esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Other Key Components Selection
#### 3.5.3 Guidance, Navigation, and Control System
GPS receivers are the most commonly implemented technology used for determining altitude [23]. A recent development in this mature technology has produced the SkyFox Labs \"piNAV-L1\" (Figure 35) which is the markets first ultra-low power GPS receiver (Navo-Avionics, 2017) [57]. The device is specifically designed to provide continuous precise altitude determination for satellites in LEO. The advantage of this product is its ability to match the performance of space grade GPS systems while consuming only 10% of the equivalent power [57]. The typical power consumption of the product is 120 mW at 3.3 V 25 \({}^{\circ}\)C. It is rated to operate at altitudes of up to 3600 km, and at velocities up to 9 km/s [49]. The wide temperature range of operation is \(-40\)\({}^{\circ}\)C to 85 \({}^{\circ}\)C. The dimensions of the product are 75 \(\times\) 35 \(\times\) 25 mm. Figure 36 provides piNAV-L1 block diagram. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product** & **Manufacturer** & **Mass (kg)** & **Accuracy** & **Status** \\ \hline Static Earth Sensor & Maryland Aerospace, Crofton, MD, USA & 0.033 & 0.25 deg & TRL9 \\ Mini Digital HCI & Servo, Westbury, NY, USA & 0.050 & 0.75 deg & TRL9 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Earth Sensor Performance Data [23]. Figure 35: SkyFox Labs piNAV-L1 [57]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Product** & **Manufacturer** & **Type** & **Mass (kg)** & **Bias Stability** & **Random Walk** & **Radiation Tolerance (Krad)** & **Status** \\ \hline \multirow{3}{*}{MIRAS-01} & Surry Satellite & \multirow{3}{*}{3 axis MEMS} & \multirow{3}{*}{2.8} & \multirow{3}{*}{10 deg h\({}^{-1}\)} & \multirow{3}{*}{0.6 deg h\({}^{-1/2}\)} & \multirow{3}{*}{5} & \multirow{3}{*}{TRL9} \\  & Technology & & & & & & & \\ \cline{1-1} \cline{6-7} \cline{8-9} \cl
## 5 Conclusions  
This study develops a model for the evolution of water temperature fields by integrating a heat flux calculation model at the air-liquid interface, a vertical temperature profile calculation model, and a cold skin calculation model, based on the vertical temperature structure of water bodies. The research investigates the spatiotemporal variations in the vertical temperature profile of water, validates the existence of the cold skin phenomenon in natural environments, and analyzes the influence of individual environmental parameter changes on the properties of the cold skin. By considering the diurnal variations in environmental parameters, the study examines the diurnal patterns of cold skin properties and conducts experimental validation. The main conclusions are as follows:  
1. The vertical temperature profile of water is primarily influenced by wind speed and solar radiation. At night, an isothermal layer forms in the upper water column. Increased wind speed enhances water turbulence, accelerating heat exchange between different water layers. After solar radiation diminishes at night, the water body radiates heat outward. The heat exchange between the water and the atmosphere is relatively weak, while a strong temperature gradient exists within the water. Under external disturbances and heat conduction, the temperature of the upper water layer redistributes, becoming more uniform and forming an isothermal layer.  
2. The cold skin phenomenon is confirmed to exist in natural environments. It is influenced by a combination of factors, including latent heat flux, sensible heat flux, longwave and shortwave radiation, wind speed, and atmospheric pressure. Under certain conditions, a cold skin forms at the air-liquid interface, with a temperature slightly lower (by a few tenths of a Kelvin) than that of the subsurface water. The cold skin is typically very thin, with a thickness on the order of 2-5.0 mm.  
3. The properties of the cold skin are mainly affected by wind speed, the near-surface-to-air specific humidity difference, and the near-surface-to-air temperature difference. As wind speed increases, the cold skin thickness gradually decreases. Due to the combined effects of heat flux and cold skin thickness, there exists a critical wind speed at which the temperature difference of the cold skin reaches its maximum. The cold skin thickness decreases with  
**Author Contributions:** Conceptualization, F.L., C.S. and C.G.; Methodology, Y.D.; Validation, F.L. and Y.D.; Data curation, F.L. and C.G.; Writing--original draft, F.L.; Writing--review & editing, F.L.; Visualization, F.L. and C.G.; Project administration, C.S.; Funding acquisition, C.S. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 52271354) and the independent research projects of Naval Engineering University (No. 202100000221).  
Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 4 Results and Discussion
### Prohibited Zone with Beamforming Antenna
#### 4.2.2 Vertical Prohibited Zone  
Figure 16 illustrates the vertical prohibited zones for the RMa base station using the \(16\times 16\) beamforming antenna at \(\theta=0^{\circ}\).  
* The largest prohibited zone occurs at \(AGL=2000\) ft. At 200 ft (60.96 m), the radius of the prohibited zone shows little variation within the 0-200 ft height range. At 1000 ft (304.6 m), the radius of the vertical prohibited zone exhibits a significant change, ranging from 0.9 km to 2.8 km. Due to interference from side lobes, the shape of the prohibited zone becomes complex. At 2000 ft (609.6 m), the shape of the prohibited zone is similar to 1000 ft, varying from 3.4 km to 6.1 km.
* This implies that even when the aircraft reaches flight altitudes of 1000 ft or 2000 ft, stations on the mountain located as far as 2.8 km and 6.1 km away can still cause interference. However, base stations on the ground do not cause interference.  
In Figure 17, the prohibited zones are presented for a vertical scan angle (\(\theta\)) of the beamforming antenna set to \(10^{\circ}\).  
* At 200 ft, the impact of \(\theta\) is minimal. At 1000 ft, there is a notable change in the shape of the vertical prohibited zone, primarily due to the aircraft being within the main lobe range of the antenna. At 2000 ft, the shape of the vertical prohibited zone is similar to that at 1000 feet, with a range of change from 6.2 km to 6.7 km.  
Figure 16: Vertical prohibited zones of RMa at \(AGL=200\) ft, 1000 ft, 2000 ft (beamforming antenna (\(\theta=0^{\circ}\))). (**a**) \(AGL=200\) ft. (**b**) \(AGL=1000\) ft. (**c**) \(AGL=2000\) ft.
## 3 Results and Discussion
### Evaluation of the Degree of Vulcanization of NR Composites
The free radicals generated at the NR segments may interact with the walls of the nanotube resulting in a reduction in the mobility of the NR chains, thus producing a more rigid polymer network. Scheme 1 shows a possible vulcanization reaction mechanism of NR using the studied organic peroxides, including the possible reaction between the free radicals generated due to the thermal decomposition of the organic peroxide with the SWCNT surface. Additionally, the differences found in mechanical properties for the peroxides used are related to the cross-linking densities obtained in each case. This can be explained by the different reaction mechanisms that occurred, which are mainly based on the type and the free radical half-life (\(t_{1/2}\)). In general, organic peroxides first undergo homolytic cleavage to form primary radicals, which can be fragmented into secondary radicals, as shown in Scheme 1, modifying the chemistry of the generated radicals. Such is the case of DCP, where its homolytic cleavage leads to the formation of primary cumyloxy radicals, which can experience a beta scission to form secondary methyl radicals and acetophenone [39; 40; 41]. Methyl radicals are smaller and less sterically hindered, so that they are more accessible to reactive functional centers on rubber chains; in addition, they have a lower half-life, which changes the physical properties of vulcanizates and promotes a major degree of cross-linking, lower values of \(\overline{M_{c}}\) and, in consequence, better mechanical properties, as shown Tables 5 and 6. Apparently, this mechanism is favored when SWCNTs are employed in the formulations, resulting in lower values of \(\overline{M_{c}}\) due to the multiple interactions among the sp\({}^{2}\) carbon bonds of SWCNTs and free radicals generated during the vulcanization process (Scheme 1), which produce a more closed network with a larger number of cross-link points. Considering the other two peroxides (T29 and T145), a similar mechanism can occur; however, the mechanical properties observed in these composites were lower than those for DCP composites. These results can be attributed to the occurrence of reactions when adding peroxide radicals to the double bonds of unsaturated rubber chains or abstraction of reactive allylic hydrogen atoms [41]. Both mechanisms can proceed concurrently and be highly competitive, leading to the formation of multiple chain bonds, which can generate clusters that decrease the resulting mechanical properties.
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 4 Overview of Recent and Existing Commercial SATis5 Testbeds and Projects
### NOKIA 5G from Space and Edge Slicing in Next-Generation Virtual Private Network  
Nokia provides its view of 5G from space and the roles of satellites in 5G [19]. Nokia also foresees 5G signals from space complementing 5G terrestrial infrastructure on Earth allowing seamless connectivity to cars, trucks, airplanes, vessels, and IoT devices in remote and rural areas. One of Nokia's latest 5G business and technology innovations is the combination of 5G Virtual Private Network (VPN) service with Edge Slicing and Edge Cloud Applications, also known as 5G Edge Slicing [20]. The 5G Edge Slicing allows for an end-to-end solution that works across 4G/5G RAN, transport layer, 4G/5G NGC, and an enterprise cloud with a distributed cloud core deployed at the enterprise premises or in the proximity of the operator's 5G Edge. Figure 11(a) illustrates a concept for 5G Edge Slicing deployment for 5G terrestrial network deployment. Figure 11(b) shows a 4G/5G end-to-end solution for 5G VPN with Nokia Edge Slicing [20]. As mentioned earlier, for 4G networks, the base station is referred to as evolved Node B (also known as eNB or eNodeB).
## 3 System Model and Security Goals
### Security Goals
#### 3.3.4 Freshness and Message Integrity  
If data are altered by an intruder, this also causes damage to the hospital's reputation. So, security systems used in hospitals should ensure the freshness and integrity of the transferred data. Random numbers and timestamps are used for data freshness.
###### Abstract  
Birdsong classification plays a crucial role in monitoring species distribution, population structure, and environmental changes. Existing methods typically use supervised learning to extract specific features for classification, but this may limit the generalization ability of the model and lead to generalization errors. Unsupervised feature extraction methods are an emerging approach that offers enhanced adaptability, particularly for handling unlabeled and diverse birdsong data. However, their drawback may bring additional time cost to downstream tasks, which may impact overall efficiency. To address these challenges, we propose DBS-NET, a Dual-Branch Network Model for birdsong classification. DBS-NET consists of two branches: a supervised branch (Res-iDAFF) and an unsupervised branch (based on a contrastive learning approach). We introduce an iterative dual-attention feature fusion (iDAFF) module in the backbone to enhance contextual feature extraction, and a linear residual classifier is exploited further improve classification accuracy. Additionally, to address class imbalance in the dataset, a weighted loss function is introduced to adjust the cross-entropy loss with optimized class weights. To improve training efficiency, the backbone networks of both branches share a portion of their weights, reducing the computational overhead. In the experiments on a self-built 30-class dataset and the Birdsdata dataset, the proposed method achieved accuracies of 97.54% and 97.09%, respectively, outperforming other supervised and unsupervised birdsong classification methods.  
bidsong classification; SimCLR; dual-branch network; iDAFF; weighted loss Article
# Diamonds for Life: Developments in Sensors for Biomolecules
## 4 Diamond-Based Electrodes
### Serotonin Detection  
The same BDD thin films described in the previous subsection were applied to the detection of serotonin [47]. Cyclic voltammograms showed well-defined sweeps, with peaks indicating no adsorption of the oxidation products of serotonin on the surface of the electrode. Consistently, no fouling or deactivation of the electrode was observed within the entire length of the experiment (several hours). The linear dynamic range was within the 0.01 to 100 uM interval with a remarkably low limit of detection around 10 nM.
# Ocean Wave Inversion Based on a Ku/Ka Dual-Band Airborne Interferometric Imaging Radar Altimeter
## 2 Method
### The Airborne DInIRA Observation Campaign  
In order to achieve the long-distance airborne dual-band simultaneous observation, an airborne DInIRA experiment was carried out for observing the ocean waves on the sea area of Wanpingkou, Rizhao, Shandong Province, China.  
As shown in Figure 2, a Global Navigation Satellite System (GNSS) buoy was placed at the intersection of the plane routes for validation purposes. The configuration parameters of the radar system and the plane are further listed in Table 1. To assess the inversion accuracy of the OWPs in the Ku and Ka bands at small incidence angles, the incidence angle ranges were set to 6-18\({}^{\circ}\) (Ku) and 4-17\({}^{\circ}\) (Ka), respectively. It is worth noting that the sea surface targets in this incidence range are reflected mainly as specular scattering.  
Figure 2: Map of the airborne experiment at Rizhao.  
In addition, the sampling frequency of the in situ GNSS buoy was set to 5 Hz. Finally, the echo data of the airborne dual-band simultaneous observation and the one-dimensional (1D) 5 Hz SSH in situ data were obtained. As depicted in Figure 3a, a mobile weather station was configured on the boat to obtain environmental information such as wind speed and wind direction.
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Map CFAR Detector
#### 2.1.3 Probability of a False Alarm  
The false alarm probability is given by the following equation [14]:  
\[\mathrm{Pfa}=\Pr(y)\int_{Th}^{\infty}\Pr(q/H_{0})dqdy \tag{6}\]  
The results are obtained when the SNR = 0. The following is a result [14]:  
\[\mathrm{Pfa}=\left(\frac{1}{1+\alpha\times(1-\alpha)^{n}\times Th}\right) \tag{7}\]
## 4 Experimental Results and Analysis
### Results and Analysis
#### 4.3.3 Object Segmentation  
The research focused on reconstructing semantic elements, such as tables and chairs, in the indoor scene. Before partitioning, the point cloud data for tables were filtered based on the label values because there was only one table present in this scenario. After filtering, the chair point cloud was subjected to entity segmentation using the octree algorithm. The results are illustrated in Figure 16.  
Simultaneously with the partitioning of each object, it was essential to record their XYZ coordinates for the subsequent reconstruction of BIM (building information model) elements using Revit 2016. This process involved importing the collected data into Revit software and then using the XYZ coordinate information to restore the position attributes of the objects, subsequently reconstructing the BIM elements. It was crucial to ensure that the recorded XYZ coordinates aligned with the coordinate system of the Revit model to prevent inaccuracies or an incorrect reconstruction of the BIM elements.  
Assuming that there are \(n\) points, \(p_{1},p_{2}\ldots,p_{n}\), their coordinates are \((x_{1},y_{1},z_{1})\), \((x_{2},y_{2},z_{2})\), \((x_{n},y_{n},z_{n})\). The coordinates of the centroid, C, of these points are presented in Formula (3). Their normal vector coordinates are \((Nx_{1},Ny_{1},Nz_{1})\), \((Nx_{2},Ny_{2},Nz_{2})\), \(\ldots\), \((Nx_{n},Ny_{n},Nz_{n})\). The normal vector of the entire point cloud is presented in Formula (4).  
Figure 16: Indoor chair segmentation results.  
Figure 15: The results of the automatic semantic segmentation of tables and chairs.
## 3 Results and Discussion
### Analysis of Experimental Results  
#### 3.4.1 Experimental Results  
In order to verify the contribution to the accuracy of the obstacle detection method by using DeblurGANv2 to deblur blurred images, the improved YOLOv4 algorithm was used to conduct experiments on three datasets. The experimental results are presented in Table 3, and some of the obstacle detection results are shown in Figure 9. The mAP based on dataset 1 was 98.02%, mAP based on dataset 2 was 88.28%, and mAP based on dataset 3 was 97.19%. Using DeblurGANv2 to deblur blurred images can effectively improve obstacle detection accuracy. It is easy to observe from Figure 6 that using the improved YOLOv4 algorithm to detect blurred images makes it prone to the problems of missing detection and inaccurate location, and deblurring can solve this problem.  
Figure 10 shows the detection results of the improved YOLOv4 in occlusion and small-target scenes, and Figure 11 shows the detection results of the improved YOLOv4 algorithm in low-light scenes. As can be observed from Figures 10 and 11, the algorithm has better detection results in three complex scenarios when adding the SA attention mechanism.  
Figure 9: Detection results for some images in the three datasets. (**a–c**) Clear pictures in dataset 1; (**d–f**) blurred pictures in dataset 2; and (**g–i**) deblurred images in dataset 3.  
Figure 11: Improved YOLOv4 detection results for low-light scenes. (**a–f**) Example of test result.  
Figure 10: Detection results for two scenes with occlusions and small targets. (**a–c**) Occlusion targets; (**d–f**) small targets.
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 3 Results
### SLA\({}_{l3}\) and SLA\({}_{nemo}\) Comparison with In Situ SLA\({}_{tg}\) Measurements
Comparison results (_r_ and RMSE) between \(\mathit{SLA}_{\mathit{TG}}\), \(\mathit{SLA}_{\mathit{L3}}\) and \(\mathit{SLA}_{\mathrm{NEMO}}\) are shown in the last four columns. Figure 11: Comparison of tide gauge-based sea-level measurements (\(\mathit{SLA}_{\mathit{TG}}\)) with (**a**) altimetry CMES Level 3 sea-level anomaly (\(\mathit{SLA}_{\mathit{L3}}\)) and (**b**) NEMO reanalysis model (\(\mathit{SLA}_{\mathit{NEMO}}\)), respectively. The red line is the linear regression between the two datasets.
###### Abstract  
Traditional villages face many difficulties in the era of globalization, especially in light of fast industrialization and urbanization. The breakdown of settlement patterns and the erosion of local characteristics and cultural identities pose critical issues for the sustainable development of these communities. While research on traditional villages and dwellings in core cultural areas is relatively advanced, there remains a significant gap in studies focusing on traditional villages and dwellings in multicultural intermingling regions. By clarifying the characteristics of traditional villages and the cultural landscapes of dwellings under the influence of multiple cultures, as well as their differentiation and underlying mechanisms, this research aims to provide theoretical support for the protective planning of world cultural heritage, which is increasingly characterized by clustering and regionalization. Taking the traditional villages and dwellings in the Jiangxi and Anhui junction area as a case study, we developed a cultural landscape factor system for traditional villages and dwellings across four dimensions: natural environment, spatial configuration, dwelling typology, and historical and cultural context. Using geographic information systems (GIS) zoning methods and statistical spatial analysis, we divided the area into three distinct cultural landscape zones. The findings indicate that the cultural landscapes within each zone exhibit unique regional characteristics at both the village and dwelling levels, particularly in site selection, settlement patterns, and architectural aesthetics. Differentiation across zones is shaped by natural factors, such as topography and water systems, as well as by regional culture, historical migration, the chronological sequence of regional development, commerce and trade growth, and the evolution of administrative systems, alongside broader cultural, economic, and social factors, showing consistent patterns. This study demonstrates that utilizing a scientific and objective zoning approach to accurately identify the cultural landscape characteristics and differentiation patterns across various cultural zones, while clarifying the historical evolution of villages and the transformation of dwelling forms, provides practical insights for cultural landscape zoning in other multicultural regions. Furthermore, it provides scientific guidance to advance China's rural revitalization strategy and supports the regional protection and sustainable development of world cultural heritage.  
Jiangxi-Anhui border region; multicultural intermingling area; traditional villages; cultural landscape; cultural zoning