esa-sceva/satcom-chunk-collection huggingface dataset:
# Prototypical Graph Contrastive Learning for Recommendation
## 4 Experiments
### Experimental Settings
#### 4.1.2 Baselines  
Selecting popular baselines from the corresponding fields as comparison models is involved in the construction according to ProtoRec:  
Graph-based collaborative filtering methods.  
* LightGCN [4] is the primary choice for the graph convolutional network backbone in current recommendation models, capable of capturing collaborative information in interaction graphs in a lightweight manner.
* DirectAU [23] optimizes embedding alignment and uniformity through a loss function and theoretically reveals the rationality of this approach for optimizing recommendation performance.  
Self-supervised learning methods.  
* LightGCL [12] constructs perturbed views through random singular value decomposition and employs a dual-view approach for CL.
* SGL [5] proposes random strategies to drop nodes and edges in the graph for CL.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **\#Users** & **\#Items** & **\#Interactions** & **Density** \\ \hline Yelp2018 & 31,668 & 38,048 & 1,561,406 & 0.00130 \\ Douban-Book & 13,024 & 22,347 & 792,062 & 0.00272 \\ Amazon-Book & 52,463 & 91,599 & 2,984,108 & 0.00062 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information about all datasets. The symbol “#” represents the total number of entities (users, items, and interactions) in the dataset.  
SimGCL [10] introduces a feature augmentation method constructed by adding uniform noise to learned embeddings.
* SCCF [24] decomposes the contrastive learning loss into two processes, making embeddings more compact and more dispersed, thereby enhancing the ability of contrastive learning to capture high-order connectivity information.
* RecDCL [25] integrates batch-wise contrastive learning to enhance the robustness of representations and feature-wise contrastive learning to eliminate redundant solutions in user-item positive samples, thereby optimizing the uniformity of embeddings.
* AdaGCL [26] dynamically reconstructs the interaction graph and denoises the data based on the learned embeddings, and it constructs contrastive learning using the embeddings learned from both processes.  
Prototype-based recommendation methods.
* ProtoAU [21] utilizes prototypes to optimize the alignment and uniformity of embeddings and constructs contrastive learning across different views.
* NCL [20] constructs contrastive schemes in both semantic and structural aspects based on extracted prototypes.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 3 Results
### Variation of Nominal Refractive Index Structure
#### 3.1.3 Impact on Scintillation Index  
Another important parameter in FSO link design is the scintillation index since it is used to quantify the amount of scintillation caused by atmospheric turbulence. Figure 6 illustrates the variation in the scintillation index with respect to ground-level refractive index structure value for different wavelengths and ground\(-\)satellite distances in an uplink channel.  
Figure 4: Variation in the Rytov variance in an uplink channel.  
Figure 5: Variation in the Rytov variance in a downlink channel.  
channel. Figure 7 illustrates the variation in the scintillation index with respect to ground-level refractive index structure values for different wavelengths and elevation angles in a downlink channel. Similar to Rytov variance, Figures 6 and 7 illustrate that the value of the scintillation index for a specific wavelength is virtually constant for smaller refractive index structure value at ground level, i.e., \(\text{C}_{\text{n}}{}^{2}(0)\) less than \(10^{-14}\) m\({}^{-2/3}\) in both uplink and downlink channels.  
A value of \(\text{C}_{\text{n}}{}^{2}(0)\) less than \(10^{-14}\) m\({}^{-2/3}\) depicts a weak turbulence scenario where the effect of atmospheric turbulence has a significantly lesser impact on FSO satellite  
Figure 6: Variation in the scintillation index in an uplink channel.  
Figure 7: Variation in the scintillation index in a downlink channel.  
ground and ground-satellite channels. Figures 6 and 7 further verify the suitability of longer wavelengths for FSO channels because the decrease in scintillation index is clearly shown with a higher value of wavelengths. Hence, to reduce the scintillation effect, the 1550-nanometer operating wavelength should be used for both satellite-ground and ground-satellite channels.  
The elevation angle also plays a vital role in determining the scintillation strength in a downlink channel. For the same wavelength, elevation angles with higher values will have higher scintillation strengths compared to smaller elevation angles. For the 1550-nanometer wavelength, the scintillation index at \(10^{-13}\) m\({}^{-2/3}\) C\({}_{\rm m}\)\({}^{2}\)(0) for 75 degrees elevation angle was almost 1, whereas, for a 55 degrees elevation angle, the scintillation index was about 0.4. Hence, smaller elevation angles are preferred in order to reduce the effect of scintillation on a downlink channel.
# Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia
## 3 Materials and Methods
### Land Cover Management Factor (C)  
The value of C factor depends on plants characteristics related to vegetation type, stage of growth and cover percentage [50]. For the Tunisian context, the C value has been defined for different plant covers [27]. In RUSLE model, C factor means the ability of vegetation cover to reduce soil erosion. It ranges from zero (total protection) to one (no protection). Values are assigned from RUSLE tables or others based on plots experimental studies. In this study, we performed a supervised classification of the 30 m resolution Landsat image (downloadable from USGS Open Access Hub ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), last accessed on 28 May 2021) to extract a land-use map. C values were then assigned according to the tables used in regional and international studies [29; 30].
## 2 Materials and Methods  
The experimental setup of HHG is shown in Figure 1. The infrared (IR) laser beam, which has a diameter of 40 mm, is adjusted by a diaphragm with a diameter of approximately 17 mm, and loosely focused by a plano-convex lens, which has a focal length of approximately 5000 mm and is mounted on a motorized translation stage. The spot diameter around the laser focus had been measured to be \(\sim\)300 \(\upmu\)m and the confocal parameter \(b=2z_{R}\approx 180\) mm, where \(z_{R}\) is the Rayleigh length. Then, the focused IR pulse is guided into the target chamber through a CaF\({}_{2}\) window mounted at the Brewster's angle of the IR pulses to avoid light reflection, and interacted with the argon gas in a gas cell that is located around the laser focus. The length of the gas cell can be adjusted from 0 to 150 mm. The generated harmonics, propagating with the residual IR pulses, subsequently pass through an iris diaphragm (a diameter, \(\sim\)4 mm), which partially blocks the IR pulses, and then are delivered into the measuring section.  
In the measuring section, a gold-coated spherical mirror (SM), mounted on a motorized translation stage is used to send the XUV beam either into the flat-field grating spectrometer equipped with an XUV charge-coupled device (XUV-CCD, Andor iKon-L, Oxford Instruments plc, Abingdon, UK) camera for spectral measurement or into an XUV photodiode detector (AXUV100G, Opto Diode Corp, Camarillo, CA, USA) for absolute energy measurement. Specifically, when the SM is inserted into the beam path, the harmonics are reflected by the SM, passing through a slit, and then diffracted by a flat-field grating. The spectrally resolved far-field high-order harmonics are detected by the XUV-CCD camera. On the other hand, when the SM is removed from the beam path, the harmonics are directly reflected by the silicon mirrors, and then illuminating the photodiode detector.
# Simulation Analysis and Experimental Verification of the
## 5 Experiment  
### Locking Force Experiment  
On the basis of the above analysis, in order to obtain the real and reliable locking torque value, the locking force of the MP was tested, and the test device is shown in Figure 12. The whole test device includes a data acquisition system, a charge bridge, eight pressure sensors and a computer, in which the force sensor is connected by a quarter bridge.  
The torque wrenches were used to tighten the L/R mechanism so as to obtain real-time torque values, and the loading position is as shown in Figure 12. During loading, eight lead screws of the L/R mechanism on both sides were extended to lock the MP at the same time, and eight groups of real-time pressure signals were detected through the pressure sensor. The locations of each sensor are shown in Figure 12. The pressure signals collected by the force sensor were converted by Wheatstone Bridge Circuit [17; 18], and then  
Figure 11: Error of locking force.  
Figure 10: The locking process contains no sensors. (**a**) The time history curve of locking force. (**b**) The time history curve of motor torque.  
Figure 9: The locking process contains sensors. (**a**) The time history curve of locking force. (**b**) The time history curve of motor torque.  
acquired by the data acquisition system, so as to obtain the real-time locking force value. Through data feedback, the relationship between torques and locking forces is shown in Figure 13. When the locking torques reach 25 Nm, the locking forces at the eight locking points exceed 2000 N.
## 1 Introduction
### Contributions of This Paper  
The key to propagation characteristics comparisons between mmWave and VLC bands requires the same conditions. In our work, the comparisons are made based on the same conference scenario. The contributions of this paper are as follows:  
* Channel characteristics comparisons between mmWave and VLC bands are performed based on the same conference scenario and parameter settings.
* A unique optical path loss (OPL) model dependent on the physical size of photodetectors (PDs) is first proposed for VLC based on the widely used floating-intercept (FI) model in mmWave. The size of PDs can be estimated to meet the required coverage range by using this model while designing systems.
* The large-scale fading characteristics and multipath-related characteristics, including root mean square (RMS) delay spread (DS), K-factor, and cluster characteristics, between mmWave and VLC bands are compared fairly based on the same scenario.  
The rest of this paper is organized as follows. Section 2 introduces the scenarios and parameter settings for mmWave and VLC, respectively. Propagation characteristics comparisons are presented in Section 3. Finally, Section 4 concludes this paper.
# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 6 Alexis Team
### Methodology  
[PERSON] team selected the single-stage object detector YOLOv3 [39] because of its good trade-off between speed and accuracy. In this section, its architecture, implementation, and training are discussed.  
In YOLOv3, predictions are made at 3 different scales (1/32, 1/16 and 1/8 of the input image resolution). For each scale, it uses 3 predefined anchors for which they selected the default sizes of (10 \(\times\) 13), (16 \(\times\) 30), (33 \(\times\) 23), (30 \(\times\) 61), (62 \(\times\) 45), (59 \(\times\) 119), (116 \(\times\) 90), (156 \(\times\) 198) and (373 \(\times\) 326) pixels. For each anchor, the network regresses the bounding box location/dimensions and predicts an objectness score (class-agnostic) along with class-specific scores.  
[PERSON] team leveraged the public PyTorch implementation of YOLOv3 with Spatial Pyramid Pooling (YOLOv3-SPP) made available by Ultralytics [47]. Spatial Pyramid Pooling [48] is a simple technique for which the input features are processed by pooling layers of different sizes in parallel and then concatenated to generate fixed-length feature vectors. For SPP employed in combination with YOLO [49], the original feature map resolution is kept by applying max pooling with appropriate padding. Output feature  
Figure 17: Examples of ESRGAN in given Dataset. The top row indicates original image, the bottom indicates respective GAN images.  
maps are then all concatenated together along with the input. In YOLOv3-SPP, the SPP layer is added between the end of the backbone and the YOLO classification/regression layers.  
[PERSON] team used YOLOv3-SPP pretrained on the Microsoft Coco dataset [50]. Then, the network was trained with Stochastic Gradient Descent (SGD) for 5 epochs with a learning rate of 0.001 (multiplied by a factor of 0.5 after epochs 3 and 4) and mini-batches of 24 images (when using a training resolution of 480 px). The network was trained using a single class (drone) and was optimized for objectness (using the binary cross-entropy loss) and bounding box regression (using the complete Intersection loss [51] instead of the sum of squared error loss proposed in YOLOv3 [39]). Figure 18 shows examples of successful detections made by [PERSON] team approach.
## 7 Defenses against Privacy Attacks
### Differential Privacy  
Differential privacy [177] provides a robust framework for safeguarding privacy in FL by injecting random noise into model updates, such as gradients, before they are transmitted to the central server [178]. This noise, calibrated to the sensitivity of the data, ensures that individual data points cannot be discerned from the updates, effectively preventing inference attacks [179]. By maintaining confidentiality regarding data contributions, even in collaborative learning settings, differential privacy enables a controlled balance between data privacy and model accuracy. A block illustration of this technique is provided in Figure 20.  
For example, [PERSON] et al. [177] introduced NbAFL (\"noising before model aggregation FL\"), leveraging differential privacy to enhance privacy preservation in FL. Clients perturb their locally trained model parameters with Gaussian noise before uploading to the server, ensuring that sensitive information remains obfuscated during transmission. The server aggregates these noisy parameters into a global model, potentially adding further noise based on privacy budget constraints. Theoretical analyses validate NbAFL's differential privacy guarantees and demonstrate its utility in maintaining model performance across varying privacy levels and client configurations.  
Additionally, [PERSON] et al. [180] applied differential privacy to address adversarial examples in deep neural networks through PixelDP. By treating each input example as  
Figure 20: Visual representation of differential privacy.  
a database protected by differential privacy, PixelDP adds noise based on sensitivity at the neural network layer, thereby ensuring that differential privacy holds for the entire network's outputs. This approach provides certified robustness against adversarial attacks, limiting the impact of minor input changes on the network predictions and bolstering security in complex real-world datasets.
# Performance Analysis of Continuous-Variable Quantum Key Distribution with Multi-Core Fiber
## 3 Performance Analysis
For the MDI CVQKD protocol, the secret key rate under Gaussian attacks is given by [16]  
\[R=g\left(\frac{\tau_{A}\chi}{\tau_{A}+\tau_{B}}-1\right)-g\left[\frac{\tau_{A} \tau_{B}\chi-(\tau_{A}+\tau_{B})^{2}}{|\tau_{A}-\tau_{B}|(\tau_{A}+\tau_{B})} \right]+log_{2}\left[\frac{2(\tau_{A}+\tau_{B})}{e|\tau_{A}-\tau_{B}|\chi} \right], \tag{7}\]  
where \(g(x)=\frac{x+1}{2}log_{2}\frac{x+1}{2}-\frac{x-1}{2}log_{2}\frac{x-1}{2}\), \(\tau_{A}=\eta_{F}10^{-\alpha L_{A}/10}\), \(\tau_{B}=\eta_{F}10^{-\alpha L_{B}/10}\), and \(\chi=2(\tau_{A}+\tau_{B})/\tau_{A}\tau_{B}+\epsilon_{0}\). \(L_{A}\) is the distance between [PERSON] and [PERSON], and \(L_{B}\) is the distance between [PERSON] and [PERSON]. However, we could not obtain a positive secret key rate for \(\eta_{F}=1.1\) dB. The reason is that the insert loss of 1.1 dB is equivalent to an extra distance of 5.5 km, which is equivalent to add 5.5 km initial distance between [PERSON] and [PERSON]. Unfortunately, the secure distance of the MDI CVQKD protocol will decrease rapidly with the distance between [PERSON] and [PERSON] [16], which prevents us to obtain a positive secret key rate for \(\eta_{F}=1.1\) dB. Furthermore, although \(\eta_{F}\) is set to 0.5 dB or 0.1 dB, the secure transmission distance is also short with \(L_{A}\simeq 0\), as shown in Figure 6, where the reconciliation efficiency is set to 1. In practice. The results in Figure 6 may become even worse since the reconciliation efficiency is not able to achieve 1. We also show the tolerable insert loss of the FIFO for the MDI scheme in Figure 7, when fixing the distance between [PERSON] and [PERSON] for difference values. It shows that the secure distance between [PERSON] and [PERSON] is able to reach a longer value of up to 50 km when \(\eta_{F}\) is low enough. It also shows the shorter \(L_{A}\) is, the larger toleration of \(\eta_{F}\) can be achieved. Figure 6: The secret key rate of one core for the multi-core-based MDI CVQKD protocol. (**a**) \(\eta_{F}=0.5\) dB. (**b**) \(\eta_{F}=0.1\) dB. \(\eta_{F}\) is the insert loss of the FIFO. [PERSON] and [PERSON]’s modulation are much larger than 1. \(L_{A}\) is the distance between [PERSON] and [PERSON]. \(L_{B}\) is the distance between [PERSON] and [PERSON]. Figure 7: The tolerable insert loss of FIFO when fixing the distance between [PERSON] and [PERSON]. The black solid line represents the result of \(L_{A}=0.1\) km. The blue dashed line represents the result of \(L_{A}=0.1\) km. The red dotted line represents the result of \(L_{A}=0.01\) km.
###### Abstract
In general, the dynamic behavior of particle/aerosol has been predicted with the discrete particle method (DPM) and volume-tracking represented by the volume of fluid (VOF) method in CFD [15; 16; 17; 18; 19; 20; 21; 22; 23]. By adopting a numerical method based on the VOF method, various phase flow behaviors and gas-liquid and gas-gas interactions can be accurately described. The DPM has been used to track the dynamic behavior of solid particles in air. The micro-sized liquid aerosols from consumer product sprays are volatized into the solid phase rapidly. So, the DPM is suitable for application in the simulations of spray dynamic behavior. In addition, it is possible to analyze various information such as the dynamic behavior of aerosols that cannot be obtained in the experiment and the ventilation rate of each measurement location [24]. However, despite the various applications of CFD that can solve the spatial problem of zero-dimensional spray models and time/cost problems of experimental methods, there are no examples of CFD used in the field of exposure science for estimating exposure by biocide spray. Therefore, this study proposes the feasibility of applying CFD for the inhalation exposure evaluation by comparing the experimental results and the zero-dimensional simulation results with CFD results. This study has two purposes, (1) to investigate the advantages and disadvantages of the zero-dimensional model based on the CFD results and experimental results of the biocide aerosol concentration, (2) to evaluate the feasibility of a new approach for the inhalation exposure assessment to solve the spatial problem of the zero-dimensional spray model and the cost/time problem of experiments by comparing the CFD results of the aerosol concentration at the various measurement positions with the experimental results of the biocide aerosol concentration at the same measurement positions. As far as we can tell, this paper is the first report to propose the new approach of exposure assessment by using CFD. The research flow chart of this work is summarized in Figure 1. First, the experiments, such as the particle size distribution (PSD) and mass generation rate measurement, are conducted to simulate the zero-dimensional model and CFD. The experiments of the model parameters are performed in a large well-controlled chamber. Then, zero-dimensionalspray simulation, CFD spray simulation and the aerosol concentration measurement by spray are performed. The three results were performed based on the same chamber volume and particle information (PSD, mass generation rate and injection time). Finally, the feasibility of the zero-dimensional spray model result is evaluated by the CFD results and the measurement results. Additionally, the CFD results were compared with the experimental results of the biocide aerosol concentration at the same measurement positions in order to verify the feasible use of CFD for exposure estimation.
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 6 Experimental Results and Evaluation
### Simulation Environment and Parameters  
In our simulation study, we have used topology with two aggregators (i.e., DODAG root) and 140 smart devices (i.e., sensor nodes), randomly scattered in the area of 300 m \(\times\) 300 m. The DODAG roots were placed at the center of the simulation area, and other nodes were spread over the sensing area. In the network, the nodes communicate with each other either directly or in a hop-by-hop manner. In our simulated scenario, all the nodes were configured as Tmote Sky motes with CC2420 wireless transceiver. We considered that the network generates frames of different sizes at varying intervals. Therefore, the maximum size of each data packet was restricted to 127 bytes, and data rate to 250 kbps. The total time for each simulation run was 1800 s. We performed our simulation 10 times for each test and drawn the average results to minimize the errors. The trickle timer parameters are set to the default value. In the graphical description, the error bar shows 95% confidence intervals. Table 1 shows the remaining key simulation parameters of our experiment.