esa-sceva/satcom-chunk-collection huggingface dataset:
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 2 Materials and Methods
### Impact Dynamics of the Shaft GFRP  
Quasi-static (QS) response by indentation was measured from half-circular (180\({}^{\circ}\)) panels (see Figure 3a) of the GFRP profile (\(D_{outer}=\) 168 mm, see Section 2.1). The panel specimen (length of 250 mm) was supported by two half-circular steel sections, which were set 200 mm apart (defining the measurement area). The upper side of the specimen was supported in the areas of steel sections resulting in a semi-rigid boundary condition. The specimen was loaded in a universal testing machine (30 kN load cell, model 5967, Instron, High Wycombe, UK) by a hemispherical head (radius 10 mm). The loading was subjected in the middle of the specimen by using a test rate of 1.0 mm/min. 3D Digital  
Figure 2: A graph of the main analysis phases for an integral smart pole allowing current 5G services.  
image correlation (DIC) was used to record and analyze (Davis 8.4 software, LaVision, Gottingen, Germany) the displacement on the lower surface of the GFRP panel. In addition, the contact force was measured by the load cell located over the loading head.  
The testing was continued on the panel specimens loaded by a falling/drop-weight impactor (FWI Type 5, Rosand, Leominster, Herefordshire, UK) (see Figure 3b). Instead of the semi-rigid support, an open boundary was used for the half-circular specimen, i.e., the upper side of the specimen was able to deform freely during the dynamic loading. The specimen was loaded by a hemispherical impactor head (radius 10 mm) weighting 7.67 kg with an impact energy of 100 J. The contact force of the impactor head was measured by a piezo-electric load cell located above the impactor (Type 9031A, Kistler, Winterthur, Switzerland). The displacement of the impactor head was calculated analytically from the contact force-time response.  
In the last test step, a tubular shaft specimen was tested using the drop-weight impactor (Figure 3c). A shaft length of 780 mm was used in a cantilever support mode and loaded with a half-circular shaped (2D) impactor head weighting 8.49 kg. The impact energy in the testing was 100 J. Similar to the testing of the half-circular specimen, the contact force of the impactor head was measured by the piezo-electric load cell and the displacement of the impactor head was calculated analytically from the contact force-time response.
## 5 Challenges and Future Research Directions
### Lack of Training Data for the AI Models  
The adoption of ML in UDNs requires large-scale datasets for training. However, there is a shortage of publicly available datasets for developing AI-based network optimization models. Future research should focus on data augmentation techniques, synthetic dataset generation, and federated learning methods to bridge this gap [46].
## 4 Modeling Setup and Realistic Experimental of Simulation Scenarios
### Modeling Scenario  
Realistic simulation scenarios using the Orolia Simulator considered three LEO satellites to estimate and track a UAV flying in a circle at high speed and powerful rotation. The UAV emits an RFI signal on the central frequency 1575.42 MHz, corresponding to the three GPS receivers onboard the three LEO satellites. The experiment has considered a binary phase-shift keying (BPSK) modulation scheme. Three instants are created and summarized as  
* **Instance-1:** Simulate UAV trajectory, generate RFI signal (common for three scenarios), and create earth-orbiting spacecraft (setup-LEO satellite # 1) 1. Simulation of UAV Circular Trajectory. 2. Create new radio assign # 0. 3. Vehicle- Keplerian orbital elements setup. 4. Save it as Master into the named file (sdx format).
* **Instance-2:** Creating earth-orbiting spacecraft (setup LEO satellite # 2). 1. Create a new radio and assign # 1. 2. Vehicle-Keplerian orbital elements setup. 1. Change the reference time. 2. Other Ephemeris Elements. 3. Save it as Slave into the named file (sdx format).  
* Creating earth-orbiting spacecraft (setup-LEO satellite # 3).
* Repeating the same steps of the Instance-2.
* Save and Record I/Q files (CSV format), as well export RAW logging files for reference. Figure 9 illustrates snapshots from the three Instances.  
I/Q files will be used as input information at the realistic scenarios of geolocation and tracking approaches. Useful quasi-real data extracted from the Orolia Simulator is presented in Appendix B.
## 3 Results and Discussion
### The Relationship between the Response Variables  
For a better understanding of the results, relationships were set up between the response variables, such as: compressive strength with dry density (Figure 10a); compressive strength with air void (Figure 10b); thermal conductivity with dry density (Figure 11a); water absorption with air void (Figure 11b); and air void with dry density (Figure 11c). As expected, the compressive strength increases with increasing density and an increase in the number of pores (air void) reduces the compressive strength [22; 23; 29; 36; 51; 71; 72; 73]. Also, the thermal conductivity depends on the density, i.e., by increasing the density the thermal conductivity increases, in accordance with other studies on this type of material [15; 74; 75; 76; 77]. Another observation is that the values of thermal conductivity for densities above 1300 kg/m\({}^{3}\) have greater dispersion. This dispersion is explained by the dispersions observed in experiments with CPV-ARI cement and with the addition of additives.  
Figure 9: Thermal conductivity related to (**a**) cement type, (**b**) foam amount, and (**c**) additive type for different curing times, where the middle point is the mean, the box is the confidence interval (95%), and the whisker shows the standard deviation.  
For the factor levels studied, it can be observed that the water absorption increases linearly with the increase in air voids and that the air voids decrease with an increase in the density. These results are also in agreement with the results found in the literature [73; 78; 79; 80; 64].
# Analysis of Non-Minimum Phase System for AC/DC Battery
## 3 Converter Analysis
### Minimum Phase System Criteria  
The above numerical analysis proves that the transfer function for the current loop \(Gi_{L1}d\_u(s)\) in boost mode has a RHP zero and, consequently, a non-minimum phase characteristics. Therefore, to avoid closed-loop instability, a low closed-loop bandwidth system must be designed that will decrease the PF and will increase the total harmonics distortion (THD) of the line current. The question here that will be answered in this section is \"can this loop be a minimum phase by changing converter parameter?\". Transfer function numerator of the current loop \(Gi_{L1}d\_u(s)\) in boost mode (4) will be investigated for this purpose. It can be modeled in the 3 rd order polynomial as follows:  
\[P(s) = s^{3}+a_{2}s^{2}+a_{1}s+a_{0}. \tag{9}\]  
According to the Routh-Hurwitz stability criterion, coefficients of (9) should be positive to have a stable minimum phase system. By comparing both equations in (4) and (9), the most attractive condition for stabilize this converter in boost mode can be extracted from \(a_{2}\) coefficient and it can be expressed as follows:  
\[C > C_{o}(\frac{V_{o}}{V_{g}}-1), \tag{10}\]  
Equation (10) is a condition for \(a_{2}\) to be positive. Inequality in (10) is a necessary condition for boost mode transfer function \(Gi_{L1}d\_u(s)\) to be minimum phase (the Routh-Hurwitz stability criterion has more conditions, and all must be accomplished to be minimum phase). Figure 5a,b shows the bode plot and its pole-zero mapping for the \(Gi_{L1}d\_u(s)\) transfer function after applying the condition represented in (10). By making the output capacitor \(C_{o}\) equal to 8 uF and intermediate capacitor C equal to 800 uF. As shown in the bode plot, the system becomes minimum phase with no RHP zero in the pole-zero map. Figure 6 shows the pole-zero map of this new converter numerator transfer function \(Gi_{L1}d\_u(s)\) in boost mode with different output power. For all operating regions, the current loop is minimum phase and doesn't have a RHP zero.
# Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
## 5 Management of WSNs Based on SDN
### Security Management  
The architecture of WSNs based on SDN is such that a logically centralized controller poses a security risk as any compromise in the controller, cluster-head in clustered topologies or even the link to the controller can affect the system QoS. A malicious attacker can introduce false data and parameters  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Features** & **Controller** & **Enabling** \\ \hline SDWN [60] & Duty cycling, data aggregation & Distributed & Software, Hardware \\ \hline Smart [14] & Resource allocation & Centralized & Software and Hardware \\ \hline Multi-task [122] & Resource allocation, QoSen, scheduling & Centralized & Software \\ \hline SDWSN-RL [123] & Load balancing, traffic control & Distributed & Software \\ \hline Wireless power transfer [126] & Energy harvest, Optimization, efficiency & Centralized & Software, hardware \\ \hline \hline \end{tabular}
\end{table}
Table 3: SDN-based Energy management schemes for WSNs.  
in the network or a Denial of Service resulting in system unreliability. A few contributions have been made in managing security for SDN-based WSNs, [PERSON] et al. [129] for example describe the need for security management in both wired and wireless networks based on SDN for Internet of Things (IoT) and propose a security model for IoT SDN architecture. An extension of the proposed architecture for IoT is made to include sensors integrating the aspect of guaranteeing the security of the network based on the grid of security concept embedded in each controller. The grid of security concept has been presented previously as an approach to securing networks by ensuring that communication takes place between trustworthy devices regardless of system policy control supported by a communication model to help structure the service distribution of security among nodes [130]. The overall concept is to decentralize the management of security of the network to ensure continuity in security during failure.  
Another security management contribution has been the integration of Secure Multiparty Computation (SMC) to secure private sensory data in SDN-based WSNs [131]. The focus is the application of SMC in securely processing sensory data between the SDN-based WSN and the web server. [PERSON] et al. [131] present a security model based on the SMC protocol allowing clients in the SDN-based WSN to connect and disconnect to the web server arbitrarily. A lottery SMC protocol is constructed for the performing selection in SDN-based WSNs with an encryption based on the layered homophobic function.
## 6 Conclusions  
This study proposes URSA. Each node begins the process of uneven clustering according to the distance to the sink node. Each cluster head node then uses the hybrid radius path-selection method to construct a connected path to the sink node and adjusts its own depth while maintaining the uneven layout on the water surface. For each in-cluster node, a cluster head node considers the redundant coverage rate on the position where the distance between the in-cluster nodes and its basic nodes is greater than \(2R_{s}\), equal to \(2R_{s}\) and less than \(2R_{s}\) and, finally, selects its position to minimize its hop number on the condition of declining coverage rate. The proposed algorithm, taking into account the indicators of the network coverage rate and network connection rate as the same with the other mobile-restricted self-deployment algorithms, additionally considers network reliability and the balance of the energy consumption of the network during node deployment. In addition, comparing to current algorithms only considering the redundant coverage rate of one or two fixed positions during optimizing the network coverage rate, the proposed algorithm additionally considers the coverage redundancy rate of all positions that the node may reach. The simulation results under different numbers of nodes show that compared to CDA, URSA has a greater network coverage rate, less and balanced network energy consumption and greater network reliability.  
As future work, we plan to extend the ideas in this paper considering the node deployment about the node probability perception model and free-to-move nodes. The in-cluster position adjustment strategy needs to be modified to some degree. In addition, we also plan to consider some more realistic models, such as an underwater environment with some obstacles, to improve the algorithm adaptability. Meanwhile, we plan to conduct experiments on the simple sensor nodes and to further verify the algorithm practicability.  
This paper was supported by the National Natural Science Foundation of China (NSFC61273072) and the Environmental Protection Plan of Zhejiang Province (2013A034), the National Natural Science Major Foundation of Research Instrumentation of China (61427808), the Key Foundation of China (61333009), the National Key Basic Research Program of China (2012 CB821204).  
[PERSON] and [PERSON] conceived and designed the research; [PERSON] and [PERSON] performed the research; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Analytical Methods  
The examined substrates were freeze dried because this process maintains their nutritional and enzyme content. Subsequently, representative samples were taken for the evaluation of the physicochemical parameters (crude proteins, TSS, RSS), fibers (crude fiber substances, cellulose, lignin, and ashes), and \(\beta\) glucan content. Moisture and ashes were assessed according to the AOAC methods [17]. Crude protein determination was performed according to the Kjeldahl method, which is considered as AOAC's official method for total nitrogen evaluation [17]. The protein content of the examined substrate was estimated by multiplying the determined nitrogen content (Kjeldahl method) by a factor of 6.25. Total soluble sugars (TSS) were estimated by the phenol-sulfuric acid method of [PERSON] et al. [18], and reducing soluble sugars (RSS) were estimated by the 3,5-dinitrosalcylic acid (DNS) method of [PERSON] [19]. Sugar evaluations were performed on the aqueous extracts of the samples. Crude fiber substance (CFS) presence was determined in accordance with the Weende method (AOAC Official method 978.10) [17]. Cellulose and lignin content were determined according to the acid-detergent fiber (ADF) method (AOAC Official Method 973.18) [17]. Finally, \(\beta\)-glucans were assessed using the Megazyme enzymatic assay kit (\(\beta\)-Glucan Assay Kit Yeast & Mushroom, Megazyme Product code: K-YBGL, Bray, Ireland). More specifically, for the determination of total glucan content, ice cold 12 M H\({}_{2}\)SO\({}_{4}\) was used for the solubilization of 1,3:1,6-\(\beta\)-D-glucans, 1,3-\(\beta\)-D-glucans, and \(\alpha\)-glucans and were then hydrolyzed to near completion in 2 M H\({}_{2}\)SO\({}_{4}\). The remaining fragments of glucan were quantitatively hydrolyzed to glucose using exo-1,3 \(\beta\) glucanase and \(\beta\)-glucoseidase; \(\alpha\)-glucan and sucrose were hydrolyzed to D-glucose and D-fructose, respectively, with glucose's determination being achieved with amyloglucidase and invertase using GOPOD reagent. \(\beta\)-glucan content evaluation is determined by the difference between the total glucan content minus the \(\alpha\)-glucan content. The results of the aforementioned analyzes are expressed as g/100 g of dry weight.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations
### Desert Studies  
In [150], the authors investigated the potential of using CYGNSS GNSS-R data to retrieve information about desert roughness. The study focused on the Sahara Desert and involved analyzing changes in reflectivity over time to identify variations in different types of land surface such as reliefs and dunes. The authors observed that the reflectivity of each type of land surface was highly stable over time, which allowed for data to be gridded at a 0.03\({}^{\circ}\)\(\times\) 0.03\({}^{\circ}\) resolution and averaged for a 2.5-year time period. The study demonstrated the potential of CYGNSS data to characterize different desert land surfaces. The authors found a strong correlation between roughness parameters and reflectivity and compared the behavior of GNSS-R reflectivity with that of the ALOS-2 SAR back-scattering coefficient, finding a strong correlation between the two.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### The Method of Nanofluid Preparation
#### 2.1.2 Two-Step Method  
In this method, different nanopowder materials like nanoparticles, etc., are utilized by physical, chemical, and mechanical methods [51]. The direct method of application includes first the dispersion of nanoparticles in the base fluid and then using a stabilization method. Although the resultant nanofluids have small size nanoparticles with high surface activity, they easily agglomerate. Ultrasonic waves are utilized to increase the stability of the obtained nanofluid and yield a dispersible suspension of nanoparticles. Some of the advantages of the two-step method are the low cost, simple process, large-scale nanofluid preparation, and industrial realization [52]. However, the stability of the nanofluids prepared in this way is weak, and using surfactants is not applicable at high temperatures. Therefore, some dispersing methods such as using ultrasound vibration, the addition of surfactant, pH variation of the base fluid, and ultrasonication are combined in some cases, to reach a suspension with better dispersion performance [53]. Nevertheless, it is recommended that nanofluids containing oxide nanoparticles may better be prepared by a two-step process, while this method is not suitable for metallic nanoparticles [54].
# Space Node Topology Optimization Design Considering Anisotropy of Additive Manufacturing
###### Abstract
This generated the best results for different manufacturing directions, enabling a comparison with isotropic optimization results and an exploration of how adding anisotropic parameters affects topology optimization. The conclusions obtained in this paper can provide a reference for the practical application of additive manufacturing and increase the applicability of topology optimization design.
# Skin Lesion Segmentation by U-Net with Adaptive Skip Connection and Structural Awareness
## 3 Proposed Method
### Loss Function  
For the main task, we use the combination of weighted binary cross entropy loss \(L_{\text{{WBCE}}}\) and Dice loss \(L_{\text{{Dice}}}\) due to the skewed distribution of lesion and non-lesion pixels. The function for \(L_{\text{{WBCE}}}\), \(L_{\text{{Dice}}}\), \(L_{\text{{mask}}}\) can be represented as below, respectively  
\[L_{\text{{WBCE}}}=-\frac{1}{N}\sum_{i=1}^{N}\beta\cdot y_{i}^{true}\cdot\log \left(y_{i}^{predict}\right)+\left(1-y_{i}^{true}\right)\cdot\log(1-y_{i}^{ predict}) \tag{2}\]  
\[L_{\text{{Dice}}}=1-\frac{2\sum_{i=1}^{N}y_{i}^{true}\cdot y_{i}^{predict}}{ \sum_{i=1}^{N}\left(y_{i}^{true}\right)^{2}+\sum_{i=1}^{N}\left(y_{i}^{predict }\right)^{2}} \tag{3}\]  
\[L_{\text{{Mask}}}=L_{\text{{WBCE}}}+L_{\text{{Dice}}} \tag{4}\]  
where \(y_{i}^{true}\) indicates the ground truth pixel label and \(y_{i}^{predict}\) indicates the predicted pixel label. \(\beta\) is a self-deduced coefficient related to the ratio of the object and background in the whole dataset. The weighted binary cross entropy loss deals with the pixel-wise classification while the Dice loss takes care of the intersection of area. Both of them can reduce the imbalance problem.  
For the axillary tasks, we use Log-Cosh loss to regress the distance map and the contour map. Log-Cosh loss is smoother than the mean squared error loss for regression tasks and less influenced by irregular incorrect prediction. The Log-Cosh loss function is written as  
\[L_{\text{{Log-Cosh}}}=\sum_{i=1}^{N}\log\left(\cosh\left(y_{i}^{predict}-y_{i}^ {true}\right)\right) \tag{5}\]  
After all, we have to optimize the total loss \(L_{\text{{Total}}}\) for our model  
\[L_{\text{{Total}}}=\lambda_{1}\cdot L_{\text{{Mask}}}+\lambda_{2}\cdot L_{ \text{{Distance\_map}}}+\lambda_{3}\cdot L_{\text{{Contour\_map}}} \tag{6}\]  
\(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) are the coefficients for lesion segmentation loss, distance map loss and contour map loss, respectively. The equal contribution of three tasks to the knowledge of the encoder is prone to the lack of attention to the main task, leading to the inefficient lesion segmentation and waste of supplementary data. Hence, we empirically set \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\) as 1, 0.01 and 0.001 so the network can fully exploit the provided information.
## 2 Materials and Methods
### Methods
#### 2.3.2 Spatial Correlation and Regression Model  
To compare the effectiveness of RS data obtained from the different sources, images were collected to coincide with the peak of the growing season. UAV images were taken on 29 June 2023, followed by satellite images of the same area from Planet Scope on the same day and from Landsat 8 OLI and Sentinel-2 on 26 and 30 June, respectively. Since the acquired images have different spatial resolutions, resulting in the study area covering a different number of pixels, resampling was performed in the ArcGIS Pro environment using a Bilinear method based on the spatial resolution of the Planet Scope satellite images (3 m). Downscaling and upscaling were implemented for the spatial resolution of the Landsat 8 OLI and Sentinel-2 data and the UAV data (P4 Multispectral), respectively. As a result, the same resolution and number of pixels were obtained for the study area across all four sources. Based on the resampled images, vegetation indices NDVI, NDWI, GNDVI, GLI, EVI, DVI, SAVI, MSAVI, and GSAVI were calculated for all images.  
The normality distribution of data was checked using the Anderson-Darling and Kolmogorov-Smirnov tests suitable for large data sets [75]. Both tests indicate that the data doesn't follow a normal distribution. Correlation analyses were carried out using the non-parametric Spearman correlation method, with significance levels \(\alpha\) = 0.05 (*) and \(\alpha\) = 0.01 (**) and \(p\) < 0.01.  
The classification of correlation was carried out according to Table 4 [76].  
Linear regression was employed to assess the reliability of the data obtained from the different sources. The GLI index, which demonstrated the best correlation, was selected for the regression analysis. The input data for the linear regression model consisted of satellite-derived data, which were used to predict UAV data. Three metrics--the determination coefficient (R\({}^{2}\)), root mean square error (RMSE), and residual prediction deviation (RPD) Table 5 [77]--were applied values to determine the reliability and accuracy of the predictions.  
\begin{table}
\begin{tabular}{c c} \hline
**Correlation Coefficient** & **The Meaning** \\ \hline +1 & Completely positive correlation \\
0.70–0.99 & Strong positive correlation \\
0.50–0.69 & Moderate positive correlation \\
0.01–0.49 & Weak positive correlation \\
0 & No relationship \\ \hline \end{tabular}
\end{table}
Table 4: The value of correlation coefficient.  
\begin{table}
\begin{tabular}{c c} \hline
**Residual Prediction Deviation (RPD)** \\ \hline Excellent & \textgreater{}2.5 \\ Very good & 2.0–2.5 \\ Good & 1.8–2.0 \\ Fair & 1.4–1.8 \\ Poor & 1.0–1.4 \\ Very poor & \textless{}1 \\ \hline \end{tabular}
\end{table}
Table 5: The range of RPD values.