esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### PBD-YOLO Algorithm Architecture  
The structure of the PBD-YOLO algorithm is depicted in Figure 5. The algorithm is based on the lightweight and high-speed YOLOv10 [26] object detection algorithm. PBD-YOLO primarily consists of three components: backbone, neck, and head. The backbone is utilized to extract features from the input image and capture feature representations from low to high levels within the image. The neck network enhances feature representation quality by integrating contextual information through multi-scale feature map fusion. The head network is responsible for the final prediction output, performing classification and regression operations based on the features passed from the neck network to determine the location of the detection boxes and defect categories. PBD-YOLO primarily focused on improvements to the backbone and head components, with the key enhancements outlined below.  
To address the issue of sparse and weak texture features on particleboard surface defects, where conventional feature extraction methods fail to capture the characteristics of certain types of defects effectively, this study introduced the SPDDEConv module. This module supersedes strided convolutions [27] as the down-sampling method in the backbone. The proposed method effectively reduced information loss during down-sampling by spatially partitioning the feature map into smaller parts and stacking these partitioned features in channels. Furthermore, the implementation of differential convolution operations enhanced the preservation and emphasis of edge-related features.  
Figure 4: Data augmentation method.  
What is more, to address the issue of significant scale variations on particleboard surface defects and the dilution of small object features when multi-scale defects co-occur, the C2f_SAC module was proposed by integrating SAC into the C2f module, which expanded the model's receptive field [27]. Concurrently, the head was redesigned, introducing the ShareSepHead module, which employed convolutional layers with shared weights to amalgamate knowledge from feature maps of varying sizes from the neck, compelling the network to learn a common feature representation across scales.
# A Review of Research on Secure Aggregation for Federated Learning
## 3 Privacy-Preserving Mechanisms
### Differential Privacy
The Gaussian mechanism produces smoother noise compared to the Laplace mechanism, but requires careful calibration of the sensitivity parameter. The exponential mechanism is suitable for discrete selection problems and is often applied in scenarios involving categorical outputs or limited candidate sets. However, it requires the design of a well-defined quality function to guide the selection process.
## 8 Comparisons of Calculation Efficiency  
Table 5 shows the comparisons of the calculation efficiency of our scheme and the schemes of [51; 53].  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Schemes** & **IBBE of [53]** & **CLR-IBBE of [51]** & **Our Scheme** \\ \hline Private key size & 6\(\theta\) & \(3(n+2)\lambda\) & \(12\theta\) \\ Leakage amount of \(SK\) & \(\times\) & \((n-2\Lambda-1)\lambda\) & \(4(1-2\Lambda)\theta\) \\ Storage requirement & 2 & \(n+2\) & \(4\) \\ Private updation & \(\times\) & \(\surd\) & \(\surd\) \\ Leakage rate of \(SK\) & 0 & \(\frac{(n-2\Lambda-1)}{3(n+2)}\) & \(\frac{4(1-2\Lambda)}{12}\approx\frac{1}{3}\) \\ CLR & \(\times\) & \(\surd\) & \(\surd\) \\ Split-state & \(\times\) & \(\times\) & \(\surd\) \\ \hline Anonymity & \(\surd\) & \(\times\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Some comparisons related to the proposed scheme and some related schemes given in [51; 53].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Schemes** & **Initialization** & **Private Generation** & **Private Updation** & **Encryption** & **Decryption** \\ \hline
[53] & \(E+P\) & \((d+2)E\) & \(\times\) & \((d+3)E\) & \(2P\) \\
[51] & \((4n+3m+5)E+P\) & \((3n+2d+4)E\) & \((3n+d+5)E\) & \((n+d+2)E\) & \((n+2)P\) \\ Our scheme & \(E+P\) & \((d+4)E\) & \(4E\) & \((d+3)E\) & \(4P\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of the calculation efficiency of our scheme and the schemes of [51; 53].  
The number of main operations (pairing operation and group exponent operation) is listed in Table 5. \(P\) indicates pairing operation. \(E\) indicates group exponent operation. \(m\) is the max value of the users in the system. \(d\) denotes the count of those users in some broadcast. The calculation efficiency of each operation of this presented scheme in this paper is better than that of the scheme in [51]. The encryption and decryption calculation efficiency of our scheme is as good as that of [53], and the efficiency is higher than that of the scheme in [51]. Since the private key is divided into two states, the scheme in this paper has two more exponential operations than the scheme in [53] for the private key generation algorithm. In addition, since the decryption is divided into two stages, the scheme in this paper has two more pairing operations than the scheme in [53].
# On the Track to Application Architectures in Public Transport Service Companies
## 4 Current State of Use-Cases from Different Transportation Service Providers
### Use Case--Passenger Counting at BVB
Given the limiting hardware capabilities coupled with the ARM architecture of the Coral Dev Board, optimization of any additional components required had to be performed over several iterations, with consideration given to both of these factors. For example, the AImodel for object detection was only executed at a pre-defined frame interval. In between, a less resource-intensive correlation tracker from the DLIB library was utilized. Finally, a simple Centroid Tracker was applied for the re-identification of the objects. This tracker compares the transmitted coordinates and measures on the basis of the Euclidean distance whether it is likely that the object identified in the last frame has made such a movement. Each of the previously mentioned features as well as their partly complex parameters such as the camera placement had to be tested and evaluated in the vehicle as illustrated in Figure 4.  
Both, common and unique scenarios of passenger movements into and out of the vehicle were studied. This includes individual boarding as well as boarding in larger groups, varying complexity and velocity. With such an agile oriented methodology, a simple but quite effective passenger counter could be developed in a relatively short time. The final comparison between the AI and the infrared system demonstrated promising results even without domain-specific training of the AI-based object recognition model. Nevertheless, it should also be noted that the system in its current design was not fully capable to beat the existing counting system in terms of accuracy.
## 2 Methods
### Radiomics Workflow  
Radiomics is a quantitative approach for extracting textural information from medical imaging, which can be used by machine learning algorithms to aid clinical decision-making. To develop a machine learning-based CT radiomics model that can differentiate between benign and malignant solid renal tumors, we started by extracting various types of radiomic features from each CT scan. Then, we randomly divided the dataset in a stratified manner as follows: 75% as the training set and 25% as the test set. In the case of developing a machine learning model for multi-phase CT radiomic features, only those participants with single-phase and two-phase CT scans were assigned to the test set. Only the training set was used for feature selection and model training with 10-fold cross-validation. We tested the following four most popular machine learning algorithms: Linear Support Vector Machine (Linear SVM), Radial basis function Support Vector Machine (Rbf SVM), Random Forest, and XGBoost [9]. We conducted hyperparameter tuning through 200 trials of random hyperparameter search for classification of malignant kidney cancer with optuna (version 3.1.0) [10]. The relevant codes are freely available for reproducibility ([[https://github.com/Transconnectome/Kidney_Radiomics](https://github.com/Transconnectome/Kidney_Radiomics)]([https://github.com/Transconnectome/Kidney_Radiomics](https://github.com/Transconnectome/Kidney_Radiomics)) accessed on 30 August 2023). Given the case-control imbalance as a result of a larger number of patients diagnosed with malignant kidney tumors than the number of patients diagnosed with benign kidney tumors, as well as a higher proportion of patients having a specific kidney cancer subtype (i.e., clear cell renal cell carcinoma) than the proportion of those having other subtypes, we implemented the Synthetic Minority Over-sampling Technique (SMOTE) with imbalanced-learn (version 0.10.1) [11] during the model evaluation-based feature selection and development phases. The hold-out test set was only used for evaluating the final performance. Feature selection, machine learning model development, and model evaluation processes were implemented along with scikit-learn (version 1.2.1) [12] using Python 3.10.8.
# Brief Review of Recent Developments in Fiber Lasers
## 6 Fiber Laser Applications  
At present, fiber lasers are used widely in both science and industry. As mentioned above, in the 1990s, fiber lasers came into wide use in the realm of the telecommunications industry, providing high-quality single-mode low-power systems. Fiber lasers in fiber telecommunications are mainly used as light sources. Since fiber lasers have the characteristics of high efficiency, stability, and good modulation performance, they can provide stable and high-quality optical signals.  
With the collapse of the telecommunications markets in the early 2000s, fiber laser producers shifted their focus to meeting the needs of industrial material processing, and of military and medical sectors. This shift to alternative markets required higher-power lasers. One particular advantage of fiber lasers is that they are inherently simple and can therefore provide easily manufacturable systems to meet a range of power levels and output quality.
## 3 Map Construction Method
### Lane-Level Network Construction  
The lane-level road network is established based on the road-level network. The constructed road centerline is used as the linear reference network for generating the lane-level network. Based on the reference network, the geographic locations of lane-level data are generated and stored using a linear reference approach for the construction of lane-level road networks.  
For data storage in GIS, the road data where each lane is located, the measured value of the lane start points and terminal points on the road, and the offset distance of the lane  
Figure 5: Determination of the intersection area schematic. (**a**) Intersection boundary points; (**b**) Intersection scope.  
Figure 6: Centerline construction in different types of triangles. (**a**) Type I; (**b**) Type II; (**c**) Type III; (**d**) Centerline construction.  
relative to the road will be recorded. The measured value indicates the distance from the start point of the reference line to a certain point along the direction of the reference line (as the distance from \(P_{5}\) to \(P_{6}\) in Figure 7), and the offset distance indicates the vertical distance from a certain point to the reference line, as shown in Figure 7 (the distance from \(P_{6}\) to \(P_{8}\)). For a four-lane road with two directions, the right lane of the road centerline travels in the same direction as the road centerline, with a positive offset distance relative to the road centerline (as the o in Figure 7), while the left lane travels in the opposite direction, with a negative offset distance (as the -o in Figure 7). The specific calculation is expressed in Equation (10).  
Here, the four points on the road edge (as \(P_{1},\ P_{2},\ P_{3},P_{4}\) in Figure 7) and the two points on the reference line (as \(P_{5},\ P_{6}\) in Figure 7) are called shape feature points. Each shape feature point has a corresponding reference value \((m,o)\) in the linear reference system based on the road centerlines. According to the shape feature points of the road, the start point and the terminal point of each lane (shown as \(p_{a},\ p_{b},\ p_{c},p_{d}\) in Figure 7) can be identified.  
\[\begin{array}{l}m_{i}=\pm(m_{k1}+m_{k2})/2\\ o_{i}=\pm(o_{k1}+o_{k2})/2\end{array} \tag{10}\]  
where \(m_{i}\) is the measured value of the lane start point and terminal point \(p_{i}\), \(o_{i}\) is the offset distance of lane start points and terminal point \(p_{i}\), \(m_{k1},\ m_{k2}\) is the measured value of two adjacent shape points (e.g., \(P_{1},\ P_{5}\) in Figure 7) along the vertical direction of the reference line, and \(o_{k1},o_{k2}\) is the offset distance of two adjacent shape points (e.g., \(P_{1},\ P_{5}\) in Figure 7) along the vertical direction of the reference line. Usually, the offset value of the reference line is set to 0.
# Software-Defined Small Cell-Linked Vehicular Networks: Architecture and Evaluation
## 4 Implementation of the V2x Data Dissemination over diSRsVN and Its Results
### Simulation Scenario
#### 4.2.1 Randomly Source and Destination Vehicle Node  
The vehicle placed based on the sumo traffic models the vehicle speed, and the vehicle starting position in the simulation is based on the SUMO configuration file. Randomly, we select the source and destination. The source node sends the hello (beacon) message to all nearest nodes. If the source in the local controller is selected, directly communicate with the local controller, or select the forward nodes by exchanging the topology discovery messages. If the small cell RSU controller is busy, send the request to the SDN controller and make the route selection and packet transmission between the source and destination.
# On-Board Parameter Optimization for Space-Based Infrared Air Vehicle Detection Based on ADS-B Data
## 3 Results and Discussion
### On-Board Spectral Band Optimization
In summary, the performance of the proposed method had a significant improvement compared with that of the traditional fixed-parameter system; especially in the cirrus cloud background, which had the greatest impact on AVD, the improvement was up to four times. Based on the improvement and maximization of \(JSNCR\) in the cruising phase, the on-board spectral band and parameter optimization process was carried out so that the  
Figure 11: Performance improvement for cumulus cloud, alidestratus cloud and cirrus cloud backgrounds (daytime). (**a**) The straight line represents \(JSNCR\) of the fixed-parameter system, and the straight line with circles represents \(JSNCR\) after the proposed on-board spectral band optimization algorithm; (**b**) the straight line with dots represents \(JSNCR\) after the proposed on-board spectral band optimization algorithm, the straight line with diamonds represents \(JSNCR\) after the proposed on-board spectral band optimization and on-board parameter optimization algorithm. Figure 10: The maximum integration time during the cruising phase. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{\(JSNCR\)} \\ \hline
**Background** & **Fixed Parameter** & \
\begin{tabular}{c} **Proposed On-Board Spectral** \\ **Band Optimization** \\ \end{tabular} &
\begin{tabular}{c} **Proposed On-Board Spectral Band Optimization** \\ **and On-Board Parameter Optimization** \\ \end{tabular} \\ \hline Cirrus cloud & 5.66 & 22.02 & 22.68 \\ \hline Cumulus cloud & 56.67 & 66.49–67.83 & 76.9–78.47 \\ \hline Altostratus cloud & 50.21 & 88.37–90.42 & 115.88–118.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The optimization result of \(JSNCR\) performance. \(JSNCR\) was kept at a high level, and realized high-quality feature extraction for further target recognition. At the same time, compared with ground-based and airborne platforms, the advantage of the space-based AVD system is realizing multi-target real-time awareness and wider coverage. The high performance of target detection and tracking algorithms is very dependent on the raw infrared image signal-to-noise ratio [36]. The method proposed in this paper can be used for target detection and support for performance improvements in target tracking algorithms such as YOLOv5 [27].
# Optical Design of a Miniaturized Airborne Push-Broom Spectrometer
## 2 System Specification
### Optical System Parameters
#### 2.2.2 Focal Length  
The focal length (\(f\) ') of the optical system is related to the sensor's pixel size (\(p\)), the Ground Sample Distance (\(GSD\)), and the flying altitude (\(H\)), as shown in Equation (2):  
\[f^{\prime}=\frac{H\cdot p}{GSD}. \tag{2}\]  
After calculation, the focal length is 13 mm when the altitude is 1 km.
## 1 Introduction  
During the last decades, there has been an important growth in the world population, which has generated a great industrial expansion and a considerable technological development that has led to an increase in the presence of substances in the environment that can cause harmful effects on living beings [1]. Among these substances are the so-called emerging pollutants or pollutants of emerging concern, substances that are not regulated or controlled by national or international environmental authorities, whose presence in the environment is not necessarily new but about which there is growing concern for their potential effects on human health and ecosystems [2, 3]. They include compounds as varied in nature as pharmaceuticals, lifestyle products, drugs of abuse, personal care products,steroids and hormones, industrial additives or flame retardants [4], and their presence has been detected in different aqueous media and in a wide variety of concentrations [5].  
Among personal care products, parabens (p-hydroxybenzoic acid alkyl and aryl esters) are widely used as preservatives in the pharmaceutical, food and cosmetic industries due to their high antibacterial and antifungal activity, stability over a wide pH range and low price [5; 6], although they have been linked to various health problems, mainly breast cancer and reproductive disorders [7].  
Methylparaben (MPB) is the most widely used paraben in commercial applications and, consequently, is the most frequently detected one in surface waters, at levels ranging from ng/L to ug/L [8].  
Different methods have been described to remove methylparaben from aqueous media, including biodegradation [8], advanced oxidation processes [9; 10; 11], adsorption [12; 13], membrane processes [14; 15; 16], constructed wetland systems [17] and electrochemical and sonoelectrochemical processes [18; 19].  
Adsorption is a widely recognized method for the removal of all types of pollutants from aqueous solutions due to its simplicity, economy and efficiency [20; 21].  
Although activated carbon is the adsorbent traditionally used to remove pollutants from aqueous solutions due to its large pore surface (with a controllable pore size), wide variety of surface functional groups, high efficiency, high adsorption rate and easy regeneration [22; 23], research on the possibilities of using adsorbents derived from agrifood wastes has increased significantly in recent years [24; 25; 26].  
In this paper, a comparative study of the removal of methylparaben from its aqueous solutions by adsorption onto activated carbon and activated olive stones is carried out, analyzing comparatively the efficiency of both processes, the equilibrium and kinetics models that best describe each of them and the mechanism of the adsorption process, identifying, for each adsorbent, the rate-controlling step of the global adsorption process.  
In addition, the first results obtained in the study of the effect that the addition of graphene-based nanomaterials (graphene oxide and reduced graphene oxide) to both adsorbents has on the removal efficiency of methylparaben are also described and analyzed.
## 4 Numerical Simulations
### Heading Control Simulations  
To reflect the anti-interference performance of the LADRC and LADRC-TD, 5 NM torque was added to the AUH as a disturbance, with a holding time of 4 s and a cycle of 20 s. In order to simulate the impact of the underwater turbulence on the AUH, a torque was applied that considered the asymmetry of the components outside the AUH shell, such as USBL and Iradia. However, it should be noted that the turbulence was not expected to be significant. Therefore, a torque with a reasonable amplitude of 5 NM was selected for this simulation.  
As presented in Figure 8a, a sinusoidal signal is used as the system input, aiming at testing the tracking performance of each algorithm. Moreover, PID exhibits large fluctuations in the heading tracking when under disturbance, while LADRC and LADRC-TD have small fluctuations, proving their certain anti-interference abilities. According to Figure 8b,c, both PID and LADRC show an overshoot under the step response. Although LADRC-TD has a slightly lower response speed, it shows no overshoot. Simultaneously, PID also exhibits an obvious fluctuation when under disturbance, and LADRC and LADRC-TD have similar performances, as shown in Figure 8a.  
Figure 6: Block diagram of LADRC-TD.  
Figure 7: Control flow of the AUH.
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 2 Materials and Methods
### Software Infrastructure  
In our previous study, we implemented a virtual fence application [22] whose main task was to create virtual fences using a remote PC. This allowed us to view, monitor and track the behavior and location of goats through a wireless network in real-time and determine the deviated position of animals in the reception area. We were able to add the current GPS error correction method to the virtual fence application.  
We did not limit our GPS error filter method to only specific types of animals, for example, goats; we were able to add a feature that makes our method available for all types of animals. Using our virtual fence applications, farmers can now easily choose their animal types and include a GPS error filter function for them.
## 4 Free-Space and On-Body Link Budget
### Measurement Setup and Procedure  
The proposed dual-mode antenna's link budget was measured in the free-space and on-/off body modes in an open space environment. We performed path loss measurements at different frequencies, considering the lower band of 2.2 GHz and the upper band of 5.6 GHz, using two meander line monopole antennas. The measurements were performed for the line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios at different distances (\(r\)) and locations between the transmitting (Tx) and receiving (Rx) antennas. The measurement environment had a width of approximately 20 m. The receiving antenna was connected to an MS2720T spectrum analyzer that worked at zero spans. The Tx signal contained a single synthesized sweeper. The Tx was fixed, while the Rx was moved between 2 m to 11 m distances to analyze the path loss from measuring the transmission coefficient (S\({}_{21}\)) in the free-space and the on-body communication scenarios. It was ensured that all the surrounding objects were at a stationary state, and it was confirmed that the measurement was carried out accurately. The measurement setup including the cables was calibrated and a two-port calibration (without antennas) was performed to remove the ripple effects from the cables. Figure 12 shows the equipment setup for the path loss measurement.