, which are able to quickly scan points on the surface [31].  
The benefits of point spectrometers are their high spectral resolution, high dynamic range, high signal-to-noise ratio (SNR), and low weight. The USB4000 weighs approximately 190 g, and the STS spectrometer only weighs about 60 g. In combination with a microcontroller, the total ready-to-fly payload with the STS is only 216 g [22], allowing their installation on very small UAVs. However, since their data contains no spatial reference, auxiliary data is required for georeferencing; this makes additional devices necessary, increasing the overall weight and cost of the system (c.f. Section 3.1). In addition, the data within the FOV of the spectrometer cannot be spatially resolved; i.e., each spectral measurement includes spectral information from objects within the FOV of the sensor.
## 3 Results and Discussion
### Validation of the HPLC Analytical Method  
To analyze the stability of the HPLC method, system suitability for parameters such as \(\mathcal{K}\), \(\alpha\), \(N\), \(Rs\), and \(Tf\) was tested using a solution of authentic standards. As shown in Table S3, as a result of considering all parameters, the method is suitable for simultaneous analysis of the chosen standards. The coefficients of determination (\(r^{2}\)), which are indicators of linearity, were all 0.9999, showing good linearity (Table 1). The LOD 0.03-0.41 \(\upmu\)g/mL and LOQ 0.08-1.21 \(\upmu\)g/mL (Table 1) were calculated in the same way as they were in previous studies [15; 16]. As shown in Table 2, the extraction recovery test performed to verify accuracy was 95.67-102.38% in samples to which authentic standards were added at various concentrations (low, medium, and high) to 103.50%, and relative standard
## 8 Results
### Resources  
Being mainly realized in form of digital circuit blocks implemented in an FPGA, the number of look-up tables (LUTs) and D-flip-flops required to realize the transceiver functionality are of major interest. These resources are extracted from the _Lattice Radiant_ toolchain of version 1.1 with the optimization goal 'area' and summarized in Figure 21, divided among the circuit blocks of Figure 9. TX packet handler and BPSK demodulator naturally require most of the components; as the BPSK demodulator includes various shift registers for sampling and storage, the number of required D-flip-flops is particularly high. The complete digital circuit requires 373 LUTs and 196 D-flip-flops.  
In terms of PCB footprint, the analog front-end and the FPGA circuit cover a net single-sided PCB area of 33 mm\({}^{2}\).
## 4 Numerical Simulation Results Analysis
### Impact of Excavation in Individual Stages on the Surface Subsidence above the Pipe-Rof Structure
These curves showed that the surface deformation pattern caused by the earthwork excavation of the closed pipe-roof preconstruction structure still conformed to the Peck model [40]. In other words, the surface subsidence was the largest at the symmetrical axis of the pipe roof, and the surface subsidence gradually decreased with the increase in the distance from the symmetrical axis of the pipe roof. These results are consistent with those in the literature [5; 9; 31]. The overall subsidence data after the completion of the underpass construction were compared to explore the influence of the height-to-width ratio of the underpass section, as shown in Figure 28. When the aspect ratio was 0.30, the final surface subsidence was \(-\)18.602 mm, which was the aspect ratio with the largest subsidence. Conversely, when the aspect ratio was 0.45, the final surface subsidence was \(-\)10.045 mm, which was the aspect ratio with the minimum subsidence. From this point of view, with the increase in the aspect ratio, the excavation impact on the surface subsidence above the underpass would gradually decrease. In other words, under the condition of the same sectional area, the smaller the width of the pipe-roof structure, the more the impact on the ground surface was reduced. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Aspect** & \multirow{2}{*}{**Stage**} & **Maximum Subsidence** & **Increase in Stages** & **Proportion of Total** \\
**Ratio** & & **(mm)** & **(mm)** & **Subsidence (\%)** \\ \hline \multirow{3}{*}{0.30} & Completion of working well & \(-\)6.656 & \(-\)6.656 & 35.78 \\  & Completion of pipe roof construction & \(-\)10.470 & \(-\)3.814 & 20.50 \\  & Completion of underpass excavation & \(-\)18.602 & \(-\)8.132 & 43.27 \\ \hline \multirow{3}{*}{0.36} & Completion of working well & \(-\)7.674 & \(-\)7.674 & 49.32 \\  & Completion of pipe roof construction & \(-\)8.769 & \(-\)1.095 & 7.10 \\  & Completion of underpass excavation & \(-\)15.402 & \(-\)6.630 & 43.26 \\ \hline \multirow{3}{*}{0.40} & Completion of working well & \(-\)5.406 & \(-\)5.406 & 45.56 \\  & Completion of pipe roof construction & \(-\)8.076 & \(-\)2.670 & 22.50 \\  & Completion of underpass excavation & \(-\)11.865 & \(-\)3.789 & 31.93 \\ \hline \multirow{3}{*}{0.45} & Completion of working well & \(-\)3.945 & \(-\)3.945 & 39.27 \\  & Completion of pipe roof construction & \(-\)5.386 & \(-\)1.441 & 14.35 \\ \cline{1-1}  & Completion of underpass excavation & \(-\)10.045 & \(-\)4.659 & 46.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: The surface subsidence values above the pipe-roof structure during excavation in individual stages. Figure 26: Surface subsidence curves above the underpass with an aspect ratio of 0.40 in individual excavation stages. Figure 24: Surface subsidence curves above the underpass with an aspect ratio of 0.30 in individual excavation stages.
# Graphene Quantum Dots Doped PVDF(TBT)/PVP(TBT) Fiber Film with Enhanced Photocatalytic Performance
## 1 Introduction
The resulting polyvinylidene fluoride (tetrabutyl titanate)/polyvinyl pyrrolone ((tetrabutyl titanate))-graphene quantum dots [PVDF(TBT)/PVP(TBT)-GQDs] film photocatalyst was shown to have higher photodegradation performance to Rhodamine B compared to other similar photocatalysts.
## 3 Proposed Solution
### Experimental Setup  
The operational private cloud environment is used for this experimental setup to test the efficiency and output of virtual machines and containers. For our all experiments, we utilized the OpenStack cloud operating system, which is an open source cloud management solution. In this experiment, a cloud environment was set with the composition of fifteen physical machines. Table 1 depicts the specification of every physical machine.  
The aim of this benchmarking procedure is to measure the CPU performance of a particular worker node in our cloud computing environment for scientific workloads. According to HEPIX recommendation, we used the recommended version of OS and kernel (CentOS 7 or Scientific Linux 7 and Kernel v3.1) to achieve better performance. In our experimental setup, we used GNU Compiler Collection (gcc-4.8) default compiler along with following compiler flags: -O2-pthread-fPIC-m32.  
Figure 1 shows complete connectivity and flow of the experimental setup with the fifteen machines. OpenStack is the free cloud platform with several nodes for providing computing resources and other services. The system is composed of network, controller, storage, and several compute nodes. The central management is provided by the controller node in this cloud setup environment. The storage node provides capacity to store images and instances and the network node provides the services related to communication and networking in this environment. This experimental setup consists of twelve compute nodes for providing resources to VMs or containers. These nodes play an essential role for our all experiments because each virtual machine or container uses their computing resources in order to work as a physical machine for scientific jobs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Specs** \\ \hline Architecture & x86\_64 \\ Processor Type & Intel Xeon 2.6 GHz CPU \\ Total Number Cores & 16 (without HT)/32 (with HT) \\ Total RAM & 96 GB \\ Storage Capacity (HDD) & 350 GB \\ NUMA Nodes & 2 (Node 0 = 8 cores and Node 1 = 8 cores) \\ NICs & 3 \(\times\) 1 Gbps Ethernet \\ Operating System & CentOS 7.0 \\ Hypervisor & KVM \\ Linux Kernel & v3.10 \\ Compiler and flags & Compiler: gcc-4.8, Flags: -O2-pthread-fPIC-m32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the physical machine.