esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypoplastic Modeling of Soil-Structure Contact Surface Considering Initial Anisotropy and Roughness
## 4 Finite Element Implementation of the Interface Constitutive Model
### Results Analysis
In addition, Figure 16 compares the shear stress-displacement curves obtained from the contact surface model proposed in this study and from the Mohr-Coulomb contact surface model commonly used in engineering. It can be observed that the maximum shear stress is limited when using the Mohr-Coulomb model; following that, the yield stress limit is achieved, and the shear stress keeps constant. In this sense, the predicted results by Mohr-Coulomb model do not accurately describe the experimental results, even though the Mohr-Coulomb model can simply reflect the tangential friction behavior at the contact surface. Meanwhile, the hypoplasticity contact surface model proposed in this study can more accurately capture the nonlinear behavior of the contact surface, suggesting that the proposed model can better predict the mechanical behaviors of the contact surfaces with large deformation, sliding, and cohesion. In summary, the analysis indicates that the direct shear test finite element model using the hypoplastic model can effectively capture the influence of different roughness and the initial deposition angle of sand on the mechanical properties of the contact surface. This validates the rationale of the model proposed in this paper, which considers initial anisotropy and roughness. The proposed model is expected to have a broad applicability in various engineering projects, including tunnel engineering, foundation design, and slope stability analysis. We are currently exploring the application of the constitutive model proposed in this paper to practical engineering projects, including the prediction of  
Figure 16: Comparison of shear stress–displacement relations between the Mohr-Coulomb model and the hypoplasticity model. frictional resistance between tunnel segments and slurry during the VSM (Vertical Shaft Sinking Machine) construction method. It is worth mentioning that two assumptions of the whole methodology are considered in this study to establish the hypoplasticity contact surface model: (1) the shear action and control mechanism at the contact surface is consistent with that of soil, and the contact surface can be regarded as a tiny continuous soil unit with the same deformation pattern as that of soil; and (2) an isotropic stress state is assumed at the contact surface (i.e., \(\sigma_{22}=\sigma_{33}\
eq\sigma_{11}\)), which develops independently of the normal stress during shear. In this sense, the model proposed in this study has some limitations for predicting stress-induced anisotropic and cohesive soil-structure contact surfaces, which should be focused on in future work.
## 5 Experimental Results  
The CI model always displayed a physical equivalence and continuous relationship between the distance and the transmitted power due to CI free-space anchor point [68]. In the result section, \(d_{0}=1\) m was applied, as this distance has been considered as a standard reference in published work since it allows the measurement of path loss to transmit power of the CI model [70]. Table 2 shows the comparison of the CI model parameters for the LOS and NLOS environments. The PLE values were 3.0990 dB and 4.2556 dB for co-polarization and cross-polarization, respectively. The shadowing factor (SF) was 9.2268 dB by using a 1-meter FSRD for co-polarization, and 8.3041 dB for cross-polarization. The cross-polarization factor (XPF) was calculated as defined in Equation (3) of [46]. The fitting of the PLE \(n\) at 38 GHz for both co- and cross-polarizations are displayed in Figures 2 and 3, respectively. On the other hand, the FI model (see Table 3) shows the path loss parameters: \(\alpha=75.1359\), \(\beta=2.3401\), and SF= 9.0714 dB for co-polarization, whereas it shows \(\alpha=97.6545\), \(\beta=1.9560\), and SF = 6.5463 dB for cross-polarization. Figures 4 and 5 illustrate the best fitting draw for both co- and cross-polarizations, respectively. Note that, the NLOS V-V and NLOS V-H outcomes were very much correlated for FI model, hence the discussion is only focused on the NLOS V-V case, as described in Sections 6 and 7. It is worth mentioning that these results are comparable to the results reported in [71]. As for the CI model, it is concluded that the cross-polarization decayed faster than the co-polarization for LOS environment, which is similar for the FI model, while cross-polarization exhibited higher path loss value than co-polarization by approximately 20 dB, as portrayed in Figures 4 and 5.  
Figure 4: LOS and NLOS FI path loss model for the co-polarization.  
Figure 5: LOS and NLOS FI path loss model for the cross-polarization.  
Figure 3: LOS and NLOS CI path loss model for the cross-polarization.
# Dual-Branch CNN-Mamba Method for Image Defocus Deblurring
## 4 Experiments and Analysis
### Ablation Study
#### 4.6.2 Ablation Study on Dimension Size  
The data in Table 4 demonstrate the impact of feature dimension on model performance. On the DPDD dataset, increasing the feature dimension from 24 to 36 channels improves PSNR by 0.12 dB but at the cost of a 5.25 M increase in parameters. Further increasing the dimension to 48 channels only yields a marginal PSNR improvement of 0.03 dB, while requiring an additional 7.25 M parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Method** & **PSNR \(\uparrow\)** & **SSIM \(\uparrow\)** & **MAE \(\downarrow\)** \\ \hline \multirow{3}{*}{DPDD} & CNN only & 24.91 & 0.753 & 0.041 \\  & Mamba only & 25.14 & 0.769 & 0.041 \\  & Baseline & **25.54** & **0.778** & **0.039** \\ \hline \multirow{3}{*}{RealDOF} & CNN only & 23.37 & 0.668 & 0.045 \\  & Mamba only & 24.18 & 0.711 & 0.042 \\ \cline{1-1}  & Baseline & **24.62** & **0.726** & **0.040** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study of the model structure on the DPDD and RealDOF datasets.  
On the RealDOF dataset, models with 36 and 48 channels show significant performance gains over the 24-channel model. However, the difference between the 36-channel and 48-channel models is negligible, despite the latter's substantial increase in parameters.  
Considering the trade-off between performance and computational cost, this study sets the initial channel dimension to 36, achieving an optimal balance between model effectiveness and resource efficiency.
# Investigating the Key Aspects of a Smart City through Topic Modeling and Thematic Analysis
## 4 Discussion
### Limitations  
We acknowledge the limitations of this research. The analysis was conducted on relatively small datasets, particularly in the case of news blog posts and social media entries, where we examined only one source for each category: Business World News and Reddit, respectively. The internet contains a wealth of information, but there are various restrictions imposed by companies on access to this data. We selected these specific sources because they offered relatively unrestricted access and provided sufficient data for the analysis to produce reasonably reliable results. Clustering short texts in specific domains, like social media entries, is widely recognized as a challenging task. [PERSON] et al. [63] enumerate the reasons for this difficulty. First, the brevity of short texts results in low word frequency and sparse content, leading to unstable and inaccurate clustering outcomes. Second, within a narrow domain, there is often a significant overlap of less meaningful words, making it challenging to differentiate between sub-domains or create finely grained clusters. Additionally, the limited vocabulary size hinders the construction of a comprehensive word bag necessary for clustering algorithms to generate meaningful topic distributions.  
In our analysis, we employed BERTopic, an unsupervised topic modeling library that produces unlabeled topics as output. As noted by [PERSON] et al. [64], the lack of labeled data can pose challenges in assessing the significance and relevance of unconventional topics, often making it difficult to distinguish them from noise or irrelevant topics. This challenge becomes especially pronounced when dealing with extensive and intricate datasets encompassing a wide array of topics. Furthermore, it is important to acknowledge the inherent subjectivity of qualitative analysis. While the collaboration between the two authors resulted in a comprehensive study, the reliability of the findings could be further enhanced with the input of additional experts in the field.
# Synovial Sarcoma of the Extremities: A Literature Review
## 2 Diagnosis
### Biopsy  
A biopsy is mandatory for a proper diagnosis in patients with synovial sarcoma; moreover, it will allow the morphopathologist to differentiate this tumor, especially from other soft tissue sarcomas, and to define the tumor staging.  
In order to harvest a representative sample of tumoral tissue, there are several options available, including: a fine needle aspiration, a core needle biopsy (with or without imaging guidance) and an incisional biopsy.  
At this point, a core needle biopsy is considered to probably be the best choice, especially if it is performed under ultrasonographic or tomographic guidance for deep, nonpalpable tumors. The major advantage of a core needle biopsy is the amount of tissue that can be retrieved compared to a fine needle aspiration and the small rate of complications compared to an incisional biopsy [11; 13].  
An incisional biopsy should be performed by a musculoskeletal oncologist or after consulting them, where the size of the incision should be as small as possible and it should  
Figure 6: Synovial sarcoma, F, 31 YO patient, CT image, axial section, with contrast—bone window: osteolytic area in the right ischio-public rami.  
Figure 7: Angio-CT with 3D reconstruction indicating the relationship of the synovial sarcoma with the major arterial vessels and the tumoral vascularization.  
be in line with the planned incision for the tumoral excision. A good surgical technique is required with careful hemostasis in order to avoid hematoma formation.  
The microscopic exam of the synovial sarcoma will indicate one of the three possible variants: monophasic, biphasic or poorly differentiated. For the first variant, the cellularity is represented by spindle cells, whereas in a biphasic synovial sarcoma, there may also be epithelial cells present (Figures 8 and 9). Furthermore, the microscopic exam may reveal the presence of calcifications/ossification areas in up to one-third of cases.
# A Frequency-Selective Reconfigurable Antenna for Wireless Applications in the S and C Bands
## 2 Antenna Geometry  
The structural geometry of the antenna is illustrated in Figure 1. The antenna printed on an FR-4 material exhibits a dielectric constant of 4.3 and a height of 1.5 mm. The copper's height is 35 \(\upmu\)m. The radiating element is a rectangular ring-shaped patch. The antenna is excited via a microstrip feed line. The ground plane, on the bottom side of the antenna, consists of two rectangular parts, and the PIN diode is placed in the middle between them, with its anode being on the top. Four rectangular slots were also inserted on the ground plane in places where secondary undesired resonances showed maximum surface currents, reducing or canceling their effect. The dimensions of the slots, as well as those of the patch, substrate, and ground plane, were adjusted to achieve optimal results. The final value for each design parameter appears in Table 1.  
Figure 1: Structural geometry of the antenna (**a**) Front view, (**b**) Back view.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 6 Experimental Evaluation
### Results and Discussion
Regarding the results from the TOPSIS method (Table 7), which are illustrated in Figure 14, we observed a closer distribution between the percentages of slices choice, except for Tests 3 and 4, in which there was a majority preference for Slice 3, of 99.2% and 76.8%, respectively. The same behavior also occurs in the COPRAS and VIKOR methods. For the Promethee II method, detailed in Table 8 and illustrated in Figure 15, there is a homogeneity in the selection of slices for Tests 1 and 2, a practically unrestricted preference for Slice 3 in Test 3, with the same slice chosen in 99.6% of the time. In general, throughout the iterations of each of the tests, Slices 2 and 3 presented better resource conditions to receive the UE flows. Figure 14: TOPSIS Method. Figure 13: COPRAS Method. To verify the performance of the MCDM methods in the slice selection problem, some other graphs were generated, aiming to more succinctly illustrate the performance of each approach regarding the tests executed (please refer to Figures 16-18). Once the data results from each method were obtained, a descriptive analysis checked for significant differences in the performance of the methods for the slices evaluated in the set of tests. The experiment consists of comparative analysis, using [PERSON]'s test of multiple comparisons of means, from the analysis of variance. We also applied the Shapiro-Wilks normality, Durbin-Watson independence, and Fligner-Killeen homoscedasticity tests [85]. Figure 16: Method percentage results for Slice 1. Figure 15: PROMETHEE II Method. From the analysis of the test results, the Tukey test revealed that the selection means between the methods do not differ significantly, as discussed above. Another point of interest refers to the results from the Shapiro-Wilks normalization test that describes a normal distribution of the samples. For all the tests performed, there was no violation for Shapiro-Wilk. From the Durbin-Watson test, we can state with 95% confidence that the residuals are independent. The Fligner-Killeen test shows the samples have homoscedasticity of variances.
## 3 Acquisition Parameters and Evaluation Metrics  
### Acquisition Parameters  
We compare the performance of the beamformers on imaging data from the simulation, phantom and in vivo data.  
The simulated datasets were generated with Field II [25; 26]. It contains two simulated datasets to evaluate the lateral resolution and contrast, respectively. The two datasets are both simulated with a 128-element probe, and the central frequency is 7.81 MHz. Each dataset contains 11 tilted plane-waves linearly distributed between \(-18^{\circ}\) and \(+18^{\circ}\) (angles number = 11).  
The mimicking phantom and in vivo datasets were acquired by an ultrasound imaging system (Vantage, Verasonics, WA, USA) with a L11-5V transducer. The mimicking phantom has isolated scatters and an anechoic cyst. The in vivo data we used is from the right-side neck of a healthy 28-year-old volunteer. Each set also contains data acquired by 11 tilted plane-waves distributed from \(-18^{\circ}\) to \(+18^{\circ}\) (angles number = 11). The simulation and the experiment parameters are the same, and all the imaging parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Pitch & 0.3 mm \\ Element width & 0.27 mm \\ Number of elements & 128 \\ Transmit frequency & 7.81 M [Hz] \\ Bandwidth & 4.8M\(-\)10.8 M [Hz] \\ Sampling frequency & 30 M [Hz] \\ F-number & 1.75 \\ Frame rate & 50 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental imaging parameters.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 4 Evaluation and Performance Analysis
### Scalability and Architectural Performance  
TrustShare leverages Hyperledger Fabric's modular _execute-order-validate_ model to decouple transaction processing from ordering and validation [31]. This architectural design enables parallelism, improving both throughput and latency by mitigating smart contract non-determinism and resource contention.  
Latency performance of TrustShare is comparable to traditional systems like PostgreSQL, particularly under increased load [79]. Kubernetes-based orchestration enhances scalability through dynamic resource allocation, while IPFS integration ensures secure off-chain replication and data availability.  
The transaction throughput is largely governed by the ordering service. Employing consensus mechanisms such as RAFT or Kafka allows horizontal scalability through node addition [33].
# Retinal Image Analysis for Diabetes-Based Eye Disease Detection Using Deep Learning
## 3 Experimental Results
### Evaluation Metrics  
For analyzing the efficiency of our proposed technique, we have considered the following evaluation metrics:  
FRCNN performed the localization of eye-diseases by using the greedy intersecting measure of the predicted box and ground truth called Intersection over Union (_IoU_). The correct prediction is true positive TP and the other is a false positive:  
\[IoU=\frac{TP}{FN+FP+TP}\times 2 \tag{7}\]  
To calculate the average precision (AP) we iterate through test images as per precision. Equation (8) represents the mean average precision (mAP):  
\[mAP:=\sum_{i=1}^{T}AP(t_{i})/T \tag{8}\]  
Here \(T\) is the number of test images, AP(\(t_{i}\)) is the average precision for given test image category. This means that we calculate the AP for each category for given test image category, \(t_{i}\), and then the average of each category across all test images. Then all AP scores would then give us a single number which is mAP [55], it describes how good the trained model is for detecting bboxes with respect to bboxes of ground truth.  
For segmentation, we have utilized the specificity (SP), sensitivity (SE), area under the curve (AUC), accuracy (Acc) and Dice coefficient (\(D_{c}\)) as the evaluation measures:  
\[D_{c}=\frac{2\times TP}{2\times TP+FN+FP} \tag{9}\]
# Cumulative Failure Rate Prediction of EDCU in Subway Vehicles Based on RF-CNN-LSTM Model
## 2 Basic Theory
### Introduction to LSTM Principles  
LSTM is a time [19] recursive neural network with the ability to process and predict key information in a time series, which can avoid the problems of gradient vanishing and gradient explosion and thus, capture long-term dependencies more efficiently. The LSTM cell consists of five parts: input gate, forgetting gate, output gate, cell state, and hidden state. The details are as follows:
## 2 Mathematical Model of the Hydrocooler
### Mathematical Model of TES System  
During the charging process, the thermal energy is stored in cold water and ice as sensible and latent heat. Cold water storage only contains sensible heat, while ice storage contains both latent heat of ice and sensible heat of cold water. Therefore, latent heat has significant advantages over sensible heat and is the preferred method for energy storage.  
In the case of litchi, a precooling system that handles 1000 kg can meet the demand of most small- and medium-sized orchards. The suitable storage temperature of litchi is about 5 \({}^{\circ}\)C, and it is appropriate to reach 8 \({}^{\circ}\)C after precooling. The pulp temperature of litchi is about 29 \({}^{\circ}\)C when picked. According to the principle of conservation of energy, the energy absorbed by litchi fruits precooling is calculated as [28]:  
\[Q_{lit}=m_{lit}c_{lit}(T_{ttl}-T_{itl}) \tag{1}\]  
The TES tank also contains the TES capacity that cannot be used in the precooling process, since the water temperature drops from the initial charging temperature to the termination precooling temperature. There is significant thermal energy leakage during the refrigeration and storage phases. During precooling, the loss of cooling capacity caused by the water pump working on the water, opening the precooling box and changing the precooling litchi, the connection between the inlet and outlet of the box, and the heat bridge at the edge of the box need to be taken into account in the TES capacity. Therefore, the TES capacity of charging should be improved as follows [29]:  
\[Q_{chr}=Q_{lit}+m_{uat}c_{uat}(T_{itw}-T_{ttw})+P_{el}i_{ctp}+P_{wpw}t_{otp}+P_ {load}t_{otp}+P_{thb}t_{ctp} \tag{2}\]  
\[\frac{dP_{eli}}{dt}=(A_{tsl}h_{tsl}+A_{spb}h_{spb}+A_{cwp}h_{cwp})\frac{d(T_{ amb}-T_{uat})}{dt} \tag{3}\]  
\[\frac{1}{h_{tsl}}=\frac{1}{h_{sinv}}+\frac{\delta_{ilt}}{\lambda_{ilt}}+\frac {1}{h_{sta}} \tag{4}\]  
\[\frac{1}{h_{spb}}=\frac{1}{h_{sinv}}+\frac{\delta_{ils}}{\lambda_{ils}}+\frac {1}{h_{spa}} \tag{5}\]  
\[\frac{1}{h_{cwp}}=\frac{ln\left(\frac{r_{\mu i}}{r_{\mu i}}\right)}{2\pi \lambda_{ilc}}+\frac{1}{h_{spa}} \tag{6}\]  
\[P_{wpw}=\alpha P_{wpp} \tag{7}\]  
Figure 1: Operation principle diagram of the spray hydrocooler.  
The temperature of ice water mixture is 0 \({}^{\circ}\)C after charging, and the total TES capacity comprises the sensible heat of water and latent heat of water freezing. Direct evaporation is used for TES, and external melt is used to release thermal energy. Ice is formed directly around the evaporation coil as a regular cylinder. The amount of ice stored in TES tank at the end of charging can be estimated using the following equation [30]:  
\[m_{ice}C_{ice}=Q_{chr}-m_{wat}c_{wat}T_{itw} \tag{8}\]  
\[m_{ice}\rho_{ice}=\pi l_{coi}\Big{(}r_{ice}^{2}-r_{coi}^{2}\Big{)} \tag{9}\]
## 1 Introduction
### Objective and Contributions  
This paper showcases the first steps of the authors' research work on CAVs and MAVs in the context of the hOListic Green Airport (OLGA) research project. The aim of implementing CAVs and MAVs into the existing public transport infrastructure adds to the main focus of improving energy efficiency in and around the airport's activities and reducing pollutant emissions per capita, to and from the airport.  
Compared to existing literature and available urban mobility solutions, a Mobility-as-a-Service (MaaS) solution for the International Airport is proposed--correlating air transport with landslide transport for a last-mile travel solution during nighttime. In this time frame, the traffic is considerably lower, and travel requests are neither constant nor exceedingly high. By analyzing the energy efficiency differences between MAVs and BEBs, the current study lays the foundation of the MaaS proposal, which is part of an intermodality concept in urban transportation of passengers and goods, combining multiple transport modes from urban area entry points (i.e., airports, train stations, factories, warehouses, etc.) to last-mile solutions.  
This initial solution is conceived around the particular case of Cluj-Napoca, Romania (a city located in valleys and on hills with approximately 300,000 inhabitants in the urban area, over 400,000 people living in the metropolitan area, and an international airport inside the urban area). In future advancements, the proposed solution will be developed to fit the needs of similar locations, with airports located inside or in the close vicinity of the urban area, where public transport routes are linking interest points of the urban area (e.g., city center, commercial centers, residential areas, etc.).  
As a primary point of evaluation, the simulations presented in this paper aim to identify the benefits of a MAV setup on energy consumption, when compared to the existing urban mobility solution containing battery electric buses. As the MAV solution is not yet implemented on-site, the weight distribution is studied in order to determine the most efficient configuration of MAV for this particular case.
## 3 The Proposed Protocol
### Mutual authentication between UAVs  
When the UAVs in the cluster (e.g., D and C) need to communicate, it is only necessary for them to implement a secure link between the UAVs through the key node (e.g., A). First, D generates two random numbers \(K\) and \(\widetilde{K}\), and sends an encrypted message \(E(K,\widetilde{K})\) to C; then D encrypts the key and sends the message \(E(SK_{AD},K)\) to the key node A; A decrypts the key \(K\) and transmits the message \(E(SK_{AC},K)\) to C; C decrypts the key and obtains \(K\) and decrypts the message \(E(K,\widetilde{K})\) to obtain \(\widetilde{K}\), which is then used as the session key between the two nodes. As shown in Figure 6.