esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
For an aerosol Lidar system that cannot be observed horizontally and does not have a Raman detection channel, a new method of correcting the geometric factor was proposed. The vertical distribution of particulate matter mass concentration detected by the UAV was used to invert the Lidar signal that was not affected by the overlap factor. The inverted signal was compared with the original signal to obtain the required overlap factor. The overlap factor of the 355 nm channel under different weather conditions was obtained from the simultaneous observation of UAV and Lidar. After comparison with the Raman method, it was found that the overlap factor calculated by this method obtains better numerical results. The Lidar's ability to detect aerosols at low altitudes is significantly improved after the overlap factor had been corrected. In summary, this method provides a new way of correcting the overlap factor for Lidar operating in different complex environments.  
C.X.: conceptualization, resources, supervision, funding acquisition; M.Z.: Conceptualization, methodology, software, formal, analysis, investigation, data curation, writing--original draft; J.C.: writing--review & editing; H.Y., Z.F., and L.C.: visualization. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Civil Aerospace Technology Pre-Research Project (D040103), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA17040524), and the Key Program of the 13 th Five-Year Plan, CASHIPS (KP-2019-05).  
Not applicable.  
The data presented in this study are available on request from the author.  
We would like to thank the Key Laboratory of Atmospheric Optics, the Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences for providing funding.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 4 Proposed M-LBS Scheme
### Cell Master Privacy Considerations  
The cell master role introduces potential privacy implications that must be carefully considered. While the cell master collects valuable system statistics, this information could be misused by an adversarial cell master. To mitigate this risk, we implement several privacy-preserving measures.  
* _Limited Data Collection:_ Cell masters only maintain aggregate statistics (node count, query frequencies) without individual user identifiers;
* _Temporal Restriction:_ Historical data are periodically purged to prevent long-term pattern analysis;
* _Pseudonym Rotation:_ Users regularly change their pseudonyms when entering new cells;
* _Distributed Trust:_ Cell master role rotates among nodes to prevent prolonged data accumulation;
* _Query Anonymization:_ Statistics record only query types not specific query content or user locations.  
Even if a cell master becomes adversarial, their ability to compromise user privacy is limited because of the following:  
* They cannot link queries across cells due to pseudonym changes;
* They only see aggregate patterns, not individual behaviors;
* Their view is temporally and spatially limited to their current cell;
* They cannot access the actual content of cached data in other nodes.
# Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning
## 3 Results and Discussion
### Analysis of Classification Results  
#### 3.2.1 Results of Training and Testing  
In order to reduce the error, each of the five SVIT models was trained three times to find optimal one using the validation set. The five models were trained three times for each of the three data sets in Table 5. A total of 45 training results were obtained, as shown in Figure 20.  
The weights with the smallest loss in the validation set among the three results were selected, and the corresponding loss value decline curves were shown in Table 9.  
Figure 19: The prediction results of CP-YOLOX on the original image: (**a–d**) Predictions based on four randomly selected images.  
Figure 20: Loss values of validation sets on different models under three data sets.  
Figure 21 presents the accuracies of 15 optimal models in the test sets. On the SViT-9 model, the 5-categories and 3-categories test sets had the highest accuracy with 63.63% and 75.57%, respectively. On the SViT-6 model, the 4-categories test sets had the highest accuracy with 68.12%. The SViT model predicted the highest accuracy of 75.57% for the 3-categories test sets, corresponding to the categories of crack, poor interlayer bonding, and mixture segregation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **5 Categories Dataset** & **4 Categories Dataset** & **3 Categories Dataset**  
Based on the results in Figure 21, the SViT-6 model was tested in the 4-categories test set, while the SViT-9 models were tested in the 5- and 3-categories test sets. The results are shown in Figure 22. In the three class-membership strategies, the models were the most accurate in classifying pavement distresses without background noises, with HD1 accuracies of 73.3%, 76.4%, and 82.1% and HD45 accuracies of 76.1%, 78.5%, 83.3%, respectively. The models had poor accuracies in predicting distress containing background noises, in which all accuracies were lower than 70%. In horizontal radar images, SViT model was capable of detecting cracks, poor interlayer bonding, and mixture segregation distress. The model was tested well on a 3-category dataset, but the best distress classification strategy needs to be determined along with longitudinal radar images. The future study should focus on how to suppress the noise waveform and on more detailed analysis of the disturbance waveform.
# Multi-Mode Lithium-Ion Battery Balancing Circuit Based on Forward Converter with Resonant Reset
## 2 Balancing Circuit  
### Circuit Structure  
Two forward converters with resonant reset and relay matrix are used for the design of the balancing circuit in this paper. The balancing circuit structure having n cells is shown in Figure 1. The balancing main circuit is improved by [35]. The modular bi-directional forward converters with resonant reset in [35] are improved via the use of two forward converters connected by energy transmission lines. Therefore, the structure of the proposed balancing circuit is symmetrical. By controlling the switching matrix, high-energy batteries and low-energy batteries will be connected to the ports on both sides for energy balancing in the form of cells or clusters. To simplify the control of the balancing circuit, the left side of the balancing main circuit is set as the access port of the high-energy cells; this is also called the energy-sending side. The positive and the negative terminals of the high-energy cells and ports are connected by S\({}_{11}\)-S\({}_{1n}\) and S\({}_{21}\)-S\({}_{2n}\), respectively. The right side of the balancing main circuit is set as the access port of the low-energy cells; this also is called the energy-receiving side. The positive and the negative terminals of the low-energy cells and ports are connected by S\({}_{41}\)-S\({}_{4n}\) and S\({}_{31}\)-S\({}_{3n}\), respectively. Considering the design of the drive circuit and the voltage stress of the components, SPST relays are selected for the switching matrix, while the 74 HC595 is used as its main drive component.  
The balancing main circuit consists of two forward converters with resonant reset and driver circuits. The LC resonant circuit is composed of the primary side excitation inductor of the forward converter and the capacitor, and the forward converter is reset during LC  
Figure 1: Proposed balancing circuit.  
resonance. To reduce the MOSFET switching loss, ZVS is achieved by this structure during resonance. Because no MOSFET drive circuit scheme is given in ref. [35], this paper shows a specific MOSFET drive circuit design: the UCC27524 chip is used for signal driving and the level conversion is completed by the filter capacitor, pull-up resistor, and voltage regulator diode. Therefore, the conduction of MOSFETs is ensured normally. Furthermore, by adjusting the turn ratio of the converters, the larger power transmission of the balancing main circuit is obtained.
# UAV Swarm Real-Time Rerouting by Edge Computing D* Lie Algorithm
## 2 Methods and Materials  
The software in this study was composed of DroneKit, an open source Python library [12]. It can be executed on Linux, Mac OS, or Windows, as long as the flight control board is compatible with the MAVLink protocol [13], which is a very compact messaging protocol widely adapted for drone communication. Three functions were used in this study. The first connected to the flight control board. The second obtained vehicle status information. The third guided the drone to a designated target point. Figure 1 shows the software flow chart. At first, the drone will wait for instructions from the small ground station. When it receives the take-off command, the drone will climb to the specified altitude, and then receive the target point and the longitude and latitude coordinates of the no-fly zone. When it receives the standby command, the drone selects a long flight path and then commences its task.  
Figure 1: System flow chart.  
Figure 2 shows the overall system schematic, consisting of three drones, each equipped with edge computing systems. Each drone is equipped with a Pixhawk 4 as a flight controller and a Jetson Xavier NX embedded computing board for algorithm processing. Communication between drones is achieved using the broadcast function of the XBee wireless transmission module. Apart from the global positioning system (GPS), the drones do not carry any environmental sensors. As shown in Figure 3, the system included a small ground station to simulate a signal for a formation change. The self-developed ground station comprises a laptop computer with an XBee wireless module, which is responsible for flight monitoring and providing the drones with target waypoints and no-fly zone information by GPS coordinates.
## 2 Results and Discussion
### The Influence of Shaking Rate on Curdlan Production  
The 150-rpm shaking rate was more favorable for curdlan production, both by immobilized cells (19.45 g/L) compared to the shaking rates of 110 (11.77 g/L) and 200 rpm (14.60 g/L) and by free cells (14.12 g/L). Control of the shaking rate is an important factor for curdlan production, because suboptimal shaking may not provide adequate oxygen supply for an increase in biomass [17], whereas too much shaking can lead to a stressful environment for a microbial cell [3] (Supplementary Figure S2).  
The yield at 150 rpm was 38.90% for immobilized cells and 28.24% for free cells. There was no statistically-significant difference between the curdlan productions by free and immobilized cells at 200 rpm, which were 15.35 and 14.60 g/L, respectively. For the other treatments, the immobilization provided higher production.  
The shaking rate of 110 rpm provided an unfavorable environment for curdlan production by free cells (approximately 0.58 g/L), possibly due to insufficient aeration. The dissolved oxygen concentration in the production medium is one of the most important factors in curdlan production. When _Agrobacterium_ sp. ATCC 31749 was submitted to various dissolved oxygen levels in a culture environment, changes occurred in the gene transcription levels involved in the tricarboxylic acid cycle (_icd_, _sdh_\(B\) and _mdh_), in lipopolysaccharide-escharide/peptide-glucan biosynthesis (_glm_\(M\)), in the curdlan production route (_gal_\(U\)) and in the electron transfer chain (_cyo_\(A\), _cat_\(D\), and _fix_\(N\)). The translation rates under 50% dissolved oxygen were higher than obtained in 30%, where four of the eight analyzed genes showed no significant change and curdlan production was reduced by approximately 25%. With 75% dissolved oxygen, all genes were repressed, and curdlan production was reduced by half compared to that obtained under 50% oxygen, which showed that gene transcription levels are determined by the concentration of oxygen and not simply its presence or absence [22].
# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
###### Abstract  
Images captured by drones are increasingly used in various fields, including geographic information management. This study evaluates a procedure that incorporates active learning semantic segmentation for verifying the building registration ledger. Several semantic segmentation techniques were evaluated to extract building information, with ResNet identified as the most effective method for accurately recognizing building roofs. Using active learning, the training data were refined by removing instances with low similarity, leading to improved network performance of the model. The procedure was demonstrated to identify discrepancies between the building information system and the inferred label images, as well as to detect labeling errors on a training dataset. Through this research, the geographic information system dataset is enhanced with minimal human oversight, offering significant potential for urban planning and building detection advancements.  
semantic segmentation; UAV; ResNet; map; building; active learning Article
## 3 Methodology and Proposed Model
### Data Pre-Processing
#### 3.2.3 Data Augmentation
We also applied three oversampling techniques, including the Random Oversampling, Synthetic Minority Oversampling Technique (SMOTE) [63], and Adaptive Synthetic Sampling (ADASYN) [64], to balance and augment the IBM data. Our goal is to compare these methods with each other and with the GAN-based method in terms of their effectiveness on the proposed model's performance for employee attrition prediction. Synthetic Minority Oversampling Technique (SMOTE): SMOTE is an oversampling method that generates synthetic data points by using the K-Nearest Neighbors (KNNs) algorithm. In SMOTE, for each underrepresented instance, a certain number of nearest neighbors are found. Then, to create synthetic data points, a subset of minority class instances is chosen at random. Lastly, along the line segments that link the chosen minority examples to their nearest neighbors, new artificial observations are made [65]. Adaptive Synthetic Sampling (ADASYN): ADASYN is an oversampling algorithm that generates synthetic samples by applying a weighted distribution to different minority class instances. The way that ADASYN creates synthetic samples on the line segments between two minority class data points is similar to that of SMOTE. However, ADASYN automatically calculates how many synthetic samples to create for every instance of the minority class based on a density distribution. Therefore, a balanced representation of the data distribution is provided by the expanded dataset [66].
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 4 Our Proposal
### Billing
#### 4.7.1 Generation and Aggregation of the Electricity Billing Ciphertext  
1. Based on \((u_{ij,m},v_{ij,m})\) and the price \(p_{m}\), \(FN_{i}\) can calculate the electricity billing ciphertext \((C_{ij,m})^{p_{m}}=(U_{ij,m},V_{ij,m})=((u_{ij,m})^{p_{m}},(v_{ij,m})^{p_{m}})\). Essentially, \((C_{ij,m})^{p_{m}}\) is the encrypted form of \(c_{ij,m}\cdot p_{m}\).
2. At the end of a billing cycle, \(FN_{i}\) aggregates the electricity billing ciphertext of users at all time slots to obtain the aggregated ciphertext as follows: \(B_{ij}=(U_{ij},V_{ij})=(\prod\limits_{m=1}^{t}U_{ij,m},\,\prod\limits_{m=1}^{ t}V_{ij,m})\).
3. \(FN_{i}\) employs its private key to generate the signature \[\sigma_{FN_{ij}^{\prime}}=H(ID_{FN_{i}}\parallel ID_{SM_{ij}}||U_{ij}\parallel V _{ij}\parallel T_{FN_{i}^{\prime}})^{x_{FN_{i}}}.\]
4. \(FN_{i}\) sends \(\mathcal{T}_{ij}^{\prime}=(ID_{FN_{i}},ID_{SM_{ij}},U_{ij},V_{ij},T_{FN_{i}^{ \prime}},\sigma_{FN_{i}^{\prime}})\) to AC.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 4 Programming and Testing
### Parallel Computing Performance  
Figure 8 demonstrates the time comparison of the general calculation of AES-384 and the parallel calculation of AES-384, while Figure 9 shows the time comparison of the general calculation of AES-512 and the parallel calculation of AES-512 under CTR mode.  
From the above results, it can be seen that in the original normal calculation, when the file size is 1762 bits, the execution time of AES-384 is 18.74 ms, and the execution time of AES-512 is 23.14 ms. With the addition of parallel calculations, it can be accelerated to 0.008927 ms and 0.009378 ms; when the file size is 56384 bits, the execution time of AES-384 is 554.50 ms, and the execution time of AES-512 is 682.62 ms. After a parallel calculation, it can be completed within 0.008239 ms and 0.010082 ms. We know from the experimental results that we can speed up this process by parallelizing the overall computing speed.  
Figure 8: Execution time of normal AES-384 and parallel AES-384.  
Figure 9: Execution time of normal AES-512 and parallel AES-512.
## 3 Multimedia  
Multimedia in Vehicle-to-Everything (V2X) communication is a rapidly evolving field with numerous promising applications. These include in-vehicle infotainment, offering passengers a range of entertainment and information services such as streaming video, gaming, and access to news and weather updates. Additionally, multimedia can enhance advanced driver assistance systems (ADASs), providing drivers with features like lane departure warnings, blind spot monitoring, and adaptive cruise control. Moreover, multimedia can be leveraged for telematics, enabling the collection and transmission of data on vehicle performance, and driving behavior to enhance vehicle safety, fuel efficiency, and maintenance.  
Multimedia in DSRCs can be categorized into broadcasting and multicasting. In broadcasting, the approach involves a single vehicle or infrastructure unit transmitting multimedia content to all other vehicles or infrastructure units within range. Broadcasting is particularly suitable for applications such as real-time traffic information dissemination or emergency alerts. On the other hand, in multicasting, a single vehicle or infrastructure unit transmits multimedia content to a specific group of vehicles or infrastructure units.  
Figure 2: DSRC architecture stack.  
Multicasting is well-suited for applications such as sending personalized messages or providing updates to a group of vehicles.  
The future of multimedia in V2X appears bright, with continued development and investment driving its potential to revolutionize the transportation system. DSRC technology shows promise in improving road safety, traffic management, and various aspects of the transportation system. The integration of multimedia in DSRCs presents further opportunities for enhancing these benefits through a range of innovative applications and services. With ongoing development and investment, DSRCs and multimedia in V2X have the potential to transform the transportation system. Multimedia encompasses the combination of video and audio, as well as image and audio components. This study specifically examines the transmission of video content with or without audio and images with or without audio. Regardless of audio inclusion, given the DSRC bandwidth limitation of 2300 bytes, the primary focus of this research is to evaluate the data sizes that can be transmitted across VANETs, including combinations of video and audio or text and audio.
# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 3 Results
### Brain Tumor Dataset
#### 3.3.2 Photoacoustic Image Segmentation Experimental Results and Analysis  
The segmentation effects of removing artifacts' photoacoustic images and photoacoustic images using YOLOv8n under the same training set are compared in photoacoustic image segmentation experiments. The predicted segmentation labels are compared with ground truth images using pixel accuracy, intersection and union ratios, and Dice coefficients as quantitative metrics of image segmentation quality.  
Figure 5: Removal of artifact images using (**a**) 64, (**b**) 128 and (**c**) 256 sensors in different algorithms. The red box is used to focus the artifact removal effect.  
Figure 6 demonstrates the segmentation effect of the original image MRI, photoacoustic image and the photoacoustic image with artifacts removed on a single image. Evaluating the segmentation results, it can be observed that the segmentation effect of the artifact-removed image is significantly better than that of the photoacoustic image with no artifacts removed. The segmentation labels of the artifact-removed image are highly consistent with the original image labels, with clear morphological edges, presenting excellent segmentation accuracy and precision. On the contrary, the segmentation labels of the image without artifact removal showed missing information and blurred edges, which could not correctly capture the subtle features in the image.  
Table 4 demonstrates the segmentation effect of the brain and brain tumors in photoacoustic images with artifacts versus those with artifacts removed in the test dataset. The results show that the segmentation of the photoacoustic image with artifacts removed is significantly more effective compared to the image with artifacts. Specifically, the artifact-removed image exhibited an overall precision of 0.955, a recall of 0.932, a pixel-level accuracy of 0.986, an intersection and merger ratio of 0.946, an average boundary F1 score of 0.972, and a Dice coefficient of 0.973. In comparison, the corresponding metrics for the image with artifacts were 0.915, 0.852, 0.961, 0.773, 0.863, and 0. 864. In addition, the segmentation assessment metrics for the brain and brain tumors are also broken down in Table 1, and the results show that the photoacoustic images with artifacts removed have improved overall precision, recall, and boundary F1 scores across all categories compared to the images with artifacts.  
Figure 6: Segmentation results of the original image, photoacoustic image and removal of artifacts.