esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations and Scope for Future Works  
Although the present investigation provides significant progress towards understanding the effects of sand particle shape on the damping ratio of dry sand, with the proposed AI-based models being applicable to other dry sands with similar particle shape parameters and minerology, there still exist some limitations and hence scope for future investigations. Firstly, this study was conducted using only one type of sand, which may limit the generalizability of the findings to other soil types (in terms of, for instance, gradation and mineralogy). Moreover, the present investigation only focused on three conventional shape parameters to characterize sand particle shape and did not consider other factors, such as particle size, particle sorting and loading rate, which may also affect the damping ratio. In addition, a further suggestion for future research is to explore the combined effects of moisture content and particle shape on the damping ratio of sands. That is, moisture content can have a significant impact on the mechanical properties of soils, including the damping ratio, as it affects inter-particle forces and particle arrangements. Therefore, mindful of the above points, further research is needed to validate and build on the experimental results and AI-based models presented in this study. It is also worth mentioning that the validity of any data-driven model, including the AI-based models proposed in this study, is dependent on the accuracy (correctness) of the data from which it is established. That is, successful predictions based on unreliable experimental data hold no practical significance. This highlights the importance of the data selection stage and the need to ensure data reliability before modeling applications. Note that the database employed in this study was compared against historical data [27] and also evaluated for reproducibility prior to application. Future research studies in the field of AI should exercise caution when selecting their database for model development.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Input Parameters**} \\ \cline{2-7}  & **Number of Cycles** & \(S\) & \(R\) & \(\rho\) & **Vertical Stress** & **CSR** \\ \hline ANN & 3 & 5 & 2 & 4 & 1 & 6 \\ SVM & 4 & 5 & 3 & 2 & 1 & 6 \\ Total Score & 7 & 10 & 5 & 6 & 2 & 12 \\ Ranking & 4 & 5 & 2 & 3 & 1 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ranking results of variable importance for the proposed ANN and SVM models.
# Study on Shear Performance of Web-Embedded Composite Shear Connector
## 2 Push-Out Test  
### Specim Design and Manufacture  
In this study, the shear properties of WECSC were analysed via a push-out test [25; 26]. The concrete grade was C50, and the unilateral volume was 180 mm \(\times\) 430 mm \(\times\) 500 mm. To eliminate the end-bearing effect, a gap of 100 mm \(\times\) 70 mm \(\times\) 50 mm was set in the inner-middle part of the outsourced concrete. The hole steel web was 16 mm thick using a Q345 steel plate with dimensions of 480 mm \(\times\) 320 mm and a hole diameter of 44 mm. The steel bar model was HRB400, with a length of 400 mm and diameters of 14, 18, and 22 mm, respectively. The stud was used in accordance with the literature [27], with the selected model being \(\mathcal{O}\)13, composed of ML-15. Further, HRB400 steel bars with a diameter of 10 mm were selected for the stirrup. The specific structure and size of the specimen P-18-S are shown in Figure 3.  
In this experiment, the sizes of the perforating steel bar, stud, and end bearing action of the WECSC were selected as the test analysis parameters (Table 1). There were seven specimens in this test, which were divided into six types. The P-0 specimens were connected to only a perforated steel plate. The P-18 specimens were connected to the steel bars. P-n-S (n = 14, 18, and 22) specimens were set to penetrate the rebar bars and stud connections. Based on the P-18-S specimens, in P-18-S# specimen, the voids in the inner middle and lower parts of the enclosed concrete were eliminated, and an end-bearing area was added.  
Figure 2: The flowchart.  
Among them, two specimens of P-18-S were used for push-out test and low-cycle repeated load test, while the other specimens were only used for push-out test. The connection details are shown in Figure 4.  
The actual compressive strength of concrete was \(f_{cu}=56.9\) MPa, measured through the standard cube test. Parameters of steel components are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Concrete Label** &
\begin{tabular}{c} **Diameter of Penetrating** \\ **Rebar/mm** \\ \end{tabular} & **Stud Type** & **End Pressure** \\ \hline P-0 & C50 & / & / & none \\ \hline P-18 & C50 & 18 & / & none \\ \hline P-18-S & C50 & 18 & \(\mathcal{O}13\) & none \\ \hline P-18-S\# & C50 & 18 & \(\mathcal{O}13\) & yes \\ \hline P-14-S & C50 & 14 & \(\mathcal{O}13\) & none \\ \hline P-22-S & C50 & 22 & \(\mathcal{O}13\) & none \\ \hline \hline \end{tabular}
\end{table}
Table 1: Internal component parameters of test specimens.  
Figure 3: P-18-S specimen structure size diagram (mm). (a) Front view; (b) side view; (c) top view; (d) 3D view.
# Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks
## 3 Multi-Headed Convolutional Neural Networks Architecture  
Since the further abstract expression of the input feature sequence by a series of convolution and pooling operations of each CNN allows the network to better learn the input feature sequence from multiple perspectives, this paper designs and implements the network structure of MHCNNs. MHCNNs input the one-dimensional audio feature sequence into three CNNs with different operations, combines the output data abstracted by the three CNNs, and inputs them into several fully connected layers for classification.  
For the first input of the network, a 1D cough audio feature sequence is input to MaxPooling1D layer after passing through two Conv1D layers in turn, and then input to GlobalAveragePooling1D layer after being abstracted through another two Conv1D layers in turn. Finally, the Dropout layer and the Flatten layer are added. After being processed by this series of layers, the length of audio feature sequence would be 128.  
For the second input of the network, 1D cough audio feature sequence first passes through two Conv1D layers and one MaxPooling1D layer, and then only one Conv1D layer is input to the GlobalAveragePooling1D layer. By the Dropout layer and the Flatten layer, the final sequence data size is 256.  
For the third input of the network, 1D cough audio feature sequence is 7936 in length after being processed by a four-layer network consisting of Conv1D layer, Dropout layer, MaxPooling1D layer, and Flatten layer.  
Then, the abstracted sequence data of three different lengths are combined, and the final output is obtained after passing through four Dense layers. Its network architecture is shown in Figure 2. Among them, the activation functions of all convolution layers use Relu. The activation function of the last dense layer is set to Softmax.
# Automated Hybrid Model for Detecting Perineural Invasion in the Histology of Colorectal Cancer
## 1 Introduction  
Perineural invasion (PNI) in colorectal cancer (CRC) is a well-established independent prognostic factor [1, 2], with an incidence range from 9% to 30% [3, 4]. PNI is defined as tumor invasion into, around, and through neural structures [5]; it is the distinct route through which cancer cells spread and metastasize to adjacent or distant organs [6, 7]. PNI detection is associated with response to adjuvant chemotherapy [8]. Therefore, a meticulous evaluation of PNI and prognostication on the basis of the standardized pathology report are mandatory in routine pathology practice [9, 10].  
Despite its importance, the histologic evaluation of PNI is a cumbersome and time-consuming process, with a high risk of misdiagnosis [11]. [PERSON] et al. [12] reported that only 7.5% of patients were PNI-positive in original pathologic reports; however, reviewing their PNI status revealed that 24.3% of patients were PNI positive. Thorough histological inspections are necessary for reducing the misdiagnosis rate. However, pathologic diagnosis of PNI is tedious and increases the workload of pathologists. Given the increasing workload for pathologists and their critical shortage nationally and globally [13, 14], developing an automated screening tool for PNI is crucial.  
Deep learning (DL) methods have achieved promising results in medical image analysis [15, 16], surpassing human performance [17]. In computational pathology, histology-based DL approaches have facilitated computer-aided diagnostics, including tumor detection, classification, segmentation, and even quantification of established biomarkers,such as tumor-infiltrating lymphocytes [18; 19; 20]. Until now, limited studies have focused on PNI detection in computational pathology [21; 22]. However, as with DL-based approaches, these studies exhibited the drawback of failing to interpret unpredictable failures. The lack of interpretability in \"black-box\" modeling limits real-world application, which may lead to user distrust [23; 24].  
In this study, an interpretable DL-based PNI detector was developed through CRC histology, demonstrating the potential of computer-aided diagnosis for PNI screening. The proposed approach could become an alternative to the conventional methods of pathologic diagnosis of CRC.
## 3 Problem Formulation
### Bit Error Probability  
The error probability for the \(M\)-QAM modulation can be approximately obtained by [23]  
\[P_{\epsilon}^{QAM}(\gamma)=1-(1-2(1-\frac{1}{\sqrt{\mathcal{M}}})Q[\sqrt{\frac {3\gamma\log_{2}\mathcal{M}}{(\mathcal{M}-1)}}])^{2}. \tag{12}\]  
Assuming, as an approximation that \(\gamma\) is Gamma distributed, the mean bit error probability \(\bar{P}_{\epsilon}^{QAM}\) can be calculated by  
\[\bar{P}_{\epsilon}^{QAM}(\gamma)=\int\limits_{0}^{\infty}P_{\epsilon}^{QAM}( \gamma v)f_{\|\mathbf{w}\|^{2}}(v)dv, \tag{13}\]  
where \(f_{\|\mathbf{w}\|^{2}}(v)\) is the pdf of \(\|\mathbf{w}\|^{2}\) as a function of an independent variable \(v\).  
Figure 2: Hellinger distance.  
[PERSON] et al. [19] derived a close upper bound for the mean error probability of an \(M\)-QAM schema under Gamma fading by using the approximation  
\[\bar{P}_{e}^{QAM}(\gamma)\approx\frac{4}{\log_{2}M}Q(\sqrt{\frac{3\gamma\log_{ 2}M}{M-1}}). \tag{14}\]  
From the Chernoff bound \(Q(x)\leq\frac{1}{2}e^{-\frac{1}{2}x^{2}}\), they obtained the following upper bound for BER  
\[\bar{P}_{e}^{QAM}(\gamma)<\frac{1.38629(\frac{2.16404\gamma\log(M)}{(M-1)\beta }+1)^{-\alpha}}{\log(M)}, \tag{15}\]  
which is close to the exact solution.
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 3 IANT Scheme Design
### The Received Signal on the Eavesdrop Side  
The signal received by the eavesdropper can be expressed as:  
\[y_{e}(n)=h_{e}s(n)+s(n)B_{i}(n)f_{i}\eta_{i}h_{i}+B_{j}(n)f_{j}\eta_{j}h_{j}+ \omega_{e}(n) \tag{13}\]  
when it comes to the eavesdropper side operation, it also calculates \(\Theta_{0}\) and \(\Theta_{i}\) based on the average power of the signal received by its receiver, as follows:  
\[\Theta_{0}=\frac{1}{N_{0}}\sum_{n=1}^{N_{0}}\left|y_{e}(n)\right|^{2} \tag{14}\]  
\[\Theta_{i}=\frac{1}{N_{0}}\sum_{n=(i-1)*N_{0}}^{i*N_{0}}\left|y_{e}(n)\right|^ {2} \tag{15}\]  
\[\Theta_{B}(q)=\frac{1}{N_{t}}\sum_{n=(q-1)*N_{t}+1}^{q*N_{t}}\left|y_{e}(n+N_{ p}+N_{s})\right|^{2} \tag{16}\]  
Similarly, receiver of the eavesdropper compares the calculated \(\Theta_{B}\) with \(\Theta_{0}\) and \(\Theta_{i}\) and then determines whether the symbol received is '1' or '0' according to the following rules:  
\[\hat{B}_{i,e}(q)=\left\{\begin{array}{ll}0&\text{if }|\Theta_{B}(q)-\Theta_{0}|<| \Theta_{B}(q)-\Theta_{i}|\\ 1&\text{if }|\Theta_{B}(q)-\Theta_{0}|>|\Theta_{B}(q)-\Theta_{i}|\end{array}\right. \tag{17}\]
## 5 Trust Adaptation
### Experimental Results
#### 5.2.2 Migration Workflow
_(4) Integration test of the Web agent:_ To validate the deployment, the orchestrator calls the interface \"add_target\" of the \"InterceptingWebAgent\" to run an integration test. The test sends a dummy SOAP request (with no authentication) to the Web agent endpoint. If the request is denied, the Web service is secured successfully. Otherwise, the orchestrator disables the delivery service from the application server. If the test is successful, the orchestrator publishes the endpoint as well as the trust capability of the delivery service (from Figure 9b) in its registry. _(5) Update:_ In this step, the shopping service signs a business contract to use the delivery service. An administrator provisions the shopping service to OpenStack. Before the deployment, the orchestrator allows the administrator to confirm the security matching between the two services. Then, the orchestrator sets up the Proxy node according to the trust capability of the delivery service. For direct trust, it imports the public key of the delivery service to the truststore. For indirect trust, it configures the Proxy to trust IDaaS1 and to impersonate the user (see the Proxy implementation). _(6) Integration test of the Proxy:_ The orchestrator also calls the interface \"add_target\" of the Proxy node to check if the delivery service's endpoint is accessible. According to the trust capability, the Proxy can call the delivery service directly (i.e., direct trust) or indirectly with the token issued by IDaaS1 (i.e., indirect trust). Otherwise, the test will fail. _(7) Termination:_ Now the delivery service is migrated from OpenStack to AmazonWS. In OpenStack, the orchestrator removes any credentials of the delivery service from the Proxy node, and then it terminates the delivery service VM. _(8) Migration:_ In this step, the orchestrator in each domain updates the deployment of the Web service under its control. In the AmazonWS domain, the topology of the delivery service is updated with the new trusted endpoint (i.e., IDaaS2). Then, the orchestrator deploys the delivery service and configures the \"InterceptingWebAgent\" to trust IDaaS2. In the domain of OpenStack, the delivery service is now an external service. The administrator defines a PEP node (as a substitution for the external service) with the \"trustee\" updated to the endpoint of IDaaS2. Then, the orchestrator can read this trust capability and configures the \"Proxy\" node for identity federation (see the Proxy implementation).
# AVSS: Airborne Video Surveillance System
## 3 Design of the Proposed System
### Surveillance Server  
Surveillance server directly interacts with the user to set mission and provide necessary information regarding the mission. The user coordinates the mission by setting the surveillance area and the number of UAVs to be deployed. When the user inputs the surveillance area and the number of UAVs to be deployed, the server calculates the individual placement of the UAV using the UAV placement algorithm. Details of the UAV placement algorithm is provided in Section 4.  
The server performs supportive but critical functions for the fleet. First, one of the critical limitations of a multi-rotor is the short flight time. This can be overcome by the rotation of the UAVs, which is conducted by the server. When a UAV has a low battery, the server reads the battery status and considers the UAV faulty. All faulty UAVs are removed from acting UAVs, and replaced with another UAV if any other is available. If none is available at the moment, the placement algorithm, which will be discussed shortly, relocates UAVs in the high-priority-first manner. All placement algorithm is run on the GCS, because GCS has much higher computational capability for the lack of weight and size limitations.
# Settlement Foundations by Exploring the Collapse of Unsaturated Soils
## 3 Validation of the Theoretical Framework
### Hydraulic Variation  
The settlement's evolution over time was calculated for both the footing and transient flow condition. A 100 kPa load was subjected to the footing. A transient water flow simulation was then performed to model a 60-day rainfall. Figure 6 shows the evolution of the pressure head based on the findings of the numerical models. The pressure head in the soil domain is presented before and after 60 days of rainfall. The pressure head after 60 days of rainfall shows changes above the water table due to the infiltration rate. An increase in the pressure head is observed above the water table. It should be noted that below the foundation, there is a zone in which the pressure does not change due to the waterproofing of the building.  
Figure 7 highlights the changes in the suction profile resulting from infiltration at different depths and times, both at the center and corners of the foundation. The initial suction was adjusted at 13 kPa at the top and at 0 kPa at a depth of 0.65 m and below, reflecting the natural water content. Figure 7a shows the results of the simulation for the suction profile changes over 60 days at the corner of the footing. After this period, almost all the soil at the corner of the footing becomes nearly saturated (Figure 7b).  
Figure 6: The evolution of the pressure head (m): (**a**) before infiltration and (**b**) after filtration.
# Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
## 2 Study Area and Data
### Satellite Data
SAR images have high speckle noise; hence, many filters, including Lee, Kuan, Frost, and Gamma MAP have been used to despeckle SAR images in the literature. In this study, Lee filter [37], one of the well-known despeckling filters, was applied to remove noise with 3 \(\times\) 3 window size. Geometric correction was conducted using ninety-five ground control points (GCP) collected from both Landsat and SAR images, and the resultant root mean square error (RMSE) ranged from 10 m to 15 m (less than one pixel). Elevation data at 30 m resolution from the Shuttle Radar Topography Mission (SRTM) digital elevation models (DEM) that was obtained from Earth Explorer ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/))) was used as an input variable for paddy rice classification. DEM provides land cover related information. For example, most paddy rice is located in relatively flat areas and low elevations. Many studies have used elevation data as input variable for land cover classification [38; 39; 40].
###### Abstract  
This study evaluated if the material, the substrate, and the cement have no influence on the color of pre-shaded monolithic zirconia crowns. The specific effect of the cement over each substrate/brand group was also studied. Two commercial brands of zirconia, Amann Girrbach (AG) and Zirkonzahn (ZZ), were used to produce crowns that were placed over three substrates (natural tooth, zirconia, metal) using two different resin cements (Ivoclar AG (Shaan, Liechtenstein) Neutral and Light) or glycerol (as the control) (\(n=10\)). Lightness (L*), chroma (C*), hue (h*), and color difference (\(\Delta\)E) of each crown were measured using a VITA Easyshade V(r) spectrophotometer (VITA Zahnfabrik, Bad Sackingen, Germany), following the standardized reference. Since normality was not verified by the Shapiro-Wilk test, data were statistically analyzed using the Kruskal-Wallis test for group comparisons and [PERSON]'s post-hoc test for multifactorial variance analysis (\(\alpha=0.05\)). \(\Delta\)E medians ranged between 1.3 in the AG/zirconia substrate/glycerol group and 8.0 in the ZZ/metal substrate/light cement group. In general, lower values of \(\Delta\)E were recorded in AG restorations compared to ZZ (\(p<0.05\)), zirconia, and natural tooth substrates compared to metal (\(p<0.001\)) and neutral compared to light cements (\(p<0.05\)). Specifically, over the metal substrate, AG crowns with neutral cement and ZZZ crowns with neutral cement and glycerol showed lower \(\Delta\)E values (\(p<0.05\)). Over the zirconia substrate, light cement presented higher \(\Delta\)E values than glycerol in both brands but similar to neutral cement. Over the natural tooth, no significant differences were observed between cements (\(p>0.05\)) in the AG brand, while in the ZZ group, light cement showed higher \(\Delta\)E values (\(p<0.05\)). The final color of the restorations was significantly influenced by the zirconia brand, substrate type, and resin cement. Light cement led to greater color variations, particularly in ZZ restorations. These findings highlight the importance of material selection in achieving esthetically pleasing zirconia restorations.  
color measurement; zirconia; resin cements; substrates +
Footnote †: journal: Applied sciences  
Article
## 4 Conclusions  
We have presented a THz-CVQSS scheme based on thermal THz sources and heterodyne detectors, which can be efficiently immune to Trojan horse attacks. On the whole, THz-CVQSS protocol actually establishes \(n\) independent point-to-point CVQKD links based on GMTS in each round of quantum transmission. By connecting THz-CVQSS to CVQKD based on GMTS, the security of the proposed protocol can be proved. We analyze the performance of THz-CVQSS in inter-satellite links. Results show that THz-CVQSS can achieve the optimal SKR with higher frequency, the optimal \(V_{M}\) value, higher reconciliation efficiency and lower excess noise in inter-satellite links. We also verify the feasibility of inter-satellite long-distance THz-CVQSS. In particular, when the frequency is increased to 50 THz, the maximal transmission distance can reach 2300 km with the comparatively high excess noise \((\xi_{0}=0.003)\) and \(n=5\) players in the finite-size scenario. This work can provide an effective way to build an inter-satellite quantum communication network. We expect that in future work, some non-Gaussian operations, e.g., quantum catalysis and photon subtraction, can be used to further improve the performance of THz-CVQSS.  
Conceptualization, C.L. and C.Z.; methodology, C.L. and C.P.; formal analysis, Z.L.; validation, M.N.; writing--original draft preparation, C.L.; writing--review and editing, C.Z. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61372076, 61971348, and 62001351), Foundation of Shaanxi Key Laboratory of Information Communication Network and Security (Grant No. ICNS201802), Natural Science Basic Research Program of Shaanxi, China (Grant No. 2021 JM-142), and Key Research and Development Program of Shaanxi Province, China (Grant No. 2019 ZDLGY09-02).  
The authors declare no conflict of interest.
## 4 Results
### Estimated Power Consumption  
The estimated power consumption of the XCZU9 EG-FFVB1156-2-e SoC-FPGA, when operating a high-sensitivity GNSS receiver, is 12 W. This estimate, provided by FPGA design tools, assumes an average processor load of 75%. Such a load occurs when the receiver processes a large volume of GNSS signals simultaneously, including 10 signals each from Galileo E1, GPS L1 C/A, Galileo E5a, and GPS L5. In contrast, in [21], the authors tested the GNSS-SDR software receiver on a personal computer (PC) with a 28 W 11 th generation Intel Core i7-1185G7 processor at 3 GHz. Processing 26 channels in real-time across multiple bands and constellations resulted in a similar processor load. This comparison suggests that the SoC-FPGA implementation is capable of processing over 50% more signals in real time within the same power budget.  
To assess the average processor load when processing multiple signals, the authors connected the receivers to the GESTATI testbed's rooftop GNSS antenna facility [46]. The receivers were allowed to simultaneously track the same signals across multiple channels, to ensure that every channel was tracking at least one satellite signal. We estimated the average processor load using the receiver's operating system tools.