esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Reviewed Papers on Empirical-Based Path Loss Models

Table 3: _Cont._To investigate five alternative path loss scenarios in an urban environment, the authors in [47] adopted the FI and CI models to evaluate the best path loss model for Lagos Island. In comparison to the CI model, they discovered that the FI model had the lowest RMSE value for the situations they were evaluating. As a result, the FI model offers the most accurate prediction model to describe the environment. A thorough measuring campaign at 26 GHz was conducted over the course of the summer in two remote areas of southern Finland, according to [PERSON] et al. [66]. The collected data demonstrate that the mean path loss increased from 4 to 6 dB for every 20 m increase in antenna height. As a result, while planning a fixed wireless access type 5G rural network, transmitter antenna height is a critical factor. The 3 GPP RMa model has the largest shadow fading, ranging from 8 to 10 dB, when the specified path loss models are compared. As a result, the ABG and CI models fit data more accurately than the 3 GPP RMa model. The CI model appears appropriate for modeling various antenna heights in an RMa environment, even though the chosen models were not compared based on an indoor measurement experiment. In [67], path loss models were thoroughly examined for the outdoor environment in a tropical climate while considering two distinct scenarios. For the (LoS) case study, the 3D T-R separation distances ranged from 16.5 m to 70 m, with data line-of-sight being gathered every 10 m. An omnidirectional antenna emulator was used to calculate the results. Although the impedance matching technique between the feed line and the horn antennas was not investigated, it might influence the effect of return and mismatch losses within the system. The result revealed that co-polarization decays rapidly in line-of-sight (LOS) scenario. To assess mmWaves and sub-tetra hertz propagation for outdoor urban microcells (UMi), the authors of [68] took into account several possible scenarios. The findings of their research showed that the 60, 100, and 120 GHz channels are more sensitive to the effects of changing environmental circumstances than the 38 and 73 GHz channels. Nevertheless, there was no actual measuring campaign run on the channels in question. In [69], it was shown that it is possible to model path loss in the frequency ranges of 1.8, 3.5, and 28 GHz by using wideband measurements made in the middle of a street. The atmosphere was static, and there was no breeze during the measuring campaign. The outcome shows that after a certain distance between the transmitter and receiver, the multiple-scattering contributions from trees in the 1.8 GHz and 3.5 GHz bands must be considered. An improvement on the widely used log-distance model was presented in [70] to predict path loss in areas with raster map data, especially in suburban environments. In
# Solar Sail-Based Mars-Synchronous Displaced Orbits for Remote Sensing Applications
## 5 Conclusions  
In this paper, we analyzed the performance required by a flat and perfectly reflecting solar sail (the thrust vector of which is described by an ideal force model) in a heliocentric mission scenario where the requirement is to maintain a circular displaced non-Keplerian orbit for remote sensing applications of the high-latitude regions of Mars. In this sense, this paper extends the scientific literature regarding solar sail-induced displaced non-Keplerian orbits. In this specific context, the Mars-spacecraft distance was evaluated during the revolution period of the Red Planet as a function of the characteristics of the solar sail-induced (Mars-synchronous) displaced orbit. Accordingly, the solar sail-based spacecraft is able to follow Mars during its motion around the Sun (i.e., the spacecraft and the planet of interest roughly share the same angular position), while the vehicle travels its trajectory on a plane parallel to the orbital plane of Mars. In this way, high-latitude planetary regions are continuously visible and potentially observable.  
In this scenario, an optimal approach was used to calculate the minimum flight time of the solar sail-based spacecraft towards the circular displaced non-Keplerian orbit around the Sun. In particular, the optimal flight times were obtained through an indirect approach, and the resulting rapid transfer trajectories correspond to medium/high-performance solar sails (i.e., solar sails with a lightness number greater than 0.05), which have the thrust performance necessary to maintain the target DNKOs. In this context, the optimization process shows that the minimum flight time necessary for a transfer from the Earth to the target DNKO is 949 days for a medium-performance solar sail and 498 days for a high-performance solar sail.  
Conceptualization, A.A.Q.; methodology, M.B.; software, M.B.; writing--original draft preparation, A.A.Q. and M.B.; writing--review and editing, A.A.Q. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 The Proposed Method
### Sentiment Analysis
#### 3.3.2 Aspect Sentiment Vector  
As aspect sentiment intensity is purely numerical, to combine it with other feature vectors, this study adopted an artificial neural network to convert the numerical values of aspect sentiment intensity into feature vectors. Based on the study of [PERSON] et al. [43], an artificial neural network was employed by this research to build a rating prediction model, as shown in Figure 2. This artificial neural network architecture consists of one input layer, two fully connected layers, and one output layer. The input layer is the user-product aspect sentiment intensity. The fully connected layer is used to concatenate the user's sentiment intensity characteristics for the product. The output layer uses a _Softmax_ function to classify the output into five categories, which represent the predicted ratings of the reviews (5-star rating). Finally, the user and product weights are extracted from the model to convert aspect sentiment intensity into an aspect sentiment vector with a fixed dimension.  
Assume that \(r_{u}\) is the predicted user rating, \(r_{i}\) is the predicted product rating, \(g\) is a nonlinear function, \(w_{u}\) is the weight of the user model, \(w_{i}\) is the weight of the product model, and \(b_{u}\) and \(b_{i}\) are the bias of the user model and the product model, respectively. The prediction scores are calculated, as follows:  
\[r_{u}=g(US_{u,x}\times w_{u}+b_{u}) \tag{7}\]  
\[r_{i}=g\big{(}PS_{p,l}\times w_{i}+b_{i}\big{)} \tag{8}\]  
Figure 2: Generating the aspect sentiment vector.  
The user and product weights are extracted from the model and unemployed to present the aspect sentiment vector. Assume that \(\widetilde{AS_{u,x}}\) is the sentiment vector of user \(u\) in aspect \(x\) and \(\widetilde{AS_{p,l}}\) is the sentiment vector of product \(p\) in aspect \(l\). These two sentiment vectors are presented by Equations (9) and (10), respectively. As their vector dimensions are the same as the aspect vector (Section 3.2) and the semantic vector (Section 3.4), integration can be subsequently conducted:  
\[\widetilde{AS_{u,x}}=US_{u,x}\times w_{u} \tag{9}\]  
\[\widetilde{AS_{p,l}}=PS_{p,l}\times w_{i} \tag{10}\]
# Improvement of Fresnel Diffraction Convolution Algorithm
## 4 Conclusions  
In summary, a more accurate Fresnel diffraction integration algorithm is urgently needed because both the D-FFT algorithm and the T-FFT algorithm cannot obtain accurate amplitude and phase distributions of the diffracted field when dealing with diffraction problems with diffraction distances exceeding the splitting distance. Based on this need, the D-FFT (Tukey) algorithm has been proposed in this paper.  
The D-FFT (Tukey) algorithm filters the transfer function using a Tukey window with adjustable edge smoothing, which eliminates the ringing effect while filtering out the phase distortion part of the transfer function. Since the algorithm filters out the phase-distorted portion of the transfer function directly using the Tukey window, it avoids the error caused by using the fast Fourier transform to compute the transfer function when calculating long-distance diffracted fields and thus improves the resolution of the phase distribution. The improved algorithm is suitable for diffraction problems where the diffraction distance is larger than the splitting distance. In particular, the edge smoothing of the Tukey window used for filtering is affected by \(\beta\). For any diffraction problem, PSNR and SSIM can be  
Figure 11: Phases reconstructed by (**A**) D-FFT; (**B**) D-FFT (rect); (**C**) T-FFT; and (**D**) D-FFT (Tukey) (\(\beta=0.81\)).  
calculated according to the actual parameters to select the optimal solution for \(\beta\), which makes the D-FFT (Tukey) algorithm generalizable.  
The improved algorithm proposed in this paper eliminates the under-sampling effect of the transfer function without causing the ringing effect, and a higher resolution of the phase distribution is obtained. It is expected to be applied in areas such as phase correction, structured illumination digital holographic microscopy, and digital holographic microscopy, which have stringent requirements for the reconstruction amplitude and phase.  
Conceptualization, Q.S., J.L. and X.Q.; methodology, Q.S. and C.G.; software, C.G.; validation, C.G., Q.L. and H.D.; writing--original draft preparation, Q.S. and C.G.; writing--review and editing, C.G. and W.C.; funding acquisition, Q.S. and J.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by grant number 62165007 to Qinghe Song, and grant number 62065010 to Jinbin Gui. The APC was funded by grant number 62165007.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Results
### Compression Properties of EMWM
#### 4.3.1 Effect of Compression Rate on EMWM  
As shown in Figure 23, the hysteresis curves at different loading rates almost coincide, indicating that it has good elastic recovery ability. Even under different loading rates, the material is able to return to the original state quickly and approximately the same after compression, indicating that the elastic recovery in compression rate is relatively stable [1].
# Study on Heat Transfer Performance and Anti-Fouling Mechanism of Ternary Ni-W-P Coating
## 4 Discussion  
Via the aforementioned studies, we evidenced that the deposition of CaCO\({}_{3}\) fouling can be effectively inhibited by ternary Ni-W-P coating after fouling in a certain period of time. This can reduce the decreasing rate of the heat transfer efficiency of the substrate surface caused by fouling. Because of the different energy barriers of transition state during the nucleation process of different lattice types of CaCO\({}_{3}\) fouling, the nucleation process on surface with different free energy was induced distinguishingly [54; 55], resulting in different types of calcite-phase, aragonite-phase, and vaterite-phase CaCO\({}_{3}\) fouling deposited on surfaces. Moreover, the changes in the thermal conductivity of ternary Ni-W-P coating were principally caused by the difference of the hot carriers of the different phase structure in the energy transfer process.  
Although ternary Ni-W-P coating can obviously inhibit the deposition process of CaCO\({}_{3}\) fouling and oxide, it should also be conditional in its application because of the thermal resistance of ternary Ni-W-P coating itself. Fortunately, we found that after 72 h of depositing in high concentration supersaturated fouling solution, the thermal resistance of the coating has been offset by the influence of a thicker fouling layer. Nevertheless, the Ni-W-P coating with low W content should be used as the anti-fouling interface firstly in the field of industrial heat. One reason is that it can ensure a good heat transfer efficiency. The other reason is that it can more effectively inhibit the excessive deposition of hard-to-remove calcite-phase CaCO\({}_{3}\) fouling on the surface.
## 5 Role in Management of CBM Extraction  
Isotope geochemistry plays a vital role in managing the environmental implications of CBM extraction, particularly regarding emissions control.  
Methane Emissions: CBM extraction releases methane (CH\({}_{4}\)), a potent greenhouse gas. Isotope geochemistry, especially the analysis of carbon and hydrogen isotopes, helps trace methane sources (biogenic vs. thermogenic) and migration. This aids in identifying methane leaks, which can be crucial for emissions management. By analyzing isotopic ratios in released gases, geochemists can pinpoint leak sources and assess the effectiveness of mitigation measures, ensuring that methane capture systems are working efficiently.  
Carbon Sequestration: In some CBM operations, carbon capture and storage (CCS) is employed to inject CO\({}_{2}\) into coalbeds, enhancing methane recovery while sequestering CO\({}_{2}\). Isotope geochemistry monitors the injected CO\({}_{2}\), tracking its migration and verifying  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Coalbed**} & **Maximum** & **Average** & **Radius of** & **Number** & **Maxed** & **Measured** & **Residual** & **Gas** & **Content** & **Deviation** \\  & **Gas** & **Mine-** & **CSM** & **Mine-** & **Mine-** & **CBM** & **Mine-** & **Ratio (\%)** & **Rate (\%)** & **Measured on** & **Rate (\%)** \\  & **Content** & **(m\({}^{3}\)/t)** & **(m)** & **(m)** & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\)/t)** & **(m\({}^{3}\)/t)** & **(m\({}^{3}\)/t)** & **(m\({}^{3}\)/t)** & **(m\({}^{3}\)/t)** \\ \hline
6m & 12.47 & 1.8 & 2.5 & 8 & 1.61 & 1,260,843 & 60.81 & 6.21 & 5.84 & 5.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of residual gas content in 6m coalbed at the 14\(\mu\) mining face.  
long-term storage to prevent leakage. This ensures that CO2 remains trapped underground, enhancing the environmental sustainability of the operation.  
Flaring and Venting: Methane is often flared or vented during CBM extraction. Isotopic analysis of methane and CO2 emissions helps evaluate the efficiency of flaring processes, ensuring that methane is completely combusted. Incomplete flaring can lead to the release of unburned methane, contributing to greenhouse gas emissions, and isotopes can guide optimization of flaring practices.  
In summary, isotope geochemistry aids in tracking methane leaks and CO2 storage, all of which are critical for minimizing the environmental impact of CBM extraction.
# Pedestrian Detection Based on Feature Enhancement in Complex Scenes
## 4 Experiments and Analysis
### Ablation Experiments
Therefore, the pedestrian detection network PT-YOLO proposed in this paper has a better detection effect in pedestrian detection. As shown in Figure 10, we present the test results of the benchmark network YOLOv5, and the PT-YOLO pedestrian detection network proposed in this paper. The left image is the original image, the middle image is the YOLOv5 detection result, and the right image is the PT-YOLO detection result. As shown in Figure 10, pedestrians in different scenes are visualized using the YOLOv5 network and pedestrian detection network PT-YOLO. The detection results (a) demonstrate that when the distance between pedestrians is relatively large and the target size is small, the detection performance of the pedestrian detection network PT-YOLO surpasses that of the YOLOv5 network. The detection result in (b) shows that when the scenes are more complex, false detection may occur. From the detection result in (b), it can be seen that the YOLOv5 network detects the pedestrian at the corner of the building incorrectly, while the PT-YOLO pedestrian detection network can detect it correctly. Detection result (c) shows that the pedestrian detection network PT-YOLO is capable of accurate detection in crowded and complex scenes. Therefore, it is verified by the visualization results that the pedestrian detection network PT-YOLO can accomplish pedestrian detection more accurately. In this paper, the public dataset JRDB was chosen as the dataset for this experiment. The JRDB dataset is a dataset specifically set up for pedestrian detection and tracking, therefore contains large-scale pedestrian scenes. This experiment was completed by using 8434 images from the dataset as the training set and 2109 images as the testing set to obtain the experimental results. Because JRDB is an open dataset, the experiments were  
Figure 9: Comparison of the ablation experiment MAP @0.5 and MAP @0.5_0.95.
## 2 Methodology  
The summary of the procedures performed in this research is presented in Figure 1. The rainfall data were obtained from in situ rainfall stations and Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS). Rainfall stations of the National Institute of Meteorology (INMET), the Agronomic Institute of Parana (IAPAR), the Water and Land Institute (IAT), and the National Water Agency (ANA) were considered. The setup and analysis of the rainfall data supported the calculation of the event's return period (RP) for each station, via probability models. RP values were afterwards spatialized via interpolation (kriging), resulting in a detailed spatial-temporal description of the rainfall occurred between September 2015 and February 2016.  
In addition, a description of EN 15-16 was made based on the Southern Oscillation Index (SOI) and a survey on the effects that the rainfalls of the period brought on geosystem components was carried out. These data served as the basis for discussions about the effects of EN 15-16 on the geosystems in the northern region of Parana.
# Quantum Key Distribution in Kubernetes Clusters
## 4 Integration in a Kubernetes Cluster  
This section presents the core of this work: the integration of the QSS in Kubernetes. Considering the impact of microservices over modern paradigms such as Cloud, Edge and Fog computing, and Kubernetes as the de facto standard for the orchestration of these microservices, it is pivotal to integrate quantum-safe cryptography with this technology. In particular, the QSS already takes care of decoupling the physical technologies needed for the QKD and the requirements of the high-level security applications. The final piece of the puzzle is to integrate the QSS into a real orchestration platform so that the applications running in pods can leverage QKD keys without changing how they interact with classical secrets provided by the platform.  
In a Kubernetes cluster, the control plane, i.e., master nodes, retains all the sensitive information about the applications. It also provides the high availability of those data leveraging technologies such as _etcd_[26]. Since each Kubernetes cluster holds its own control plane and secrets management system, a reasonable scenario can involve two or more distributed clusters sharing cryptographic keys with QKD. Secrets in Kubernetes are unique resources that provide cryptographic keys, credentials and other sensitive data to applications. Our idea of integration lies in delivering a mechanism that allows a QKD key to be requested through the Kubernetes APIs from a specific application. Then this resource can be stored within the application namespace as a secret. Once available as a secret, the application can use it as a classical key.  
To add the functionalities of a QSS in a Kubernetes cluster, as we describe in Section 4.1, we used the operator pattern (Section 2.2). A set of Kubernetes clusters could be imagined as nodes of a QKD network, and each one integrates a QSS as an operator. This strategy  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Field Name** & **Description** \\ \hline version & protocol version \\ type & type of information carried, \$ for SAEs and \$ for QKDM links \\ source & QKS source ID, address and port \\ routing & source routing module address and port \\ forwarder & ID of the last QKS which forwarded the packet \\ neighbors & list of connected SAEs or active QKDM links, based on the type field \\ timestamp & packet creation time \\ authentication & data for authentication and integrity checks \\ \hline \hline \end{tabular}
\end{table}
Table 2: Routing LSA packet structure.  
allows applications to treat the QSS as a native resource that could exploit its functionalities without significant changes.
## 2 Materials and Methods
### Physical-Layer Security Analysis: Colluding Eavesdroppers
#### 2.1.5 Asymptotic Probability of Non-Zero Secrecy Capacity  
To study the impact of enhancing the wiretap channel's conditions over the privacy of the shared information, asymptotic \(P_{nsc}\) is evaluated as \(\gamma_{E}\rightarrow\infty\). Following the same approach used to find the asymptotic secrecy outage probability, the asymptotic probability of non-zero secrecy capacity is expressed as  
\[P_{nsc} \approx 1-\sum_{v_{1}=0}^{\infty}\sum_{v_{2}=0}^{\infty}\cdots\sum_ {v_{N}=0}^{\infty}\sum_{r_{1}=0}^{\infty}\sum_{r_{2}=0}^{\infty}\cdots\sum_{r _{n_{\epsilon}}=0}^{\infty}c_{x,\epsilon}a_{1}c_{\epsilon}\prod_{i=1}^{N} \Gamma[\mu_{i}+v_{i}]\prod_{j=1}^{n_{\epsilon}}\Gamma[\mu_{\epsilon j}K+r_{j}], \tag{22}\]  
where \(c_{\epsilon}=\frac{\prod_{j=1}^{n_{\epsilon}}E\left[X_{j}^{2}\right]}{4\prod _{j=1}^{N}E\left[X_{j}^{2}\right]\big{(}\prod_{j=1}^{n_{\epsilon}}\mu_{j}K_{ j}(1+\kappa_{j})\big{)}^{\mu_{\epsilon 1}K+r_{1}}\big{(}\prod_{j=1}^{N}\mu_{i}(1+\kappa_{i}) \big{)}^{\mu_{1}+v_{1}}}\).  
Equation (22) demonstrates that the eavesdropper taps the information and decodes it effectively since the characteristics of the wiretap channel are extremely strong in terms of the average received SNR \((\gamma_{E})\). A similar scenario is attainable when the eavesdropper is relatively close to the transmitter, enabling it to effectively decode the intercepted information.
## 3 Methodology
### Data Augmentation  
The synthetic minority oversampling approach (SMOTE) was utilized to eliminate biases in the produced models [44]. SMOTE is an oversampling approach that generates new samples from existing class samples to increase the number of minority class samples in the dataset. The method creates new minority class samples that are convex mixtures of two or more randomly selected neighboring data samples in the feature space rather than duplicates. A recent study showed that using SMOTE in clinical datasets improves model performance by decreasing the detrimental impact of unbalanced data.