esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Different Welding Methods on Flip-Chip LED (FC-LED) Filament Properties
## 3 Results and Discussion
### Change of Photoelectric Performance with Aging Time  
Figure 6 shows the steady-state voltage curve of the filaments with aging time increasing. From this figure, it can be seen that the voltage of the two groups of filaments decreases significantly after 72 h of aging. This is because in the early stage of the filaments' aging process, a large amount of heat is produced in the solder layer, which causes a chemical reaction in the solder to form an intermetallic compound. The solder becomes more compact, the hole becomes smaller, the resistance value of the filament is reduced, and the current is constant when it is lit. The decrease of resistance leads to the decrease of voltage.  
The voltage then increases slowly during the aging of 72-504 h, but the overall trend is still downward. Before and after aging, the absolute value of the steady-state voltage change rate of the DW group was 1.03%, and the steady-state voltage change rate of the VFW group was 1.07%. There is a small difference between the two groups.  
In the process of falling luminous flux, there is sometimes an abnormal rise. It was concluded that there were two contradictory mechanisms in the aging process of LED, that is, the increase mechanism of optical output and the mechanism of attenuation. The authors of [25] believe this is because in the early stages of aging, the P-type doped layer MG-H complex matrix decomposition caused by the activation of Mg and the conductivity of the P-type layer is increased, and the forward voltage drops; on the other hand, the composite probability of minority carriers increases, thereby enhancing the light output. In the meantime, with the increase of the impurity level and the non-radiative recombination of the center, much new energy is generated in the process of aging, resulting in a decreased light output, thus increasing luminous flux and the attenuation of two kinds of phenomena [26].  
Figure 7 shows the change curve of aging time and steady-state luminous flux. As was the case with the voltage, the luminous flux of the two groups decreased obviously at 72 h. After aging for 216 h, the luminous flux of the DW group increased continuously, whereas that of the VFW group increased first and then decreased. The steady-state luminous flux of both groups decreased after aging. The luminous flux decreased by 8.45% in the DW group and 5.4% in the VFW group. In fact, 50% of the initial luminous flux for the LEDs was used as indicators and 70% for general lighting applications; in this study, the 70% value was selected in light of the type of applications for which these LEDs were  
Figure 6: Steady-state voltage curve of the filaments with aging time increasing.  
used. They all met the standard. However, compared with the direct welding filament, the vacuum furnace welding filament had a higher luminous flux maintenance. Therefore, the light decay after vacuum welding is less.
## 2 Materials and Methods
### Data Preprocessing and Labeling  
The Entrepot de donnees de Sante (EDS) stores the medical data of millions of patients treated at the greater Paris University hospitals--Assistance Publique--Hopitaux de Paris (AP-HP). The University hospitals are in full agreement with the European General Data Protection and Regulation (GPDR) and the French Data Protection Authority (CNIL) for health data processing (reference method MR-004). The EDS provided centralized virtual working environments with dedicated computing platforms.  
The pseudonymized targeted 3D PET data (DICOM format) [12] were transferred, together with their corresponding matched report metadata, from the general storage space to a personalized dedicated working space by using advanced request procedures based on HiveQL ([[https://hive.apache.org/](https://hive.apache.org/)]([https://hive.apache.org/](https://hive.apache.org/)) accessed on 22 April 2023)--a query language which allows Hive to process and analyze structured massive data in the Metastore--and PySpark ([[http://spark.apache.org/](http://spark.apache.org/)]([http://spark.apache.org/](http://spark.apache.org/)) accessed on 22 April 2023), a Python application programming interface for massive structured data processing. In this dedicated working space, the 3D PET data were processed and downsampled by the removal of the body-surrounding background, the conversion of voxel values from float32 to float16 and matrix size reduction by a 3D kernel of 3 \(\times\) 3 \(\times\) 3 mean pooling and then normalized by z-scoring of the SUV values with standardization of the slice number (\(n\) = 549). All the 3D PET data were then labeled \"normal\" or \"abnormal\" through a two-step cross-validation procedure:  
* First, an \"in-house\" automated text mining procedure was developed to automatically classify the PET scans based on the anonymized written imaging reports.  
All the reports were written following a predefined \"normal\" template. Reports that did not differ from this template were considered \"normal\" and the others were set aside for a deeper report verification.  
* Second, the maximum intensity projection of all the PET data was converted into nine 2D projections (from 0\({}^{\circ}\) to 180\({}^{\circ}\) rotation) to be carefully qualitatively reviewed by an experienced nuclear medicine physician ([PERSON], who had 5 years of expertise in PET imaging). For this purpose, any well-grounded nonphysiological uptake was considered abnormal.
## 2 Study Area and Data Sources
### Methods
#### 2.3.5 Future LUC Simulation Projections  
The GeoSOS-FLUS model is a multi-class LUC change scenario simulation model developed by [PERSON] et al. [50; 51] based on the principle of the FLUS model. This is mainly composed of an artificial neural network and an adaptive inertia competition mechanism. The artificial neural network can effectively discover the relationship between natural, social, and economic factors and LUC change, obtaining the suitable probability of each class in the research scope. The adaptive inertial competition mechanism can help to solve the uncertainty of local class transformation and make up for the complexity of the local transformation and parameter determination of traditional cellular automata. The GeoSOS-FLUS model has better predictive power and higher accuracy compared with other models. Therefore, the GeoSOS-FLUS model was used to simulate and compare the LUC layout under the scenarios of NDS and EPS in Guilin in 2040, testing the effectiveness of biodiversity protection measures.  
This study synthesized natural and socio-economic factors, selecting the DEM, slope, aspect, annual rainfall, GDP, and average annual temperature as the driving factors of LUC change. The probability, intensity, and direction of transitions between different LUC types were adjusted by setting the model parameters for different scenarios of the LUC simulation. (1) In the NDS, the Markov chain was applied to predict the LUC demand in 2040 according to the LUC transfer matrix from 2000 to 2020. The parameters of the model remained unchanged, and the trends in population, economic, and technological innovation continued to align with the current situation in this scenario. (2) In the EPS, WTL, and WL should be protected first, while CL and AL should be controlled based on the analysis results of the cold spots and hotspots of biodiversity in the study area. It is established that WL and WTL cannot be converted to other LUC types, and the neighboring factor of WL and WTL was set to 1. The neighboring factor of CL and AL was set to 0, and the neighboring factor of other LUC types was set to 0.5.
## 3 Methodology  
### Research Questions  
The review of the relevant literature showed that the existing mathematical models are insufficient to enable a realistic simulation study of automotive lidar performance under real-world environmental conditions.  
Therefore, two research questions were formulated to guide this study:  
Research Question 1: How can a comprehensive set of empirical models for the extinction and backscattering coefficient be developed to cover all environmental weather conditions relevant to automotive lidar applications?  
The literature review revealed that for some weather phenomena, no empirical law is available, whereas in other cases, multiple laws are provided, with no clear prescriptive guidance on usage in an engineering context. In a first step, the empirical models available are to be evaluated and their use in the simulation discussed. This research proposes to address missing models by using the Mie theory, as well as the plausibility of the proposed models to be analyzed.  
Research Question 2: Do the proposed models, utilized in conjunction with virtual driving simulation environments, produce valid results?  
Figure 7: Comparison of different visibility models during sandstorms, [PERSON], 1996 [31], [PERSON] et al., 2003 [32], [PERSON] et al., 2014 [33].  
The use of the proposed empirical models is to be verified by including the effect of the weather conditions on the lidar perception in the simulation via the empirical mathematical model. The validation exercise revolves around the simulation of a specific lidar system, i.e., the Valeo Scala 2 system. The results from the simulation will be compared with results on actual vehicles, reported in the literature. For weather situations for which there is no comparable real experiment, a prediction of the expected behavior is evaluated under the corresponding weather effect.
# Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant
## 2 Literature Review  
### Steady State Simulation  
Steady state simulation is based on first principles such as conservation laws, phase equilibria, heat and mass transfer, and reaction kinetics. Steady state simulation focuses on stable operating conditions. Unlike dynamic simulation, it does not consider the time dependency of the process [14; 15], so it assumes that variables are constant with respect to time. In steady state, there is no accumulation of mass or energy within the system, so the overall mass and energy input equals its output. Steady state simulation is typically conducted in the early-state design of plant wide systems or process departments. Steady state modeling and simulation has been widely used in the industry for establishing mass and energy balances, evaluating and improving the process performance, process design, plant equipment sizing, and process optimization [14; 15; 16; 17; 18]. Inputs to steady state models are pressures, temperatures, flows, and compositions; outputs are equipment sizing and process optimizations [14].  
The computational complexity increases considerably from steady state to dynamic simulation. Thus, dynamic simulation model building requires significant additional engineering effort to determine the model parameters [18; 19]. However, since the steady state simulation model is a basis for the development of a dynamic model [18], dynamic modelling can be considered only later when more understanding of the intended commercial implementation of the technology is available.  
The level of detail in steady state simulation studies varies from small-scale chemical reactions to mill-wide process calculations in many fields of industry. In the pulp and paper industry, steady state simulation has been applied to optimize water consumption [20; 21], minimize energy and utility consumption [22; 23; 24], and evaluate the chemical state of the process [25; 26]. [PERSON] et al. [27] defined a steady state simulation model of a kraft pulp mill and evaluated the economic feasibility of the process. In the field of biorefineries, steady state simulation has been used for process modelling and evaluating the economic performance of biomethane [28], bioethanol [29], biodiesel [30], and renewable diesel production [31]. [PERSON] et al. [32] used steady state simulation to study carbon capture and utilization opportunities in a sugarcane biorefinery. [PERSON] and [PERSON] [33] used plant-wide steady state process simulation models as part of a methodology for early stage screening of forest biorefinery retrofit scenarios. Steady state simulation has also been used in numerous other process and/or economic performance studies in the field of wastewater purification [34], chemical production [18; 35; 36], mineral processing [37] and food industry [38; 39].
## 3 Methodology
### Summary  
The proposed method contains three steps: simplified belief propagation, high agglutination sampling, and variation expectation-maximization. A simplified belief propagation algorithm is proposed as the overall reasoning framework by modeling the cooperative localization problem as a graph model. The high-aggregation sampling and variation expectation-maximization algorithm is applied to sample and fit the complicated distribution. The overall flow is depicted in Algorithm 2.  
```
Input: Position of anchor point, distance estimation between nodes Output: Location estimation of target nodes initialization; whilei<i_iterdo  i = i+1 for\(t\in\mathcal{V}_{t}\)do  1. Receive the belief of neighbor nodes..\(B_{u|u\in\mathcal{G}_{t}}=m_{u|u\in\mathcal{G}_{t}}^{i-1}\)  2. Based on the Formula (12), calculate the potential function \(\psi_{ut}(x_{u},x_{t})I\)  3. Calculate the aggregate \(\hat{B}_{t}^{i}\) of the potential functions of neighboring nodes based on Formula (13) 4. Using (20)-(25), the feasible spatial sampling of the node is \(x_{sample}^{i}\)  5. Based on VEM, fit the sampling results to \(B_{t}^{i}=g(\alpha,\mu,\sigma)\)  6. Broadcast your own location distribution \(m_{t}^{i}=b_{t}^{i}\)  end while end
```  
**Algorithm 2**Cooperative localization based on SBPVEM method  
The point-to-point message transmission between nodes is turned into the node's position estimation broadcast in simplified belief propagation. Before importance sampling, high-aggregation sampling adds a filter layer to improve efficiency by clustering particles in high-probability intervals. Finally, the particle is fitted into a Gaussian mixture model with an unknown number of components using variable expectation maximum.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Simulation Results
#### 6.2.3 Number of Hop Count  
Figure 7 depicts the number of hop count for the network size. In a dense network, the number of hops between the sources to the destination is very high. The reason is that the numbers of nodes are deployed randomly in a small network area. The simulation experiment is conducted and the results are noted for the performance analysis. The hop counts of RPL, LA-RPL and EDADA-RPL are 8, 7, and 5 respectively, for the network size of 120. It is also found that the size of the network is increasing; the number of hop counts also increases. In EDADA-RPL, the number of hop count decreases, as it performs the data aggregation during the data transmission. Thus, it reduces the number of hop count and provides route stability among the nodes in the network.
# Pharmacokinetics of Pullulan-Dexamethasone Conjugates in Retinal Drug Delivery
## 3 Results
### Safety Assessment of Pullulan-Based Formulations
#### 3.4.1 Safety on Ex Vivo Mouse Retinal Explants  
Ex vivo mouse retinal explants were treated with Cy3-pullulan (1.7 mg/mL) or Cy3-pullulan-DEX (0.7, 1.4 and 1.9 mg/mL) and untreated explants were used as controls. All three Cy3-pullulan-DEX concentrations significantly reduced the percentage of TUNEL positive cells in both the inner nuclear layer (INL) and the outer nuclear layer (ONL), indicating a reduction in the cell death in both layers (Figure 9A,B). This is due to the neuroprotective effect of DEX in ex vivo mouse retinal explants [55]. Treatment with Cy3-pullulan alone did not increase TUNEL labelled cells, indicating that the polymer had no toxic effects. The number of INL and ONL cell rows did not change (Figure 9C,D).  
To evaluate any inflammatory response due to the conjugates, we visualized the microglial cell shape and distribution using Iba-1 antibody, an inflammatory marker for microglia. The microglial cells were observed in ganglion cell layer (GCL) and INL in all groups, but no microglial migration to the ONL was seen (Figure 10). The conjugates (Cy3-pullulan, Cy3-pullulan-DEX) were found in all monitored layers, mostly in GCL and INL, and partially in ONL. The inflammatory response of microglia was not activated, and the conjugates are well tolerated in the ex vivo retinas.  
Figure 9: Ex vivo retinal explants of mice were treated with 15 \(\upmu\)L of fluorescently labelled Cy3-pullulan-DEX (0.7, 1.4 and 1.9 mg/mL) and Cy3-pullulan (1.7 mg/mL). Untreated retina (NT) was used as control explant. TUNEL-positive nuclei in (**A**) the inner nuclear cell layer (INL), and (**B**) the outer nuclear cell layer (ONL) were counted and plotted as percentage of all nuclei in the INL and ONL areas. The number of cell rows in (**C**) INL and (**D**) ONL are also presented. Bars indicate standard deviations of means. One-way ANOVA ***\(p<\) 0.0001.
## 2 Materials and Methods
### Sample Preparation  
#### 2.1.1 Cleaning Prior to Coating  
Two different sets of cleaning processes were applied. Samples of set A were cleaned using a standard semiconductor wet bench equipped with a quick dump rinse. The change of cleaning baths was done manually. A fully automatic cleaning was performed for samples of set B using an industrial ultrasonic cleaning machine provided by UCM AG. According to a specified recipe, the substrates dwell for a well-defined time in the baths and were transported entirely automatically from bath to bath. A total of seven different water-based precision cleaning processes referred to as A-CP1, A-CP2, A-CP3 for set A and B-CP1, B-CP2, B-CP3, B-CP4 for set B, respectively, were applied on the uncoated samples prior to coating. For better comparison, the referenced standard cleaning process was A-CP1.  
In all cases, the precision cleaning steps were: cleaning 1--rinsing-cleaning 2--rinsing-rinsing-lift-out-drying. As water-based chemicals for cleaning 1 and cleaning 2 deconex(r) OP 148 (3 vol%, pH value of 12) and deconex(r) OP 164 (2 vol%, pH value of 7) from Borer-Chemie AG were used, respectively. In case of set A, the samples were dried in an oven under air for 600 s at a temperature of 75 \({}^{\circ}\)C. To avoid renewed contamination with particles, the oven was equipped with a HEPA-filter. In case of set B, the samples were dried with infrared heaters. To investigate the influence of ultrasonic frequencies and power, we used ultrasonic of about 40, 80, 120 as well 500 kHz (A-CP3, B-CP2-B-CP4), whereas in case of A-CP3 the ultrasonic power was not adjusted. Prior to precisions cleaning, some substrates were pre-cleaned using solvents. As the solvent, acetone at room temperature without ultrasonic assistance was used. As acetone is highly volatile, purging by means of isopropanol (IPA) after the acetone bath is recommended. In Table 1, the parameters of the cleaning processes are summarized.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cleaning Process** & **Pre-Clean** & **Cleaning 1** & **Cleaning 2** & **Rinsing** \\ \hline Set A & A-CP1 & Yes & 40 kHz/180 s & 40 kHz/180 s & -/2 \(\times\) 180 s \\ \hline  & A-CP2 & No & 40 kHz/180 s & 40 kHz/180 s & -/2 \(\times\) 180 s \\ \hline  & A-CP3 & Yes & 40 kHz/180 s & 80 kHz \({}^{1}\)/60 s & -/2 \(\times\) 180 s \\  & A-CP3 & Yes & 40 kHz/180 s & 120 kHz \({}^{1}\)/60 s & \\ \hline Set B & B-CP1 & Yes & 40 kHz/180 s & 40 kHz/180 s & -/2 \(\times\) 180 s \\ \hline  & B-CP2 & Yes & 40 kHz/180 s & 40 kHz/60 s & \\  & B-CP2 & Yes & 40 kHz/180 s & 80 kHz/60 s & -/2 \(\times\) 180 s \\  & B-CP3 & Yes & 40 kHz/300 s & 80 kHz/150 s & -/2 \(\times\) 180 s \\  & B-CP4 & Yes & 40 kHz/180 s & 40 kHz/60 s & \\  & B-CP4 & Yes & 40 kHz/180 s & 80 kHz/60 s & 500 kHz/2 \(\times\) 180 s \\  & & & & 120 kHz/60 s & \\ \hline \hline \end{tabular}  
* No ultrasonic power adjustment.  
\end{table}
Table 1: Applied ultrasonic frequencies and process times for the ultrasonic cleaning steps as well as the fine-rinsing step used for cleaning processes of set A and set B.
## 3 Results
### Occurrence and Removal of Residual Antibiotics During Wastewater Treatment  
Among the eight target antibiotics, sulfamethazine displayed the highest initial concentration, measuring 7269.6 \(\mathrm{\SIUnitSymbolMicro}\)/L in the raw wastewater (Figure 3). Its level dropped to 3449.3 \(\mathrm{\SIUnitSymbolMicro}\)/L after flocculation, and decreased further to 2520.4 \(\mathrm{\SIUnitSymbolMicro}\)/L following sedimentation, but rose slightly to 3114.7 \(\mathrm{\SIUnitSymbolMicro}\)/L after sand filtration. Notably, sulfamethazine declined dramatically to 11.4 \(\mathrm{\SIUnitSymbolMicro}\)/L and 7.6 \(\mathrm{\SIUnitSymbolMicro}\)/L in the first and second ozone reactors, respectively, but rebounded to 41.8 \(\mathrm{\SIUnitSymbolMicro}\)/L following activated carbon filtration and ultimately reached 438.9 \(\mathrm{\SIUnitSymbolMicro}\)/L in the final effluent. During coagulation and sedimentation, 52.5% of the initially detected sulfamethazine was removed; following the ozone treatment stage, 99.9% of the original sulfamethazine was eliminated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Title 1** & SS & BOD & TOC & TN & TP \\ \hline Removal Efficiency (\%) & 92.2 & 33.6 & 69.8 & 3.1 & 96.9 \\ \hline \end{tabular}
\end{table}
Table 1: Removal efficiencies of water quality parameters after flocculation.  
Figure 3: Antibiotic concentrations at each treatment process.  
Figure 2: Water quality at each treatment process.  
The concentration of limomycin was 69.1 \(\upmu\)g/L in the raw wastewater; this was reduced to 24.5 \(\upmu\)g/L after flocculation. It hovered around 30.7 \(\upmu\)g/L and 29.8 \(\upmu\)g/L in the respective sedimentation and sand filtration steps before dropping below the detection limit in the first ozone reactor and remaining undetected through to the final discharge. Tiamulin, initially detected at 14.2 \(\upmu\)g/L, decreased to 7.7 \(\upmu\)g/L after flocculation, rose slightly to 12.9 \(\upmu\)g/L following sand filtration, and was entirely removed after the first ozone reactor, with no traces found in subsequent stages. Marbolloxacin, present at 2.80 \(\upmu\)g/L in the raw influent, decreased to 0.18 \(\upmu\)g/L after flocculation and further declined to 0.04 \(\upmu\)g/L by the point of the second ozone reactor. However, it rebounded to 0.24 \(\upmu\)g/L in the activated carbon filtrate and reached 0.40 \(\upmu\)g/L in the final effluent. Oxytetracycline and chlorotetracycline, measured at 0.23 \(\upmu\)g/L and 1.15 \(\upmu\)g/L, respectively, were both eliminated immediately after flocculation and did not reappear in any subsequent treatment steps. Fenbendazole followed a similar pattern, starting at 0.24 \(\upmu\)g/L in the raw wastewater and becoming undetectable after flocculation. Trimethoprim was not detected at any point in the process.