esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Recently, the development and applications of multifarious fluorescence probes for metal ions detection in living organisms and the environment have drawn considerable attentions [1; 2; 3; 4; 5]. For all we know, aluminum is the richest metallic element on earth [6]. During cell growth and living body survival, aluminum is not a necessary element. However, it is used broadly in our daily life, such as food ingredients [7], medicines [8], water treatment [9], and the huge manufacturing industry [10], which lead to many aluminum ions discharged in our soil, water, and air [11; 12]. Thus, the excess deposition of Al\({}^{3+}\) in the human body from the food cycle may disturb the immune system and nervous system, and lead to numerous irreversible diseases including cerebral disease [13], Alzheimer's disease [14], and Parkinson's disease [15]. The World Health Organization (WHO) suggested that the daily intake of Al\({}^{3+}\) should be approximate 3-10 mg per person [16; 17]. In China, the National Food Safety Standard sets the aluminum content not to exceed 100 mg/kg in food and food additives (China GB 29924-2013). In view of the above, it is necessary to detect Al\({}^{3+}\) in organisms and the environment [18]. Ion selective electrodes [19], voltammetry [20], and atomic absorption spectroscopy [21] are generally employed to detect metal ions. Nevertheless, these methods commonly have some disadvantages, such as being time-consuming, expensive, needing skilled operations, and having tedious synthetic processes [22]. However, the fluorescence method has attracted much interest in environmental chemistry, biology, and clinical science owing to its high sensitivity and easy operational procedure [16; 23].  
Currently, Schiff bases compounds are vastly recognized as fluorescence probes for metal ions with the high sensitivity, good selectivity, and high excitation coefficients, but a lot of them displayed fluorescence \"on-off\" behavior [24; 25; 26], which is a disadvantage for the visualization study. In recent years, a few \"off-on\" fluorescence probes have been developed, but the probes applied to detect Al\({}^{3+}\) in living cells and real samples were scarce [27; 28; 29; 30]. Hence, it is very meaningful to develop \"off-on\" probes for Al\({}^{3+}\) monitoring in real application [31].  
In this manuscript, we reported a feasible method to synthesize a novel Schiff base, 3,6-imine-triphenylamine-(9-ethyl) carbazole (ITEC), as a \"turn-on\" fluorescence probe for selective detection of Al\({}^{3+}\). The ITEC was further applied to detect Al\({}^{3+}\) in living cells. In addition, the experiments of Al\({}^{3+}\) detection in the real sample have been carried out.
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 1 Introduction
### Monitoring Data Contamination
In terms of numerical values, data whose absolute values are larger than two times the mean square errors can be regarded as gross errors, which are manifested as abnormal sudden jumps or outliers. The outliers are not representative enough for dam deformation characterizations, so they should be opted out from the deformation behavior analysis. Random errors are errors that are caused by a combination of unrelated random factors. In the cases of a single measurement, the random errors may show no regularity in a single measurement, but with enough measurements, this type of error obeys the statistical laws. The noise is a kind of random error. The deformation monitoring information can be divided into real data information and noise information. In deformation behavior analysis and prediction, the existence of noise information will severely affect the accuracy of behavior analysis and prediction, so it is necessary to extract effective information from monitoring information.
# A Survey on Congestion Control for RPL-Based Wireless Sensor Networks
## 2 Overview of RPL
### Supporting Multiple RPL Instances  
A network may run multiple RPL instances concurrently. Each instance is logically independent in that each instance may serve different constraints or performance criteria. A node may belong to multiple RPL instances. A network may consist of one or several DODAGs which together form an RPL instance identified by an RPLInstanceID.
## 4 Results and Discussion
### Comparison of Elastic Beam and Numerical Analyses  
Figure 15 shows a comparison of the bending stress generated in the subway box structure through elastic beam and finite element analyses.  
Figure 14: Comparison between the visual inspection mesh diagram and analysis results (Cell 2 wall) of settlement section.  
The bending stress exceeded the concrete rupture modulus in most sections. The concrete rupture modulus in Figure 15 was obtained using Equation (4).  
\[f_{r}=0.63\lambda\sqrt{f_{ck}}, \tag{4}\]  
where \(f_{ck}\) is the standard design compressive stress of concrete (\(f_{ck}=24\) MPa), \(\lambda\) is coefficient of light weight concrete (1.0).  
Compressive stress was generated inside the raised lower slab and the lower slab of the subsidence of Cells 1 and 2.  
In the case of Cell 1, the compressive stress of most sections exceeded the concrete rupture modulus, as shown in Figure 16a, with transverse cracks in the elevation, diagonal cracks at the upper and lower edges of the boundary, and transverse cracks at the top of the subsidence.  
As shown in Figure 16b for Cell 2, most of the compressive strength exceeded the concrete rupture modulus. In Cell 2, several transverse cracks in the elevation, diagonal cracks at the boundary, and transverse cracks at the top of the subsidence occurred. In  
Figure 16: Comparison result of elastic beam and finite element analyses: (**a**) Cell 1; (**b**) Cell 2.  
Figure 15: Comparison result of elastic beam analysis (bending stress).  
addition, as a result of comparing for the elastic beam analysis and finite element analysis, a difference of approximately 20% occurred in the heaving and approximately 15% difference occurred in the settlement. Therefore, the numerical analysis result of the elastic beam analysis was generally consistent with the crack occurrence.
## 4 Discussion  
### On-Orbit Operations History  
After the launch, data started to be acquired according to the planned mission phases. The first action was the confirmation of the beacon signal reception in several parts of the world. After that, the operations from the Kyutech MGS started by testing all of the subsystems individually (LEOP phase) during the three weeks following the launch. In this period, the main payloads were turned on for a short time to verify the power consumption and that the different experimental data can be read from the different sensors in real-time mode.  
During the LEOP phase, operation training and planning were conducted with the Ten-Koh operators team. For three weeks, the students involved in the operations developed procedures and scheduling tasks that were tested and verified first with the EM model of the satellite before being executed in orbit. This procedure of testing with the EM model allowed the correction of some ground station errors, before commanding the satellite. For example, an error in the pointing of the antenna was corrected to enable more accurate satellite tracking and improve the downlink data throughput.  
Figure 14: Material mission data obtained between December 2018 and January 2019. Estimated CTE from orbital data compared versus the ground test data for Sample 3.  
Figure 13: Magnetometer measurements obtained in the instrument frame of reference (**left**) and in the Ten-Koh frame of reference (**right**) for a period of 220 s. The discontinuities come from loss of data packets in the downlinking process during a communication session between the satellite and Kyutech ground station.  
Another corrected error refers to software bugs in the commanding interface application, which were solved by an upgrade of the software interface.  
Once all ground systems were tested and ready, and after the confirmation of the operation status of the different Ten-Koh subsystems, the nominal operations phase started on 19 November 2018. The first main mission was to operate the CPD instrument for a total duration of 4 min 43 s.
## 5 Key Findings and Critical Discussion  
The key finding from this review shows that lean and green complement each other rather than contradict, as both strategies aim to improve resource utilization, reduce process inefficiencies, and reduce emissions. However, recent frameworks such as those in [16, 76, 88] have made headway in such improvements to effectively incorporate both strategies. Despite these synergies, implementation problems remain even in the SSA petroleum industry, which has, in the past, focused on maximizing profit rather than sustainability. In developed economies, lean and green manufacturing practices lower energy usage and greenhouse gas emissions, save 5-30% on costs, and have less environmental impact [42]. From the review, the SSA petroleum industry has not fully integrated the lean and green approach, which may have contributed to the environmental challenges that the region still faces. Countries with higher emission rates of flaring, like Nigeria and Algeria, have mainly been affected by social, technological, and political issues that can be addressed with a structured approach, which the lean and green integration strategy offers. The SSA petroleum industry can collaborate with suppliers and research institutions to manufacture and commercialize products by employing cutting-edge technologies for improving product quality and supply chain optimization, including operational and energy management [42]. However, implementing this integration requires a significant shift in mindset and behavior within the organization, investment in advanced technology, employee training, also the development and implementation of effective metrics to track sustainability performance. As suggested in this review, the sustainable value stream mapping (Sus-VSM) tool is crucial for the industry as it considers sustainability metric mapping, and the suggested strategic solutions offer pathways to overcome the sustainability challenges in the SSA petroleum industry. These answered the research questions of what the lean and green tools/techniques are and what the strategic solutions to the sustainability challenges in the SSA petroleum industry are.
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 5 Performance Analysis
### Delay Performance  
Regarding the performance verification of the DB-IPDD algorithm, we compare the proposed task offloading and resource allocation algorithm with the following algorithms: (1) Alternative Optimization (AO) algorithm; (2) Genetic algorithm; (3) Greedy algorithm; and (4) IPDD algorithm, where all schemes are running in the same simulation environment.  
The convergence of the DB-IPDD algorithm and the delay performance under different resource allocation schemes are verified in Figure 4. We can observe that the DB-IPDD algorithm has good convergence. The performance of the DB-IPDD and IPDD algorithms is obviously better than that of the Greedy, Genetic, and AO algorithms. The performance of the DB-IPDD algorithm is better than that of IPDD, which is because the DB-IPDD adopts Theorem 1 to find a better resource allocation condition.
# MAOA: A Swift and Effective Optimization Algorithm for Linear Antenna Array Design
## 3 MAOA Algorithm
### Overall Workflow of MAOA  
Figure 5 shows the flowchart used to explain the overall workflow of the proposed MAOA. Initially, the modified initialization scheme, as detailed in Figure 2, is used to generate the initial population of MAOA by harnessing the benefits offered by chaotic maps and ROLM. The MAOA population is represented as \(\mathrm{P}=[X_{1},\ldots,X_{n},\ldots,X_{N}]\). During the iterative search process of MAOA, the construction mechanism described in Figure 3 is used to determine the unique guidance (\(UG_{n}\)) assigned to each \(n\)-th solution to guide its search process. Unlike the original AOA, the \(UG_{n}\) is used to calculate the new position (\(X_{n,d}^{New}\)) of each \(n\)-th solution in every \(d\)-th dimension using the four arithmetic operators, as described in Equations (20) and (21). After calculating the new position (\(X_{n,d}^{New}\)), mutual learning is further applied to each \(n\)-th solution based on the mechanism described in Figure 4 to calculate the \(d\)-th component of its updated position (\(X_{n,d}^{New}\)) by interacting with the corresponding dimension of the mutual learner (\(ML_{n,d}\)). Fitness evaluation is then performed on the updated solution of each \(n\)-th solution to obtain the associated fitness value of \(f\big{(}X_{n}^{Next}\big{)}\).  
Let \(\mathrm{P}^{\mathrm{Next}}=\big{[}X_{1}^{Next},\ldots,X_{n}^{Next},\ldots,X_ {N}^{Next}\big{]}\) represent the set variable containing all updated solutions of MAOA produced through the mutual learning process. Both \(\mathrm{P}=[X_{1},\ldots,X_{n},\ldots,X_{N}]\) and \(\mathrm{P}^{\mathrm{Next}}=\big{[}X_{1}^{Next},\ldots,X_{n}^{Next},\ldots,X_ {N}^{Next}\big{]}\) are then combined to create a merged population (\(\mathrm{P}^{\mathrm{MG}}\)) that has a population size of 2N, as follows:  
\[\mathrm{P}^{\mathrm{MG}}=\mathrm{P}\cup\mathrm{P}^{\mathrm{Next}} \tag{25}\]  
All solution members within \(\mathrm{P}^{\mathrm{MG}}\) are then sorted based on their fitness values in ascending order, i.e., from the best to the worst fitness values, to generate a sorted population variable denoted as \(\mathrm{P}^{\mathrm{S}}\). In other words, the better-performing solutions in \(\mathrm{P}^{\mathrm{SR}}\) are assigned smaller population indices, while the worse-performing solutions are assigned  
Figure 5: Flowchart of the MAOA algorithm.  
larger population indices. The best N solutions within \(\mathrm{P}^{\mathrm{S}R}\), having better fitness values, are then extracted to form the population \(\mathrm{P}\) for the next iteration:  
\[\mathrm{P}=Extract(\mathrm{P}^{\mathrm{S}R},N) \tag{26}\]  
where \(Extract(\cdot,\cdot)\) is an operator that takes a population set and the number of elements to be selected from that set as the first and second input arguments, respectively. The iterative search process of MAOA is repeated until the termination count is attained, i.e., when the current iteration count (\(\tau\)) exceeds the maximum number of iterations (\(\tau^{max}\)), i.e.,\(\tau>\tau^{max}\).
## 3 Results and Discussion
### Mechanical Properties
#### 3.5.2 Tensile Properties
They reported that tensile samples without PWHT fractured at the weld joint, whereas after PWHT, the fracture occurred in the SNCRW parent metal. Figure 16: The failure location of the dissimilar SS304L/IN718 joints in the (**a**) as-welded ST, (**b**) as-welded STA, and (**c**) PWHT conditions. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Sample** & **Yield Strength** & **Ultimate Strength** & **Elongation** & \\
**(MPa)** & **(MPa)** & **(\%)** & **Location of Failure** \\ \hline IN718 ST Base & 938 \(\pm\) 4 & 1312 \(\pm\) 2 & 30 \(\pm\) 2 & - \\ IN718 STA Base & 1075 \(\pm\) 5 & 1375 \(\pm\) 10 & 28 \(\pm\) 1 & - \\ SS304L Base & 460 \(\pm\) 3 & 615 \(\pm\) 5 & 51 \(\pm\) 4 & - \\ IN718 ST-SS304L & 573 \(\pm\) 4 & 713 \(\pm\) 2 & 10 \(\pm\) 1 & Near weld interface on SS304L side \\ IN718 STA-SS304L & 497 \(\pm\) 2 & 670 \(\pm\) 3 & 16 \(\pm\) 2 & Near weld interface on SS304L side \\ IN718-SS304L PWHT & 604 \(\pm\) 5 & 721 \(\pm\) 4 & 07 \(\pm\) 1 & SS304L base \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Typical tensile data of the base metals and welded joints.
## 2 Materials and Methods
### Model Output Accuracy Evaluation  
We calculated the R\({}^{2}\), RMSE, and MAD in this study to evaluate the performances of the GPPs (GPP_NDVI, GPP_EVI, GPP_Cl\({}_{\text{norm}}\)) estimated by the remote sensing GPP model and the GPPs (GPP_train1, GPP_train2, GPP_train3, GPP_train4, GPP_valid1, GPP_valid2, GPP_valid3, GPP_valid4) estimated by the BNNA (Figure 7).  
P and O, represent the GPP simulation values and the GPP from eddy covariance, respectively, and i and n represent the days of the year and the total number of days in the year, respectively. The R\({}^{2}\), RMSE, and MAD are expressed as follows:  
\[\text{R}^{2}=1-\frac{\sum_{\text{i=1}^{n}(\text{P}_{\text{i}}-\text{O}_{\text{ i}})^{2}}}{\sum_{\text{i=1}^{n}\text{O}_{\text{i}}}^{2}} \tag{18}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{\text{i=1}^{n}(\text{P}_{\text{i}}-\text{O} _{\text{i}})^{2}}} \tag{19}\]  
\[\text{MAD}=\frac{\sum_{\text{i=1}^{n}\left|\text{P}_{\text{i}}-\text{O}_{\text {i}}\right|}}{n} \tag{20}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters and Values** & **Explanatory Notes** \\ \hline epochs = 100 & Number of iterations \\ batch\_size = 6 & Number of feeding data for each training model \\ learnrate = 0.1 & Learning rate \\ Activation = “tanh” & Activation function \\ Terror = \(1\times 10^{-7}\) & Iteration stop condition \\ Loss = “mean\_squared\_error’ & Loss function \\ Dropout = 0.2 & Rejection rate of each layer \\ model & Hierarchical model \\ \hline \end{tabular}
\end{table}
Table 1: The parameter settings in the BP neural network algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline
**Experiments** & **Input Layer** & **Output Layer** \\ \hline Exp 1 & f(VPD), PAR, f(T\({}_{\text{min}}\)), CI\({}_{\text{norm}}\) & GPP \\ Exp 2 & f(VPD), PAR, f(T\({}_{\text{min}}\)), NDVI & GPP \\ Exp 3 & f(VPD), PAR, f(T\({}_{\text{min}}\)), EVI & GPP \\ Exp 4 & f(VPD), PAR, f(T\({}_{\text{min}}\)), CI\({}_{\text{norm}}\), NDVI, EVI & GPP \\ \hline \end{tabular}
\begin{tabular}{c} GPP refers to the GPP from eddy covariance. \\ \end{tabular}
\end{table}
Table 2: Machine learning-based GPP estimation experiments.  
Figure 6: Structure of the BP neural network algorithm.
## 2 Materials and Methods
### Hemolysis Assay  
The hemolysis assay was utilized to determine _S. triloba_'s hemolytic effect. Institutional Review Board (IRB) approval was granted from the American University in Cairo to collect 2 mL blood samples from three healthy volunteers, after informed consent at the university clinic. Human erythrocytes were exposed to concentrations approximate to either IC50 or IC75 of _S. triloba_ acetone extract. Blood was withdrawn into vials containing Ethylenediaminetetraacetic acid (EDTA) and centrifuged at 1000\(\times\)\(g\) for 5 min, then washed twice using PBS. The Serum was discarded, and 2% erythrocytes were prepared using PBS. 50 \(\upmu\)L of the 2% erythrocyte solution was added per well in a round bottom 96-well plate, in addition to 50 \(\upmu\)L of each treatment; this resulted into a final erythrocyte concentration of 1% per well. As a negative control (0% hemolysis), 50 \(\upmu\)L of PBS was used in place of extract treatment. Deionized water was, on the other hand, used as a positive control (100% hemolysis). Plates were incubated at 37 \({}^{\circ}\)C for 1 h, and then centrifuged for 10 min at 3000\(\times\)\(g\) in a plate centrifuge. The resulting supernatant (80 \(\upmu\)L) was transferred to a new 96-well plate and absorbance readings were taken at 570 nm. Percentage hemolysis was calculated using Equation (6) [45]:  
\[Hemolysis\ (\%)=\frac{(Amax-At)}{(Amax-Amin)}\ \times\ 100 \tag{6}\]  
where _Amax_, _Amin_, and _At_ represent the absorbance values for erythrocytes incubated with deionized water, PBS, and _S. triloba_ acetone extract respectively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Matrix library & TRANSFAC MATRIX TABLE, Release 2020.2 \\ \hline Sequence file & MTBP\_PM000918140 \\ \hline Profile & vertebrate\_non\_redundant\_minFP:prf \\ \hline Only high-quality matrices & Yes \\ \hline Cut-offs & Minimize false positives \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used for TFBS annotation.
## 1 Introduction
This framework provides an effective method for determining the location and size of defects in the steel process at a relatively low cost without the need for expensive and detailed segmentation annotation, and contributes to the appropriate subsequent handling of defects. This study presents the following three main contributions:  
* This paper proposes an efficient framework to segment and detect defects effectively from raw images collected in a steel manufacturing process using only bounding box annotations. This contributes to a better understanding of the size and shape of defects necessary for subsequent processes, thereby aiding in cost reduction in defect management. * We compare different networks on fault detection and classification tasks to determine which models perform best on the steel surface defect dataset. * Using steel surface defect data, we explored recursive learning methods to train a segmentation model using only bounding box annotations and GrabCut, and continuously improved the model's segmentation predictions through an incremental learning process. This paper is structured as follows. Section 2 discusses steel surface defect detection and deep learning techniques relevant to this study. Section 3 describes the methodology proposed in this work. Section 4 details the data used in this study and the experimental results obtained using the proposed methodology. Section 5 presents the conclusions of this study.
# Functionalization Strategies and Fabrication of Solvent-Cast PLLA for Bioresorbable Stents
## 2 Materials and Methods
### PLLA Films Degradation  
To compare the rate of degradation of the different PLLA films compared to the REMEDY stent, an accelerated degradation assay was performed in an alkaline solution [28; 44; 45]. First, in order to have the same ratio surface/volume for each condition, all the PLLA films were cut in rectangular fibers with sections similar to REMEDY stent strut sections (\(\approx\)70,000 \(\upmu\)m\({}^{2}\)). All the initial samples (REMEDY stent, SC, TT, OP, SH, and ET) were dried in an argon atmosphere oven at 48 \({}^{\circ}\)C during 2 h and weighed with a Sartorius Quintix35-1S laboratory balance (Gottingen, Germany). Next, all the samples were placed at 37 \({}^{\circ}\)C in a 100 mL solution of 0.1 M NaOH. At 2, 4, 7, and 10-days, the samples were dried as initially (at 48 \({}^{\circ}\)C during 2 h) and weighed in order to calculate the mass loss. Moreover, initial and final samples surfaces were observed by scanning electron microscopy (SEM) with a Zeiss Neon40 FE-SEM microscope (Carl Zeiss NTS GmbH, Oberkochen, Germany). For each sample, five images were taken at a working distance of 6 mm and a potential of 5 kV. Finally, the samples topography was characterized by WLI, as previously described (Section 2.5), to evaluate changes between the final (Ra\({}_{I}\)) and initial (Ra\({}_{i}\)) mean roughness, the ratio Ra\({}_{I}\)/Ra\({}_{i}\) was calculated.
# Si Photonics for Practical LiDAR Solutions
## 4 Si Photonics Optical Phased Array
### OPA System Design
#### 4.1.1 Beam Divergence
Although an mm aperture in an OPA chip does not sound unreasonable, when this requirement couples with antenna arrangement and phase shifter controlling complexity, it becomes a real challenge. The details are discussed in Section 4.2. This simple relation in Equation (4) also explains why millimeter wave radar has great challenges in achieving image grade resolution with reasonable size and cost.