esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances in Remote Sensing and Propulsion Systems for Earth Observation Nanosatellites
## 5 Discussion
### Main Aspects  
Nanosatellites can be deployed in constellations to provide frequent revisits to specific areas on Earth. This capability is crucial for monitoring vegetation health [14; 15; 16; 17; 18; 19; 20], tracking maritime activities [33], and assessing urban development [40]. Despite their small size,nanosatellites can carry various sensors and payloads, including optical and infrared cameras, spectrometers, and other specialized instruments for monitoring specific Earth features such as land cover, atmospheric conditions, or vegetation.  
Operating at higher altitudes to mitigate orbital decay introduces additional challenges for nanosatellites. Higher altitudes require remote sensing instruments like radars and cameras to operate with increased sensitivity and resolution to maintain data quality. However, these advancements are often constrained by the nanosatellite design's payload and power limitations. Balancing these trade-offs remains a critical focus area for future nanosatellite missions.  
Moreover, nanosatellites serve as platforms for testing and demonstrating new technologies in space, including miniaturized sensors, communication systems, and propulsion technologies that can be applied to larger and more complex satellite missions in the future. They can also support precision agriculture and environmental monitoring by providing high-frequency, high-resolution imagery. This enables farmers to monitor crop health, optimize irrigation, and detect pest infestations, leading to more efficient and sustainable agricultural practices. Additionally, nanosatellites can contribute to environmental monitoring by observing changes in land use, deforestation, and pollution, which can aid in studying climate patterns, monitoring biodiversity, and assessing the impact of natural disasters on ecosystems.  
Nanosatellites play a crucial role in disaster response as they can provide rapid and updated information on natural disasters such as wildfires, floods, and earthquakes. This information is essential for assessing damage, planning rescue operations, and coordinating relief efforts. Nanosatellites can also be part of collaborative missions involving multiple satellites working together to achieve a common goal. This collaborative approach enhances data collection capabilities and provides a more comprehensive understanding of Earth's processes.
## 3 Results
### The Contribution of Multiple Variables to the Carbon Flux Trends of the TRSR  
In quantifying the contribution of meteorological factors to the trend of carbon fluxes in the TRSR, we found, through multiple linear regression analysis, that the three variables (temperature, precipitation and solar radiation) could explain \(69.40\%\) and \(52.20\%\) of the variation in GPP and NEP, respectively, and we estimated the contribution of each variable to the trend of carbon fluxes through the regression coefficient of each variable (Figure 12).  
Figure 12: The contributions of temperature (**a**,**d**), precipitation (**b**,**e**) and solar radiation (**c**,**f**) to the GPP and NEP trends in the TRSR.  
Temperature was the predominant contributor to the increasing trend of GPP in the TRSR, with a contribution of 0.2326 g C/m\({}^{2}\)/yr. The regions with the greatest contribution were located in the eastern TRSR. It was noteworthy that temperature contributed negatively to GPP in 82.60% of the regions. Precipitation also positively contributed to the rising trend of GPP with a contribution of 0.2127 g C/m\({}^{2}\)/yr. Furthermore, 79.83% of the area had a positive contribution, and the north-eastern part of the TRSR was a high-contribution area; solar radiation had a minimal negative contribution to the trend of GPP (-0.050 g C/m\({}^{2}\)/yr), and positive and negative contributions accounted for 32.58% and 67.41%, respectively. In the sub-park area, precipitation continued to be a meteorological factor with a high contribution to the GPP trend, with contributions of 0.186, 0.084, and 0.056 g C/m\({}^{2}\)/yr in the HRSP, LRSP, and YRSP, respectively. Temperature was the largest contributor to the GPP trend at LRSP (0.1134 g C/m\({}^{2}\)/yr), and solar radiation contributed negatively to the GPP trend at HRSP and YRSP, whereas its contribution was positive in the LRSP.  
Temperature and precipitation contribute synergistically to NEP growth trends. At TRSR, LRSP, and YRSP, temperature remained the largest contributor to the NEP trend, with 0.034, 0.035, and 0.002 g C/m\({}^{2}\)/yr, respectively. The contributions of precipitation to the NEP trend were 0.122, 0.046, and 0.061 g C/m\({}^{2}\)/yr, respectively. Solar radiation had an offsetting effect on the increasing NEP trend in the TRSR and LRSP, with contributions of \(-0.002\) and \(-0.003\) g /m\({}^{2}\)/yr, respectively. The difference was that precipitation was the largest contributor to the NEP trend in the HRSP, with 0.136 g C/m\({}^{2}\)/yr, followed by temperature and solar radiation, with contributions of 0.005 and \(-0.004\) g C/m\({}^{2}\)/yr, respectively. In terms of spatial distribution, temperature negatively contributed to NEP in 87.30% of the study area. The proportion of regions with a negative contribution of precipitation to NEP was 24.45%, and the proportion of regions with a positive contribution was 75.55%. Solar radiation positively and negatively contributed to NEP in 41.60% and 58.40% of the study area, respectively.
# Adaptive Semi-Supervised Algorithm for Intrusion Detection and Unknown Attack Identification
## 3 Methodology
### Algorithm Design
#### 3.2.2 Overall Model Architecture  
The overall system architecture is shown in Figure 6, where the generator (G), discriminator (D), and noise encoder (E) work together in a closed-loop GAN framework.  
The original input data are first processed through an encoder for feature extraction, which transforms the feature matrix into a latent space vector, denoted as \(z\). This latent vector is then fed into the generator, which uses it to produce a synthetic feature matrix. The goal of the generation process is to make the synthetic feature matrix as similar as possible to the real feature matrix.  
The algorithm architecture includes two discriminators: one that assesses the authenticity of the generated synthetic feature matrix and another that evaluates the differences between the generated and the original feature matrices in the feature space. This dual discriminator mechanism enables the algorithm to learn not only the overall visual similarity of the synthetic feature matrix but also to more accurately align it at the feature level.  
By jointly optimizing both pixel-level and feature-level losses and incorporating the dual discriminator mechanism, the system ensures that the generator produces high-quality features that are aligned with the real feature matrix, both visually and in terms of feature representation. The final evaluation of generation performance is carried out through the combined scores of \(\text{Loss}_{\text{Img}}\) and \(\text{Loss}_{\text{Feat}}\), guiding the continuous optimization process. The following outlines the complete implementation logic for the testing phase of the TransGAN-IDS model (Algorithm 1):  
Figure 6: Overall architecture of TransGAN-IDS.
# Measurement of Head-Related Transfer Functions: A Review
## 7 Discussion
### Considerations and Trends in HRTF Measurements
Individual HRTF measurements in ordinary home environments are of great interest because not all listeners have the possibility to measure HRTFs in anechoic chambers or in acoustically controlled rooms. Several commercially available applications provide the possibility to rapidly synthesize personal HRTFs using photogrammetric computational methods, e.g., Genelec Aural ID ([[https://www.genelec.com/aural-id](https://www.genelec.com/aural-id)]([https://www.genelec.com/aural-id](https://www.genelec.com/aural-id))), Sony 360 Reality Audio ([[https://www.sony.com/electronics/360-reality-audio](https://www.sony.com/electronics/360-reality-audio)]([https://www.sony.com/electronics/360-reality-audio](https://www.sony.com/electronics/360-reality-audio))), Super X-Fi ([[https://sg.sxfi.com/sxfitech](https://sg.sxfi.com/sxfitech)]([https://sg.sxfi.com/sxfitech](https://sg.sxfi.com/sxfitech))), etc. A recent study has proposed a method to accurately capture head-torso shapes with a smartphone camera, which allows for further calculating personal HRTF [210]. Due to the simple acquisition procedure, these applications will be becoming more popular in the future. However, the acquisition of highly accurate personal HRTFs is still a challenge with these methods. Regarding the measurement approach, one commercially available device, namely Smyth realizer ([[https://smyth-research.com](https://smyth-research.com)]([https://smyth-research.com](https://smyth-research.com))), offers the possibility to measure personal binaural room impulse responses (BRIRs) based on their multi-channel setups in home environments. Different from BRIRs, HRIRs should not contain reflections and they are usually measured at anechoic chambers. Several studies show possibilities to suppress reflections and background noises from measured HRTFs in ordinary rooms and even in complex acoustic environments [154; 155; 156]. Furthermore, some recently proposed mobile HRTF measurement systems can quickly measure 2D/3D individual HRTFs with a single fixed loudspeaker, and such game-like measurement procedures may be preferred by users [120; 166; 168; 169]. Those studies can serve as a good starting point for the rapid measurement of individual 3D HRTFs in ordinary home environments.
## 1 Introduction
### Our Approach and Contributions  
The mathematical approach to analyzing cellular systems is often considered as a promising solution [18]. However, for cellular networks, analytical tractability has been proven by relying on the methodology of stochastic geometry while modeling the BSs using point processes. Stochastic geometry has been widely applied in the field of communication theory, and it has proven that it is a powerful approach in conducting performance analysis and solving optimization problems. Examples of the usage of stochastic geometry in modeling cellular networks can be found in [19; 20; 21; 22; 23; 24]. In this study, we model the locations of the base stations in the network as identically and independently distributed following a homogeneous Poisson point process (PPP). In this paper, we fitted the channel gain and the antenna gain into statistical distributions based on channel simulations from NYUSIM and the 3 GPP antenna array model. We determined the closed-form expression of the MGF for the exposure in the network, which is equivalent to the total power received at a certain MT location, with which we compute its cumulative distribution function (CDF). We analyzed the behavior of the 90 th percentile of the exposure for different network parameters. We showed the comparison between this model and a simplified, less accurate, model with simple antenna and channel models in [25]. We performed a sensitivity analysis on the exposure expression to show the impact of each parameter on the global exposure. We also proved that ignoring the network usage when analyzing exposure will lead to large inaccuracies. The list of the symbols used in the paper is presented in Table 1 and the list of all abbreviation used can be found in in Appendix B at the end of the article.
# Multi-Module Combination for Underwater Image Enhancement
## 3 Proposed Method
### Comparative Enhancement Module  
Underwater image quality is frequently compromised by complex optical environments, which are characterized by non-uniform light distribution, reduced detail contrast, and blurring effects caused by the scattering of suspended particles. The contrast enhancement module improves color saturation and contrast, thereby highlighting the textural details of the image. This enhancement facilitates subsequent analysis and processing.  
The AGCWD [24] algorithm dynamically adjusts image luminance through gamma correction while incorporating a weighted probability distribution of pixel intensities, thereby effectively improving the visibility of underexposed regions while maintaining  
Figure 4: A flowchart of the rank-one prior method.  
preserving natural image appearance. Assume that \(X\) represents \(I_{c}\) or \(\hat{Z}(x)\) and that its luminance l varies in the range of \(l_{min}\) and \(l_{max}\). The dynamic range of the image \(X(i,j)\) luminance gray scale \(I\) is \(l_{min}\) and \(l_{max}\), where \(l_{min}\) is the smallest gray scale order. The probability density function of the gray scale pixel distribution of a statistical image:  
\[pdf(l)=\frac{nl}{MN} \tag{18}\]  
where \(MN\) is the total number of pixels and \(nl\) is the number of pixels corresponding to the gray scale \(n\).  
The optimal tuning of the probability density function (\(pdf\)) is achieved by utilizing \(cdf\) and the normalized gamma transform curve while preserving the statistical properties of the original histogram. Specifically, the probability density function is initially calculated based on the statistical histogram from the previous step. Subsequently, the weighted distribution (WD) function is introduced to correct the histogram, ultimately resulting in the optimized weighted probability density function:  
\[pdf_{w}(l)=max(pdf)\times\left(\frac{pdf(l)-min(pdf)}{max(pdf)-min(pdf)} \right)^{a} \tag{19}\]  
A lower gamma parameter produces a more significant adjustment and can take the value [0.1, 0.5], which can be taken as 0.5 as a rule of thumb to give a smoother function curve. We calculate the cumulative distribution function (\(cdf\)) using A to carry out the normalization. Using the weighted probability density function calculated from Equation (19), the smoothed cumulative distribution function is derived:  
\[cdf_{s}(l)=\sum\limits_{l=l_{min}}^{l_{max}}\frac{pdf_{w}(f)}{\sum pdf_{w}} \tag{20}\]  
\[T(l)=(l_{max}-l_{min})*\left(\frac{l}{l_{max}-l_{min}}\right)^{\gamma} \tag{21}\]  
\[\gamma=1-cdf_{s}(l)*P \tag{22}\]  
\(T(l)\) is the transform function. Adaptive gamma correction uses the image cumulative distribution function information \(cdf\) of Equation (20), where \(P\) denotes the adaptive parameter, set to 1, with a range of values [0.5, 1].  
\[L_{2}(x)=T(X(i,j)) \tag{23}\]  
Mapping the luminance values of each pixel point of the image gives the contrast-enhanced image \(L_{2}(x)\).
## 3 Interactions of Antitoxins and Toxins with Cellular Components
### Interactions Involving Antitoxins
#### 3.2.2 Direct Interactions with Non-TA Promoters  
The _E. coli_ MqsA and HipB antioxins have the capacity to regulate the expression of specific genes in addition to their own operon. The [PERSON] group recently showed that MqsA, as well as MqsA-MqsR complexes negatively regulate the expression of _rpoS_, the general stress response sigma factor [25; 118]. They also show that under oxidative stress, MqsA is degraded by the ATP-dependent Lon protease, leading to derepression of _rpoS_ by MqsA [118]. Amongst the genes regulated by sigmaS is the master regulator of mobility FlhDC. CsgD, the master regulator of biofilm formation, is also regulated by MqsA [119]. Furthermore, MqsA also regulates expression of other genes involved in biofilms, the folding of periplasmic proteins and the inhibition of replication (_mcbR_, _spy_, _cspD_) [25; 120]. Hence, MqsA seems to be a key transcriptional regulator of stress response and biofilm formation. A further layer of regulation comes from the type II DinJ antitoxin, which also regulates RpoS. In contrast to MqsA, DinJ acts indirectly by repressing _cspE,_ a positive regulator of RpoS [121].  
A bioinformatics search for the palindrome bound by the HipB antitoxin identified a sequence upstream of 33 genes of diverse functions, _i.e._, persistence, metabolism, transcriptional regulation and mismatch repair. Experimental validation was obtained for the _relA_, _eutH_ and _fadH_ promoter regions [117]. RelA is involved in (p)ppGpp synthesis and required for the high persistence phenotype of the _hip47_ mutant [122]. _eutH_ and _fadH_ encode an ethanolamine transporter and a 2,4-dienoyl-CoA reductase, respectively.
## 4 Experiment Verification
### Discussion  
Although different initial embedded learning models have different effects on different KG inference tasks, the proposed method can improve the processing ability of different embedded learning models, and the degree of improvement is related to the representation learning model and task. At the same time, since the experimental data set does not contain complex relations and there are not many types of relations and entities, it can only show that the proposed method can improve the task handling capacity of the representation learning model, but cannot show which embedded learning model is optimal.
# Analysis and Improvement of a Two-Stage Centrifugal Compressor Used in an MW-Level Gas Turbine
## 2 Analysis of the Performance of Low/High-Pressure Two-Stage Centrifugal Compressor  
The structure of an MW-level gas turbine is shown in Figure 3. The low/high-pressure two-stage centrifugal compressor is composed of an inlet guide vane, an impeller, and a diffuser. A vaned diffuser is used to connect the two stages. The geometric structure of the centrifugal compressor is shown in Figure 4.  
Figure 3: Structure of an MW-level gas turbine.  
Figure 2: Comparison of centrifugal compressor pressure ratios.  
The low-pressure-stage impeller's rotation speed is 28,000 rpm, and the rim speed is 478 m/s. Figure 5 shows the CFD calculation results for the performance curve of the low-pressure-stage centrifugal compressor. The pressure ratio of the low-pressure-stage centrifugal compressor is 3.92, and its efficiency is 79.9% in the design condition [18].  
The high-pressure-stage impeller's rotation speed is 38,000 rpm, and the rim speed is 501 m/s. Figure 6 shows the CFD calculation results for the performance curve of the high-pressure-stage centrifugal compressor. The pressure ratio of the high-pressure-stage centrifugal compressor is 2.85, and its efficiency is 80.5% in the design condition [18].  
Figure 4: Geometric structure of a low/high-pressure two-stage centrifugal compressor: (**a**) Low-pressure stage (**b**) High-pressure stage.  
Figure 5: Performance curve of the low-pressure-stage centrifugal compressor.  
The performance analysis shows that the efficiency of the low/high-pressure two-stage centrifugal compressor is lower than that of a unit with a similar pressure ratio, as shown in Figure 1. This observation indicates that several limitations exist in the design of the low/high-pressure two-stage centrifugal compressor, and that the design needs to be optimized.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 1 Introduction
In this study, both the current and the past outputs of the SINS are taken as the inputs of the predictor to improve the accuracy of prediction. The rest of the paper is organized into the following sections: Section 2 describes the underwater integrated navigation system of SINS/DVL/MCP/PS; Section 3 presents the PLSR-SVR hybrid predictor and its implementation in the integrated navigation system; Section 4 provides simulations and field tests along with specific analysis; Section 5 is committed to concluding remarks.
# Analysis and Design of Rateless Two-way Relay Networks Based on a Multiply-and-Forward Scheme
## 3 Analysis on Uncoded TWRN
### AF Scheme
: _In the high SNR regime, when source nodes transmit uncoded BPSK symbols in a three-stage AF-TWRN over the flat Rayleigh fading channel, the mean SER of AF-TWRN is derived as_  
\[\rho_{\mathrm{AF}}^{2}\mathrm{E}\Big{[}\|\mathbf{y_{AR}}+\mathbf{y_{BR}}\|_{2 }^{2}\Big{]}=1 \tag{12}\]1  
**Proof:** Let us approximate the received SNR \(\gamma_{\mathrm{A}}^{\mathrm{AF}}\) at node A in the high SNR regime as  
\[ \begin{array}{ll}\gamma_{\mathrm{A}}^{\mathrm{AF}}&\approx\frac{h_{\mathrm{ A,R}}^{2}h_{\mathrm{B,R}}^{2}P^{2}}{\left(3h_{\mathrm{A,R}}^{2}P+h_{\mathrm{B,R}}^{2} \right)\sigma_{\mathrm{R}}^{2}}\\ &=\frac{1}{3}\cdot\frac{3h_{\mathrm{A,R}}^{2}\gamma^{2}+h_{\mathrm{B,R}}^{2} \gamma}{3h_{\mathrm{A,R}}^{2}\gamma+h_{\mathrm{B,R}}^{2}\gamma}.\end{array}  \tag{23}\]  
It is clear that \(\gamma_{\mathrm{A}}^{\mathrm{AF}}\) in (23) is the harmonic mean of \(3h_{\mathrm{A,R}}^{2}\gamma\) and \(h_{\mathrm{B,R}}^{2}\gamma\). Since \(3h_{\mathrm{A,R}}^{2}\gamma\) and \(h_{\mathrm{B,R}}^{2}\gamma\) follow exponential distributions with parameters \(d_{\mathrm{A,R}}^{2\eta}/\left(6\gamma\sigma_{\mathrm{R}}^{2}\right)\) and \(d_{\mathrm{B,R}}^{2\eta}/\left(2\gamma\sigma_{\mathrm{R}}^{2}\right)\) respectively, the MGF of \(\gamma_{\mathrm{A}}^{\mathrm{AF}}\) can be well approximated by  
\[M_{\gamma_{\mathrm{A}}^{\mathrm{AF}}}(\tau)\approx\frac{3}{\tau}\Bigg{(}\frac {d_{\mathrm{A,R}}^{2\eta}}{6\gamma\sigma_{\mathrm{R}}^{2}}+\frac{d_{\mathrm{B,R}}^{2\eta}}{2\gamma\sigma_{\mathrm{R}}^{2}}\Bigg{)}=\frac{d_{\mathrm{A,R}}^ {2\eta}+3d_{\mathrm{B,R}}^{2\eta}}{2\gamma\sigma_{\mathrm{R}}^{2}\tau} \tag{24}\]  
according to Lemma 1. Substituting (24) into (19), we can yield the mean SER at node A as  
\[P_{\mathrm{SER,A}}^{\mathrm{AF}}=\frac{1}{\pi}\int_{0}^{\pi/2}\frac{d_{ \mathrm{A,R}}^{2\eta}+3d_{\mathrm{B,R}}^{2\eta}}{\gamma\sigma_{\mathrm{R}}^{2} }\sin^{2}\theta d\theta=\frac{d_{\mathrm{A,R}}^{2\eta}+3d_{\mathrm{B,R}}^{2 \eta}}{4\gamma\sigma_{\mathrm{R}}^{2}} \tag{25}\]  
Similarly, the mean SER at node B is given by  
\[P_{\mathrm{SER,B}}^{\mathrm{AF}}=\frac{3d_{\mathrm{A,R}}^{2\eta}+d_{\mathrm{ B,R}}^{2\eta}}{4\gamma\sigma_{\mathrm{R}}^{2}} \tag{26}\]  
According to the results in (25) and (26), the mean SER of a BPSK-modulated AF-TWRN is given by  
\[P_{\mathrm{SER,AF}}=\frac{1}{2}\Big{(}P_{\mathrm{SER,A}}^{\mathrm{AF}}+P_{ \mathrm{SER,B}}^{\mathrm{AF}}\Big{)}=\frac{d_{\mathrm{A,R}}^{2\eta}+d_{\mathrm{ B,R}}^{2\eta}}{2\gamma\sigma_{\mathrm{R}}^{2}} \tag{27}\]  
\(\sqcap\)\(\sqcup\)
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results
### Dataset Generation and Pre-Processing
#### 4.2.3 Analysis of Variance Distribution  
As shown in the left side of Figure 6, the explained variance distribution across principal components follows a characteristic pattern for image data. The first component captures approximately 22%  
Figure 7 visually demonstrates the effect of our PCA transformation on sample digits. The top row shows the original MNIST digits '0' and '1', while the middle row displays their PCA reconstructions using eight principal components. Despite the significant dimensionality reduction, the reconstructed images retain the essential characteristics needed for classification. The bottom row visualizes the first eight principal components (PC 1-8) as spatial patterns. PC 1 clearly captures the fundamental circular structure distinguishing '0' from '1', while subsequent components encode increasingly subtle variations. These visual patterns confirm that our PCA implementation successfully extracts the most discriminative features for quantum encoding.