esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### People Counting Using the ROI  
This research employed surveillance cameras or cameras installed in corridors and doorways at angles of 45 or 90 degrees relative to the ground to count the number of people passing through the current scene. [PERSON] et al. [9] detected the behavioral characteristics of crowds using anomaly detection and trained a CNN on collected behavioral feature datasets. In this study, the first step involved the collection and annotation of pedestrian detection training sets for training on the CNN. Then, the algorithm based on object extraction was used to locate the X and Y coordinates of individuals in each frame image. Simultaneously, the target object's positioning in the previous or subsequent frame image was considered. Compared to conventional CNN-based detection of image positions, this approach can instantly capture the locations of target objects in the image. However, the limited variety of training data used in this study may have resulted in a decreased accuracy when recognizing large and diverse datasets, which does not meet the authors'expectations. [PERSON] et al. (2018) proposed a people-counting method for regions using the mosaic image difference (MID) and histograms of oriented gradients (HOGs) based on the head-shoulder detection algorithm. First, MID was used to extract the foreground from the active area and the target image. Then, the head-shoulder detection model was employed to detect the shapes and numbers of heads and shoulders in the foreground-extracted region, which served as the basis for people counting. This method is suitable for both static and dynamic conditions and focuses on a specific region of interest in the image. However, its performance under occlusion is not well described, and its effectiveness when dealing with head-obscured situations remains unclear. [PERSON] et al. (2018) utilized the Faster R-CNN and ResNET50 for people counting in surveillance footage, achieving an accuracy of 97.20%. The authors trained on 900 images of 640 \(\times\) 480 resolution with 300 images used for testing. The training set was used to train the Faster R-CNN, and the obtained weights were added to the target Faster R-CNN model to count people in the surveillance footage. The major drawback of this study is the limited size of the training set, which raises questions about its generalizability to different scenarios. [PERSON] et al. (2018) aimed to address the inaccuracies in traditional pedestrian-counting methods caused by overlapping individuals. This research combined person detection and tracking, where the detected target objects in each frame were tracked and erroneous tracking targets (e.g., persons' shadows, etc.) were eliminated. On the other hand, [PERSON] et al. (2018) used a two-step object tracking approach to reduce the instances of object detection failure and detection errors. In the first step, all possible target objects were tracked, and in the second step, a trained deep learning model was used to select and highlight the desired target objects from the candidates.
###### Abstract  
Jawoong is a traditional prescription used widely in East Asia for external applications and is effective for burns, sunburn, wounds, and symptoms of dryness in skin disease. This study was established to examine how Jawoongo treatment affects the photodamage of HaCaT cells caused by ultraviolet B (UVB) irradiation and explore its possible anti-photoaging mechanisms from the perspective of the associated signaling pathways. An in vitro photoaging model was established by irradiating HaCaT with UVB, and the expression of photodamage markers such as matrix metalloproteinases (MMPs) and reactive oxygen species (ROS) was assessed by enzyme-linked immunosorbent assay (ELISA), FACs, and RT-PCR. Western blotting was also used to ascertain the expression levels of the TGF-\(\beta\)/Smad and nuclear erythroid 2-related factor \((\text{Nrf2})/\text{antioxidant}\) response element (ARE) signaling pathways. The findings of this study showed that the increased production of ROS, MMP-1, and MMP-3 due to UVB irradiation was reduced by Jawoongo. In addition, the Nrf2/ARE and TGF-\(\beta\)/Smad pathways were upregulated by Jawoongo, while it reduced phosphorylation activation of the mitogen-activated protein kinase (MAPK)/activator protein 1 (AP-1) pathway. This study shows that Jawoongo regulates the Nrf2/ARE and TGF-\(\beta\)/Smad signaling pathways in UVB-irradiated HaCaT cells, highlighting its potential to protect against and repair skin photodamage.  
Keywords:Jawoongo; anti-photoaging; ultraviolet B (UVB); Nrf2/ARE; TGF-\(\beta\)/Smad +
Footnote †: journal: Applied Sciences
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Paper Selection Process  
With the aim of being as comprehensive as possible, a wide swipe of various digital libraries was carried out: IEEE Xplore, Science Direct, Scopus, Elsevier, Google Scholar. The review is limited to the last six years (2015 to 2020 included), and search expressions comprised various combinations of specific words: resilience, quantification, evaluation, assessment, metrics, indicators, measures, smart grid, distribution network, ICT network, (tele)communication network.  
A first selection step consisted of reviewing abstracts of all found papers (in the order of several hundreds), and shortlisting works which:  
* Analyze the power network at the distribution level, or the ICT network of power network, and;
* Present quantitative analysis of resilience, with the proposed metrics.  
This resulted in a total of 34 pre-selected papers, 10 of which were excluded from this survey as they were recognized after deep analysis to not entirely satisfy the two selection criteria. Thus, the final selection included 24 papers, 18 of them targeting distribution power network [122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139], and 6 for grid ICT network [140; 141; 142; 143; 144; 145]. This set of works was evaluated based on proposed categorizations in Sections 3 and 4.
## 3 ACCA Service
### V2X Information Flow  
There are two phases in the information flow between the different entities of the ACCA service. The first phase performs the authentication and subscription, followed by the exchange of V2X messages in the second phase.  
Figure 2 illustrates the authentication and subscription process between backend services, and between vehicles and backend services. Note that there is no specific order in time in the message flows during authentication and subscription. As can be observed, the Edge Geoserver 1, the Edge Geoserver 2, and the Central Geoserver perform the authentication and subscription with the TMS. Additionally, each Edge Geoserver requires the authentication and subscription with the MQTT Broker within its edge cloud. For the sake of simplicity, only the MQTT Broker that is deployed in the ACCA Edge (MNO2) has been included in Figure 2.  
Each vehicle performs the authentication and subscription process with the respective backend services. The vehicle OEM A communicates with the Edge Geoserver 1 over HTTP(S) in the process of authentication and subscription and, upon success, it is ready to exchange V2X messages over UDP, as can be observed in Figure 2. Similarly, the vehicle OEM B establishes the connectivity and authenticates with the MQTT Broker of the Edge Geoserver 2, and then it publishes onto inQueue to transmit V2X messages and subscribes to outQueue to receive V2X messages.  
Figure 3 illustrates the way that a hazard information is disseminated within the architecture of the ACCA service when an event is detected by a vehicle. The top left part of the message flow illustrated in Figure 3 shows that vehicle OEM A detects an event and immediately transmits a DENM over UDP towards the Edge Geoserver 1. The Edge Geoserver 1 processes the event information, encodes it into a JSON message and then publishes it into the MQTT Broker and the TMS. Subsequently, the event is reached the Central Geoserver and the Edge Geoserver 2. Finally, the vehicle OEM B receives the event information from the Edge Geoserver 2 through the MQTT Broker deployed on the edge cloud of MNO2.  
Similarly, on the bottom right part of the message flow in Figure 3, an event is detected by the vehicle OEM B, which generates a JSON encoded DENM and then publishes onto the MQTT Broker on the edge cloud of MNO2. This information reaches the TMS through the Edge Geoserver 2. Finally, the Edge Geoserver 1 is notified by the TMS and it subsequently transmits over UDP the event information to the vehicle OEM A.
## 4 Experimental Results
Figure 8 provides the BER comparison of \(\mathbf{B}_{s}^{3/5}\) and \(\mathbf{B}_{s}^{regular}\) with different source probabilities. Despite having the same row weight distribution (\(W=[5,5,5]\)), the proposed \(\mathbf{B}_{s}^{3/5}\) performs better than \(\mathbf{B}_{s}^{regular}\) for different source probabilities. For instance, \(\mathbf{B}_{s}^{3/5}\) achieves coding gains in all five different source statistics. Furthermore, it is obvious that \(\mathbf{B}_{s}^{regular}\) has a higher error floor than the proposed code for \(p=0.9\) and \(p=0.91\). In Figure 9, even though \(\mathbf{B}_{s}^{0.94}\) has a slightly better performance in the waterfall region than the proposed code for \(p=0.95\) and \(p=0.96\), the proposed \(\mathbf{B}_{s}^{1/2}\) achieves a significant  
Figure 7: Achievable system rates for the proposed source code \(B_{s}^{1/3}\) (target system rate \(R=9\)). advantage at high \(E_{s}/N_{0}\). Specifically, \(\mathbf{B}_{s}^{1/2}\) obtains 2 dB, 0.59 dB, 0.42 dB, and 0.23 dB gains over \(\mathbf{B}_{s}^{0.94}\) at a BER of \(10^{-4}\) for \(p=0.93\), \(p=0.94\), \(p=0.95\), and \(p=0.96\), respectively. For \(p=0.92\), our proposed code achieves a lower error floor (\(8\times 10^{-4}\)) than \(\mathbf{B}_{s}^{0.94}\) (\(7\times 10^{-3}\)). In Figure 10, it can be found that the source code \(\mathbf{B}_{s}^{1/3}\) outperforms \(\mathbf{B}_{s}^{LEfloor}\) by 1.5 dB and 2.4 dB at a BER of \(10^{-4}\) for \(p=0.97\) and \(p=0.98\), respectively. Figure 8: BER comparison of different source codes (\(R_{s}=3/5\)) with different source probabilities. Figure 9: BER comparison of different source codes (\(R_{s}=1/2\)) with different source probabilities. The above observations imply that the proposed rate-compatible source codes are expected to provide a better system performance for JSCCM systems with varying source probabilities than conventional source codes.
## 5 Performance Evaluation
### Performance Comparison
The execution ratio is 100%, so they are not drawn in Figure 6. The BR-CMCRA algorithm includes Loc + SeV execution mode and Loc + SeV + EdgeV execution mode, and tasks in the BR-CMCRA algorithm tend to be executed in Loc + SeV + EdgeV execution mode because tasks executed in more entities may improve performance. In addition, the two curves of the BR-CMCRA algorithm do not intersect, which means the Loc + SeV + EdgeV execution mode has obvious advantages. Similar to the BR-CMCRA algorithm, the two curves of the LSVLESE algorithm also do not intersect. The LSVLESE algorithm includes Loc + SeV execution mode and Loc + Edge execution mode, and tasks in the algorithm tend to be executed in Loc + SeV execution mode. The number of TaVs that ES can provide computation resources for is constant, and each TaV can be served by a corresponding SeV. Unlike the BR-CMCRA algorithm and the LSVLESE algorithm, the two curves of the LESE algorithm have a point of intersection. The LESE algorithm involves Loc execution mode and Loc + Edge execution mode. When the task number is less than 35, tasks tend to be executed in Loc + Edge mode. ES can provide services for TaVs at this time. However, when the task number is more than 35, tasks tend to be executed in Loc mode because ES's computation resources are insufficient. When the task number is near 35, the ratio of the Loc execution mode is equal to that of the Loc + Edge execution mode. As shown in Figure 7, the lost rate of each execution mode in each algorithm is simulated. The lost rate of the BR-CMCRA algorithm is approximately equal to 0, which means the tasks of all TaVs can be executed within its delay. Compared with the BR-CMCRA algorithm, the LSVLESE algorithm changes Loc + SeV + EdgeV execution mode into Loc + Edge mode, causing the lost rate of the LSVLESE algorithm to be slightly higher than the BR-CMCRA algorithm. Without the help of ES, the lost rate of the LSVE algorithm is higher than the BR-CMCRA and LSVLESE algorithms and ranges from 1% to 2%. The LEA algorithm, which executes tasks locally, has the highest lost rate. In addition, the lost rate of the BR-CMCRA algorithm, LSVLESE algorithm, LSVE algorithm, and LEA algorithm are all relatively stable. On the contrary, the lost rate of the LESE algorithm increases rapidly, and that of the LESE algorithm in Loc + Edge execution modes even exceeds the LEA algorithm. Because ES fails to decrease the task processing time to less than  
Figure 5: The average throughput of each TaV versus the number of TaVs. limited latency, the lost rate of the LESE algorithm mainly comes from Loc + Edge. Similarly, the lost rate of the LSVLESE algorithm mainly comes from the Loc + SeV execution mode.
## 2 Materials and Methods
### Datasets
#### 2.2.4 Meteorological Variables  
The China Meteorological Forcing Dataset (CMFD) is a high spatiotemporal resolution near-surface meteorological dataset designed specifically for land surface processes in China ([[https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)]([https://data.tpdc.ac.cn/](https://data.tpdc.ac.cn/)), accessed on 28 April 2024). It integrates remote sensing products, reanalysis datasets, and in-situ observation data from weather stations, incorporating data from a larger number of stations during the data fusion process. As a result, its quality surpasses that of the Global Land Data Assimilation System. CMFD is one of the most reliable meteorological datasets for China [55]. The present study used the monthly data from 1979 to 2018 provided by the CMFD, including five near-surface meteorological variables: air temperature (Temp, \({}^{\circ}\)C), precipitation (Prec, mm), surface downward shortwave radiation (Srad, W/m\({}^{2}\)), near-surface specific humidity (Shum, kg/kg), and near-surface air pressure (Pres, Pa), with a spatial resolution of 0.1\({}^{\circ}\). Among them, near-surface specific humidity and near-surface air pressure were used to calculate the actual vapor pressure e.  
\[\mathrm{q}=\frac{622\mathrm{e}}{\mathrm{P}-0.37\mathrm{Se}} \tag{1}\]  
where q is the near-surface specific humidity (kg/kg), P is near-surface air pressure (kPa), and e is the actual water vapor pressure (kPa).
# A Model-Based Intelligent Adjustment Method of Toolface for Bent-Housing Motor
## 3 Model-Based Intelligent Method  
The principles of the drill string dynamics model are clear and easy to understand. However, it has made some idealized assumptions and simplifications, mainly including the following:  
1. The dynamics model assumes that the drill string curve is completely consistent with the wellbore curve, but, in reality, there are some sinusoidal and spiral bucklings in the drill string that are not completely consistent with the wellbore curve.
2. The wellbore trajectory used in the calculation process is interpolated through MWD single-point measurement data, which has a certain error with the actual wellbore trajectory.
3. The friction coefficient used in the drill string dynamics model is derived from previous data inversion, and its value is the friction coefficient between the entire drill string and the wellbore. For the assembly of the lower drilling tool, the contact between the drill string and the wellbore is present at several points, such as the bit and stabilizer. However, due to the unique structure of the bit and stabilizer, their friction coefficient with the wellbore is not equal to the friction coefficient between the upper drill string and the wellbore.
4. There are other reasons for the deviation between the model and the actual situation.  
The above reasons make the actual situation at the drilling site not completely consistent with the physical model, and there is a certain deviation between the calculated values of the drill string dynamics model and the actual values. If the drill string dynamics model calculation results are directly applied to guide drilling production, it will be misleading to some extent. Figure 1 shows the toolface deviation between the actual value and the drill string dynamics model calculated value of a two-dimensional horizontal well in the Sichuan Basin of China.  
Artificial intelligence methods can fit the relationship between physical model results and actual drilling site data well. The genetic-algorithm-based back propagation neural network (GA-BP) has a good fitting effect on nonlinear functions affected by multiple parameters. In recent years, it has been widely used in the field of drilling and has achieved satisfactory results.
###### Abstract  
Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and the empirical Bayesian approach for parameter estimation. Moreover, the relationship between the reflectance and the leaf area index was incorporated into the statistical model. Plant growth and ground-based canopy reflectance data of paddy rice were measured at three study sites in South Korea. Spatiotemporal vegetation indices were processed using remotely-sensed data from the RapidEye satellite and the Communication Ocean and Meteorological Satellite (COMS). Solar insulation data were obtained from the Meteorological Imager (MI) sensor of the COMS. Reanalysis of air temperature data was collected from the Korea Local Analysis and Prediction System (KLAPS). We report on a statistical hybrid approach of crop modelling and remote sensing and a method to project spatiotemporal crop growth information. Our study results show that the crop growth values predicted using the hybrid scheme were in statistically acceptable agreement with the corresponding measurements. Simulated yields were not significantly different from the measured yields at \(p=0.883\) in calibration and \(p=0.839\) in validation, according to two-sample \(t\) tests. In a geospatial simulation of yield, no significant difference was found between the simulated and observed mean value at \(p=0.392\) based on a two-sample \(t\) test as well. The fabricated approach allows us to monitor crop growth information and estimate crop-modelling processes using remote sensing data from various platforms and optical sensors with different ground resolutions.  
Keywords: assimilation; crop growth; modelling; remote sensing; simulation +
Footnote †: journal:
# Preparation of Nanoparticle-Loaded Microbubbles via an Electrohydrodynamic Atomization Process
## 3 Results and Discussion
### Effect of Applied Voltage on the Microbubbles  
Voltage is an important factor affecting the preparation of microbubbles by electrohydrodynamic atomization. The effects of applied voltage were studied and analyzed, and the results are shown in Figure 11. During the experiment, we found that microbubbles could not be observed in the collection medium when the voltage was less than 8 kV. With an increase of voltage, a few microbubbles could be gradually observed. When the voltage rose to 11 kV, microbubbles were be prepared stably. Figure 11a shows the variations of the average size of microbubbles with voltage. With increasing of voltage, the size of microbubbles decreased first and then increased. When the voltage reached 12.5 kV, the average diameter of the microbubbles reached 243.19 \(\pm\) 12.53 \(\upmu\)m. According to the theoretical  
Figure 10: Size distributions of microbubbles at different gas/liquid flow rates.  
Figure 9: Variation of normalized microbubble number with time at different gas/liquid flow rates (unit: mL/h/mL/h).  
knowledge of droplet atomization and fragmentation behavior under the condition of electric field polarization, at greater electric field intensity the droplet breaks into smaller droplets. However, there is a polarization threshold in this process. When the electric field intensity exceeds this threshold, the process of breaking into smaller droplets is affected and larger droplets may appear. A voltage of 11\(\sim\)12 kV is suggested as the ideal range under the experimental conditions of \(Q_{l}\) = 10 mL/h, \(Q_{g}\) = 50 mL/h, and \(C_{NP}\) = 1 mg/mL. The polarization threshold of the electric field under the above experimental conditions can be calculated by the formula E\({}_{0}\) = V/H, where E\({}_{0}\) is the polarization threshold of electric field intensity, V is the voltage applied, and H is the working distance of the nozzle (H = 10 cm in this experimental condition). Under these experimental conditions, we found that the average size of microbubbles was 138.76 \(\pm\) 9.76 \(\upmu\)m when the voltage was 11 kV, while it was140.45 \(\pm\) 9.98 \(\upmu\)m when the voltage was12 kV. When the voltage reached 12 kV, the size of microbubbles started to increase. When it reached 12.5 kV, the average size of microbubbles shot up to 243.19 \(\pm\) 12.53 \(\upmu\)m. This indicates that the electric field polarization threshold under this experimental condition is in the range of 1.1-1.2 kV/cm. When the electric field intensity is greater than the polarization threshold, the size of microbubbles may increase significantly with increasing voltage. This suggests that it is not advisable to produce smaller microbubbles simply by increasing the voltage when the polarization threshold is reached. The variation of microbubble number with time at different voltages is shown in Figure 11b. The results show that the stability of microbubbles prepared at 11 kV was better than that at 12 kV and 12.5 kV.