esa-sceva/satcom-chunk-collection huggingface dataset:
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
###### Abstract  
Global Navigation Satellite System Reflectometry (GNSS-R) is a rapidly developing Earth observation technology that makes use of signals of opportunity from Global Navigation Satellite Systems that have been reflected off the Earth's surface. The Cyclone Global Navigation Satellite System (CyGNSS) is a constellation of eight small satellites launched by NASA in 2016, carrying dedicated GNSS-R payloads to measure ocean surface wind speed at low latitudes (\(\pm 35^{\circ}\) North/South). The ESA ECOLOGY project evaluated CyGNSS v3.0 products, which were recently released following various calibration updates. This paper examines the performance of the new calibration by evaluating CyGNSS v3.0 Level-1 Normalized Bistatic Radar Cross Section (NBRCS) and Leading Edge Slope (LES) data from individual CyGNSS units and different GPS transmitters under constant ocean wind conditions. Results indicate that L1 NBRCS from individual CyGNSS units are well inter-calibrated and remarkably stable over time, a significant improvement over previous versions of the products. However, prominent geographical biases reaching over 3 dB are found in NBRCS, linked to factors including the choice of GPS transmitter and the bistatic geometry. L1 LES shows similar anomalies as well as a secondary geographical pattern of biases. These findings provide a basis for further improvement of CyGNSS Level-2 wind products and have wider applicability to improving the calibration of GNSS-R sensors for the remote sensing of non-ocean Earth surfaces.  
global navigation satellite system; GNSS; GNSS reflectometry; GNSS-R; CyGNSS; ocean remote sensing; calibration; validation; wind speed +
Footnote †: journal: remote sensing  
13, 3500[[https://doi.org/10.3390/rs13173500](https://doi.org/10.3390/rs13173500)]([https://doi.org/10.3390/rs13173500](https://doi.org/10.3390/rs13173500))
# Spectrum Sharing Opportunities for 6G Terrestrial and Non-Terrestrial Networks +
## 2 Case Study: Spectrum Sharing in the 28 GHz Band
Figure 2a shows the antenna pattern of the receiving FS WLL TS that follows Recommendation ITU-R F.699-8 [15] and Figure 2b shows the antenna pattern of the receiving FS WLL CS that follows Recommendation ITU-R F.1336-5 [16]. The antenna patterns in Figure 2 have been obtained with the FUB sharing tool [17; 18]: the diagram of the FS WLL TS receiver in Figure 2a is plotted for different values of the antenna diameter which correspond to different antenna gains; the diagram of the FS WLL CS receiver in Figure 2b is plotted for different maximum gains of the FS P-MP antenna. Figure 2b shows the antenna pattern in the elevation and azimuthal planes. The propagation model used in the simulations is based on Recommendation ITU-R P.452-16 [19] at 20% of the time (i.e., the interference is characterized as the interference power that is exceeded by 20% of the time at the victim receiver input), including the effect of the clutter in the suburban environment. This propagation model is commonly used at the international level for coexistence studies (e.g., [13; 20; 21; 22]). For each CS and TS receiver, the I/N ratio is evaluated, and the obtained value is compared with the protection threshold (I/N max). This methodology is usually used at the ITU and CEPT level for the evaluation of the technical conditions of coexistence between different systems. In order to carry out the simulation analyses, this methodology has been implemented in the proprietary sharing tool of the Fondazione Ugo Bordoni (FUB) that is used in many projects, both at a national and international level, commissioned to the FUB by the Italian Administration. The validation of the implemented methodology has been performed considering the results contained in the ECC Report 304 [13] as a benchmark. Figure 2: FS WLL antenna pattern: (**a**) FS WLL TS antenna pattern (Recommendation ITU-R F.699-8); (**b**) FS WLL CS antenna pattern (Recommendation ITU-R F.1336-5).
# Impact of Carpets on Indoor Air Quality
## 1 Introduction
Previous studies found that building materials with high surface area (floors, walls, and ceilings) reflect an important role in IAQ through emission and sorption of contaminants to and from the air. Flooring materials typically cover large areas and consist of multi-layers of different materials [3], significantly impacting IAQ. Flooring materials can be divided into basically two categories: smooth or hard flooring materials (e.g., wood, linoleum) and soft or fleecy materials (e.g., carpet). Among the flooring materials, carpets can significantly impact IAQ because of their large surface area in buildings, when applied, in combination with the large surface area of the dense fibre piles. The carpet piles consist of about 10 million fibres per square meter that provide various functional compounds for emission and sink effects on air pollution [14]. Therefore, numerous researchers have studied the behaviour of carpets on IAQ, of which some considered the perception of IAQ by sensory evaluation of people using their noses, called perceived IAQ [15; 16], and others used chemical measurement techniques [17]. Human exposure to VOCs can be through inhalation, dermal contact, or ingestion [18]. Both inhalation of the indoor air and suspended particles, and dermal contact with a flooring material that emits VOCs or adsorbs the VOCs from other components, have shown a whole range of effects, from low to severe health effects [19; 20]. The settled dust on the floor may be ingested through resuspended dust from the floor surface. Moreover, the suspension of dust can affect the concentration of VOCs in an indoor environment [21], especially regarding semi-VOCs [22]. In the light of the current need for improving IAQ, it is questioned how carpets contribute to that IAQ both from the polluting (e.g., emission and resuspension) and the cleaning effect (e.g., ad/absorption) point of view. This literature review was performed to answer the following questions. What do we know about the effects of carpets on IAQ? In addition, do different carpet material components and different environmental conditions affect the behaviour of different carpets on IAQ?
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 6 Results and Discussion
### Examples of Guaranteed Cost Strategy  
To verify the guaranteed cost strategy's effectiveness, we give the following example without considering noise and time delay.  
**Example 1**.: _Both pursuer and evader adopt the guaranteed cost strategy._  
Figure 4 shows the positions changing with time during the confrontation. From Figure 4, it can be seen that the pursuer finally intercepts the evader, and the interception time is 209.56 s. The terminal miss distance is 0.9013 m, which is less than the safe distance.  
Figure 5 shows \(t_{go}\) and \(\Delta t_{go}\) (\(\Delta t_{go}~{}=~{}t_{go}^{P}-t_{go}^{E}\)) changing with time during the confrontation. As shown in Figure 5a, at the beginning of the confrontation, the pursuer's time-to-go \(t_{go}^{P}\) is 212.27 s, and the evader's time-to-go \(t_{go}^{E}\) is 207.75 s. This result means the pursuer can guarantee capture within 212.27 s, and the evader can avoid capture within 207.75 s by adopting the guaranteed cost strategies. As shown in Figure 5b, \(\Delta t_{go}\) decreases with time and turns to zero at the end of the confrontation. This result means \(\Delta t_{go}\) and \(t_{go}^{E}\) approach the real interception time as time goes on.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Player** & \(\mathbf{X}\) **(m)** & \(\mathbf{Y}\) **(m)** & \(\mathbf{Z}\) **(m)** & \(\mathbf{V_{X}}\) **(m\(\times\)s\({}^{-1}\))** & \(\mathbf{V_{Y}}\) **(m\(\times\)s\({}^{-1}\))** & \(\mathbf{V_{Z}}\) **(m\(\times\)s\({}^{-1}\))** \\ \hline Pursuer & 1500 & 1000 & 2000 & 0 & 0 \\ Evader & 0 & 0 & 0 & \(-3\) & 8 & 5 \\ \hline \end{tabular}
\end{table}
Table 2: Positions and velocities of the initial time.  
Figure 4: The position of each player changing with time.  
In the case of \(\rho_{P}>\rho_{E}>0\), when the pursuer chooses the appropriate control strategy, the pursuer will eventually capture the evader no matter how the evader reacts. However, the guaranteed cost strategy provides an opportunity to determine upper and lower bounds on the capture time. When the evader chooses the guarantee cost strategy, the capture time can be guaranteed to be greater than \(I_{go}^{E}\). When the pursuer chooses the guarantee cost strategy, the capture time can be guaranteed to be less than \(t_{go}^{P}\).  
As can be seen from the above example, the evader is intercepted by the pursuer in 209.56 s, which is less than 212.27 s (\(t_{go}^{P}\)) and greater than 207.75 s (\(t_{go}^{E}\)). The effectiveness of the guaranteed cost strategy is verified. By adopting the guaranteed cost strategy, the pursuer can guarantee capture within \(t_{go}^{P}\), and the evader can avoid capture before \(t_{go}^{E}\).
## 2 Results and Discussion
### Sugar Contents in Bee Products  
The results of the sugar content analysis in bee bread, bee pollen, beeswax, and honey are provided in Table 2. Fructose, glucose, and sucrose contents were determined, and the total sugar content and fructose/glucose (F/G) ratio were calculated, as well. Glucose and fructose were the major components in the investigated bee products. The highest total sugar content was determined in honey (54.02 g/100 g), then pollen (25.63 g/100 g), and bee bread (16.81 g/100 g), and the lowest one in beeswax (2.09 g/100 g). Glucose and fructose contents in honey were lower than these noted by [PERSON] et al. (2018) for rape honey, i.e., 37.6 and 37.3 g/100 g, respectively. Fructose, with the mean content of 35.91 g/100 g, followed by glucose, with the mean content of 35.00 g/100 g, were also detected in floral honey samples in the study by [PERSON] et al. (2019). The differences in sugar content could be related to the various floral/geographical origin of honey from the cited and our study. However, the range of glucose and fructose contents determined in our study were similar to those presented for multiforal light honey (2018). Moreover, the analyzed honey met the requirements set out in the European regulations concerning the sum of fructose and glucose. Furthermore, the honey sample did not exceed the permissible levels for sucrose (2018; 2018). Only a low content of sucrose was detected at 0.10 g/100 g for honey, at 0.11 for beebread, and at 0.01 for bee pollen. No sucrose was reported in beeswax in our study. This is in agreement with the data reported by [PERSON] et al. (2018), who determined the contents of sucrose in bee pollen only in the range of 0.05-0.28 g/100 g.  
The fructose/glucose (F/G) ratio is a standard parameter launched in many research dedicated to bee products. Therefore, the F/G ratio was also examined in our study and ranged from 2.26 (in beebread) to 0.03 (in beeswax) (Table 2). Its highest value was noted in beebread, followed by bee pollen (F/G = 1.22). This data is similar to those reported by [PERSON] et al. (2018) and [PERSON] et al. (2018), who calculated the F/G ratio in the range of 1.01-2.24 for bee pollen from Brazil, and in the range of 1.12-2.53 for bee pollen from Turkey, respectively. The F/G of honey analyzed in our study reached 1.05 and was similar to that noted by [PERSON] et al. (2018) and [PERSON] et al. (2019). Moreover, [PERSON] et al. (2019) emphasized that the F/G ratio could be a good indicator of honey origin. They reported that the mean F/G = 1.03 could be a marker of floral honey, which is correct in the context of our multiforous honey. The lowest F/G value was determined for beeswax, since its major sugar is glucose, which is less sweet than fructose. Therefore, the content of sugars is important not only from the viewpoint of product authenticity, but also regarding consumer acceptance, as consumers definitely prefer more sweet products (in both taste and aroma).
# 3L-YOLO: A Lightweight Low-Light Object Detection Algorithm
## 2 Methods
### 3L-YOLO Model
#### 2.2.2 Neck Module Based on Multi-Scale Features and Channel Attention Mechanism  
The neck network of YOLOv8 combines the path aggregation network (PAN) [37] and feature pyramid network (FPN) [36], which enhances the model's ability to extract fine features through rich hierarchical information flow. Specifically, FPN transmits deep feature information to the shallow layers and injects key semantic information in a top-down manner. Conversely, the PAN structure uses a bottom-up approach to transfer shallow position information to the deeper layers, providing location cues for the deep layer features. However, in low-light images, the object signal features are weak, lacking rich color and texture details. The neck module of YOLOv8 does not sufficiently fuse shallow features, limiting the detection accuracy for low-light objects, particularly small objects, as shallow features retain more important position information. To address this limitation, enhancing the fusion of shallow features in YOLOv8 is essential.  
To improve the utilization of shallow features in the PAN-FPN structure, we propose a multi-scale feature fusion neck module inspired by BiFPN [38], as shown in Figure 4. This module extends the FPN + PAN architecture by adding two horizontal connection paths to integrate the original shallow features extracted from the backbone network. Specifically, compared to YOLOv8, we fuse P2 scale features into the P3 scale features in the FPN, preserving rich low-light object location information with minimal convolution operations, and merge P3 and P4 scale features into the P4 and P5 scale features in PANet, respectively.  
Figure 3: Improved C2f module by switchable atrous convolution. Except for the first and last, all convolutions are replaced by switchable atrous convolutions.  
To further refine the feature fusion process, the ECA module is applied after each feature concatenation. As illustrated in Figure 5, the ECA module first performs global average pooling on the feature map and then applies a one-dimensional convolution to enable local cross-channel interactions without dimensionality reduction. The size of the one-dimensional convolution kernel is determined adaptively. Afterward, the sigmoid function normalizes the input into a \(1\times 1\times C\) vector, where each element represents the weight for the corresponding channel. Subsequently, the weight of each channel is multiplied by the features of the corresponding channel in the input feature map, thereby performing channel-wise weighting. Given the channel dimension \(C\), the adaptive convolution kernel size \(k\) is calculated using the following formula:  
\[\mathrm{k}=\left|\frac{\log_{2}(C)}{r}+\frac{b}{r}\right|_{old} \tag{2}\]  
where \(b\) and \(r\) represent the control coefficient, and \(C\) is the number of channels. \(\left|t\right|_{old}\) indicates the nearest odd number \(t\). In this paper, we set \(r\) and \(b\) to 2 and 1, respectively, in all experiments.
# Educational 5G Edge Computing: Framework and Experimental Study
## 3 5G Smart Campus System Architecture
### Smart Education Dedicated Network Architecture
#### 3.2.3 Fixed-Mobile Integrated Education Metropolitan Area Network  
The fixed-mobile integrated education metropolitan area network takes the education cloud as the anchor point to achieve full coverage of the campus 5G wireless network and gigabit optical fiber network. By organically integrating these two networks, the communication network and information applications are highly correlated and integratedto achieve the construction goal of cloud-network-terminal integration. The fixed-mobile integrated education metropolitan area network brings several benefits, such as ensuring the convenience and reliability of smart educational applications on campus, as well as enabling the centralized control of network performance and application content quality through the central cloud.
# Ultra-Low-Power Wide Range Backscatter Communication Using Cellular Generated Carrier +
## 4 System Design and Simulation Configuration
### Simulation Tool, Models and Parameters
#### 4.3.2 Building Penetration Loss (BPL) Model for Indoor BD  
In our simulations, the source of an ambient signal is located in an outdoor environment. Therefore, for indoor BDs we need to consider the building penetration loss (BPL). The 3 GPP has defined the penetration loss for different types of materials as a function of the frequency for two types of buildings i.e., for old and new building type, such that old and new buildings have lower and higher BPL, respectively [51]. Here we have assumed that old buildings are composed of 15% plain glass windows and 85% concrete wall, whereas the modern buildings are covered with 70% infrared reflective (IRR) glass windows and 30% concrete wall. Accordingly, we are using a modified form of those BPL models presented by 3 GPP, see Equations (18) and (19). In Equation (18), \(BPL_{Low,dB}\) is the BPL for the old building type and is expressed in \(dB\), \(L_{Platinglass,dB}\), and \(L_{Concrete,dB}\) are the single material penetration losses for plain glass and concrete in \(dB\), respectively. Similarly, in Equation (19), \(BPL_{High,dB}\) is the BPL for the modern building type and \(L_{IRRglass,dB}\) is the penetration loss for IRR glass in \(dB\). The penetration loss for double plain glass, IRR glass  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{HPBW_{H}}\) & \(\mathbf{HPBW_{V}}\) & \(FBR_{H}\) & \(SLLV\) & \(A_{Max}\) \\ \([^{\circ}]\) & \([^{\circ}]\) & \([dB]\) & \([dB]\) & \([dB]\) \\ \hline
65 & 7 & 30 & 18 & 17.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extended 3 GPP antenna model parameters.  
and concrete slab are given in Equations (20)-(22), respectively, where \(f\) is the frequency that is expressed in \(GHz\)[51].  
\[BPL_{Low,dB}=-10\log_{10}[0.15\times 10^{\frac{-1_{Plagingless,dB}}{10}}+0.85 \times 10^{\frac{-1_{Constrated,dB}}{10}}] \tag{18}\]  
\[BPL_{High,dB}=-10\log_{10}[0.7\times 10^{\frac{-1_{LRRglass,dB}}{10}}+0.3 \times 10^{\frac{-1_{Constrate,dB}}{10}}] \tag{19}\]  
\[L_{Plagingless,dB}=2+0.2f \tag{20}\]  
\[L_{IRRglass,dB}=23+0.3f \tag{21}\]  
\[L_{Concrete,dB}=5+4f \tag{22}\]
## 2 Proposed Speech Enhancement Methods for LDVs
### Problems with Sound Measurement Using LDVs  
The sound wave can be restored by measuring the vibration generated by sound excitation. An LDV is a device that measures the moving velocity of an object by calculating the phase difference between the reference light and reflected light that irradiates the object surface [3]. Since a laser beam has high straightness, it is possible to measure vibration occurring far away and acquire only the sound near the irradiated object not affected by the noise surrounding the LDV. Therefore, the speech acquired from an LDV includes the frequency characteristics of the vibrating object. Therefore, the sound quality of observed speech greatly depends on the shape and vibration characteristics of the object. For example, objects with rough surfaces will cause tiny changes in the intensity and direction of reflected light. This also causes noise to mix into the observed speech. The vibration characteristics can also result in a lack of speech components in a small amplitude and phase delay in the frequency spectrum, as shown in Figure 1. Figure 1 also shows the spectrogram of the observed speech acquired from different irradiated objects, which are common everyday objects. From Figure 1, it can be seen that there are varying degrees of noise and a lack of speech components (see Figure 1c 2-5 kHz), and certain frequency bands are enhanced (see Figure 1c 5-8 kHz). We selected the observed speech in Figure 1b PET bottle as the processing object that has the greatest lack of speech components at the frequency band of 3-8 kHz with a noise of about 30 dB.  
\begin{table}
\begin{tabular}{c l l l} \hline \hline
**Variable** & **Signification** & **Variable** & **Signification** \\ \hline \(x\) & Clean speech & \(\phi_{x}\) & Phase spectrum of clean speech \\ \hline \(y\) & Observed speech & \(\phi_{y}\) & Phase spectrum of observed speech \\ \hline \(x^{\text{NB}}\) & Narrow-band clean speech & \(\phi^{\text{PD}}\) & Phase difference between \(\phi_{x}\) and \(\phi_{y}\) \\ \hline \(y^{\text{NB}}\) & Narrow-band observed speech & \(K\) & Fourier transform length \\ \hline \(x^{\text{WB}}\) & Wide-band clean speech & \(k\) & Frequency index \\ \hline \(y^{\text{WB}}\) & Wide-band observed speech & \(m\) & Frame index \\ \hline \(|\,X^{\text{LPS}}|\) & Log-power spectrum of clean speech & \(n\) & Sampling index \\ \hline \(|\,Y^{\text{LPS}}|\) & Log-power spectrum of observed speech & value & Estimated value \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of notations.  
Figure 1: _Cont._
# Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks
## 5 Results and Discussions
### Comparison of Predicted Versus Target \(R_{A}\)  
In Figure 4, we visualize the scatter line plot for actual versus predicted \(R_{A}\) values with the \(x\)-axis denoting the number of observations (for randomly selected 100 instances) and the \(y\)-axis representing the \(R_{A}\). Figure 5 shows the regression plot between predicted and target (actual) retro-reflectivity values using the prediction results from the best model. A regression trend line indicating the prediction values is shown by a solid line in the figure. The values of the \(R^{2}\) for this best fit regression line is given in Table 4. It can be seen that our prediction is correlated with the base which explains the high \(R^{2}\) score of the adopted models. It may be noted from the figure that the predictive performance of a plotted one-hot encoding model is good at low actual
## 6 Conclusions  
This paper proposes a PD signal denoising method which is based on the AO-VMD and K-SVD algorithms. The optimization algorithm is employed to fine-tune the parameters of the VMD, facilitating an initial noise reduction stage that accentuates the intrinsic characteristics of the original PD signal. Subsequently, the K-SVD algorithm is applied to eliminate residual noise, yielding a purified or significantly noise-reduced PD signal. The efficacy of the proposed algorithm is demonstrated through simulations and actual PD signals, where it consistently excels in noise reduction. Comparative analyses with other denoising strategies--SVD-VMD, AEEMD, and Wavelet Multilevel Soft Thresholding--reinforce the superior noise reduction capabilities of the proposed approach, confirming its potential for improved PD signal analysis.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14072755/s1](https://www.mdpi.com/article/10.3390/app14072755/s1)]([https://www.mdpi.com/article/10.3390/app14072755/s1](https://www.mdpi.com/article/10.3390/app14072755/s1)), Supplementary Materials Data.  
J.Z.: Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing--review and editing; Z.L.: Formal analysis; Investigation; Methodology, Software, Validation, writing--original draft, Writing--review and editing; X.B.: Resources, Formal analysis, Methodology, Visualization. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data is contained within the article and Supplementary Materials.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: