esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Bulk-Rock Geochemical Investigation of Fe-Ti-V Oxide and Sulphide Ores  
The subject of the research was to determine the concentration of elements in the magnetite-ilmenite ores, along with the locally co-occurring polymetallic sulphides from the Krzemianka and Udryn deposits located in the SAM. Twenty-three ore-bearing samples were collected from the Krzemianka deposit and 16 samples from the Udryn deposit. The rocks hosting magnetite-ilmenite mineralization with sulphides were mainly rich in Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) anorthosites, norites, and jotunites, and to a lesser extent pyroxenties and gabbors. In the studied bulk-rock samples, the iron contents ranged from 6.4 to 76.2% Fe\({}_{2}\)O\({}_{3}\) (_n_ = 39; Tables 6 and 7). Most of the Fe-Ti rich ores showed massive and semi-massive textures and they were classified as \"ferrolites\" (Figure 6). The \"ferrolites\" showed concentrations in the range of 50-70% (_n_ = 19) and 30-40% Fe\({}_{2}\)O\({}_{3}\)t (_n_ = 10), with samples containing 40-50% Fe\({}_{2}\)O\({}_{3}\)t constituting approximately 10% of the population (_n_ = 5). Only a few samples had a poorer Fe-Ti mineralization and showed impregnation-veinlet textures. The arithmetic mean of the total iron concentration Fe\({}_{2}\)O\({}_{3}\)t was higher, at 48.6% (_n_ = 39; Table 6). Fe\({}_{2}\)O\({}_{3}\)t showed a very strong correlation with Zn (_r_ = 0.94; Table 8) and a strong correlation with TiO\({}_{2}\) (_r_ = 0.89; Figure 16C), Ga (_r_ = 0.86), and Ni (_r_ = 0.72) and a weak correlation with Co (_r_ = 0.69) and Cd (_r_ = 0.64) and no correlation, e.g., with V (_r_ = 0.42; Figure 16B). The arithmetic mean content of TiO\({}_{2}\) was 7.6% (_n_ = 39). TiO\({}_{2}\) showed a strong correlation with Fe\({}_{2}\)O\({}_{3}\)t (_r_ = 0.89), Zn (_r_ = 0.88), and Ga (_r_ = 0.75) and a weaker correlation with Co, Ni, Cd, and U (_r_ = 0.6), and no correlation with V (Figure 16D). Vanadium concentrations ranged from 104 to 2208 ppm and in 40% of the population the vanadium concentration was above 1000 ppm (0.1%). The arithmetic mean for vanadium = 960.2 ppm (_n_ = 39). Vanadium showed a good correlation with cobalt (_r_ = 0.70, Figure 16D) and no correlation with Fe\({}_{2}\)O\({}_{3}\)t (_r_ = 0.42; Figure 16E), Ga, or TiO\({}_{2}\). Figure 15: Bivariate diagrams of vanadium vs. iron (**A**) and vanadium vs. titanium (**B**) in ilmenite depending on crystal form in the Krzemianka and Udryn Fe-Ti-v deposits. All plots represent the same set of samples; 0 values and samples with values below the level of precision of a single measurement are not presented on the axis.
## 3 Results and Discussion
### Correlation Analysis  
Microbial metabolic activities play a pivotal role in the formation of metabolites in cheese. Consequently, we selected the top six most abundant bacterial genera in cheese and conducted [PERSON]'s correlation analysis of the metabolites (\(p<0.05\), \(\mid\)R\(\mid\)\(\geq 0.6\)) (Figure 8). The results revealed that _Streptococcus_ and _Lactococcus_ exhibited positive correlations with organic acids and negative correlations with lactose and other carbohydrates. This is attributed to the abundant lactate dehydrogenase present in these bacteria, which enables them to decompose lactose and other carbohydrates, leading to the production of organic acids [36]. In addition, the correlation patterns between _Streptococcus_ and _Lactococcus_ with various metabolites are similar, indicating that they have similar metabolic pathways, which indirectly supports their synergistic relationship [37]. In contrast, _Brochtbrix_ and _Enhydrobacter_ displayed positive correlations with amino acids and negative correlations with lactose and other carbohydrates, indicating their potential capacity to degrade macromolecules, such as proteins and lactose and other carbohydrates in cheese. Conversely, _Carnobacterium_ showed positive correlations with lactose and other carbohydrates and negative correlations with amino acids. This opposite correlation pattern compared to Brochothrix and Enhydrobacter may explain the divergent trends observed when the salt content is reduced. Additionally, _Pseudomonas_ was positively correlated with biogenic amines and negatively correlated with amino acids, indicating that _Pseudomonas_ facilitate the decarboxylation of amino acids, leading to the production of biogenic amines. This conclusion is in line with the report by [PERSON] et al. [38], which states that _Pseudomonas_ fluorescens can convert lysine into cadaverine. In summary, the unique metabolites formed and altered in cheese are the cumulative result of the intricate interplay between various microorganisms.  
Figure 8: Correlation heatmap for the major microbial genera and key differential metabolites in three groups of cheddar cheese. The horizontal axis represents microbial genera, while the vertical axis represents metabolites. The intensity of the correlation is indicated by the color and saturation, with red representing a positive correlation and blue representing a negative correlation.
## 4 Experiments
### Ablation Study  
To further investigate the contribution of different components within Hybrid MARL, we conduct an ablation study by systematically removing key modules from the framework. The following variations are tested:  
1. Hybrid MARL (Full Model): The complete proposed model, integrating spatio-temporal feature extraction, hierarchical reinforcement learning, and global coordination.
2. Without Spatio-Temporal Attention (No-ST): This variant removes Graph Attention Networks (GATs) and LSTMs, preventing agents from learning dynamic traffic dependencies.
3. Without Global Coordination (No-GC): This model eliminates the global agent, forcing sub-region agents to operate independently.
4. Without Hierarchical Learning (No-HL): A single-layer reinforcement learning approach without hierarchical control.  
Figure 7 presents the results of the ablation study. Removing spatio-temporal attention results in a significant degradation in performance, confirming its importance in modeling dynamic traffic conditions. Similarly, without global coordination, waiting times increase sharply, indicating that hierarchical structure plays a crucial role in optimizing network-wide traffic flow.  
Figure 7 highlights the impact of different model components. The full Hybrid MARL model achieves the best performance.
## 3 Results
### Characteristics of Obtained L-Asparaginase Conjugates
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RrA Preparation** & \multicolumn{2}{c}{\(\mathbf{t_{R}}\), **Min**} & \multirow{2}{*}{**Control Samples**} & \multirow{2}{*}{**t\({}_{\mathbf{R}}\), **Min**} \\  & **Tetramer** & & & \\ \hline
**RrA** & 18.3 & 24.3 & **BSA** & 17.5 \\
**RrA-PEI-PEG** & 17.6 & 23.9 & **PEI-PEG** & 24.7; 26.7 \\
**RrA-Spm** & 17.3 & - & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Retention times for the main fractions of native RrA solutions and their conjugates. Figure 2: Chromatograms obtained by gel filtration: (**a**) solutions of RrA and conjugate, RrA-PEI-PEG; (**b**) native RrA and conjugate, RrA-Spm. Protein concentration of the samples is 1 mg/mL; C(PEI-PEG) = 5 mg/mL. To characterize the obtained conjugates and find correlations between the surface charge and enzyme activity, we measured the particle surface charge and specific activity (Table 4). The polyamine modification increases the enzyme charge from \(-9.0\) to \(+1.4\) mV  
Figure 4: CD spectra of the native enzyme and RrA-PEI-PEG, RrA-PEI, and RrA-Spm conjugates. C(RrA) = 1 mg/mL, 0,01 M PBS, 37 \({}^{\circ}\)C, pH 7.5. Figure 3: IR spectra of the native enzyme compared with conjugates: (**a**) RrA-PEI-PEG; (**b**) RrA-Spm; (c) RrA-PEI. The concentration of RrA was 1 mg/mL, PEI-PEG was 1 mg/mL, Spm was 0.5 mg/mL, and PEI was 0.1 mg/mL. Conditions: PBS 10 mM, 22 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Secondary Structures** & **RrA** & **RrA-PEI** & **RrA-PEI-PEG** & **RrA-Spm** \\ \hline
**Helix** & 35.4 \(\pm\) 1.1 & 31.0 \(\pm\) 1.0 & 31.4 \(\pm\) 0.9 & 32.1 \(\pm\) 1.0 \\
**Antiparallel** & 7.6 \(\pm\) 0.7 & 9.0 \(\pm\) 0.7 & 8.8 \(\pm\) 0.6 & 8.6 \(\pm\) 0.7 \\
**Parallel** & 8.7 \(\pm\) 0.3 & 9.3 \(\pm\) 0.3 & 9.3 \(\pm\) 0.2 & 9.1 \(\pm\) 0.2 \\
**Beta-turn** & 17.1 \(\pm\) 0.3 & 17.3 \(\pm\) 0.4 & 17.2 \(\pm\) 0.3 & 17.1 \(\pm\) 0.3 \\
**Random coil** & 31.5 \(\pm\) 1.1 & 34.0 \(\pm\) 1.0 & 33.9 \(\pm\) 1.2 & 33.3 \(\pm\) 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Percentage content of native enzyme secondary structure elements and RrA-PEI-PEG, RrA-PEI, and RrA-Spm conjugates, calculated in the CDNN program. (for RrA-PEI-PEG) compared with the native protein. All of the conjugates had a higher specific activity than the native enzyme. The conjugate with the PEI-PEG copolymer showed the greatest increase in activity. The conjugate with a linear PEI showed an insignificant increase in activity by about 5%. The RrA-Spm conjugates showed an activity increase of 23%. Polyamines, therefore, have a positive effect on the specific activity of L-asparaginase and a minimal effect on the secondary structure of the enzyme. Next, to determine how conjugation affects the properties of the enzyme, several parameters of the conjugates were studied in comparison with the native enzyme. These parameters were the pH optimum of the enzyme, thermostability, trypsinolysis resistance, and cytotoxic activity.
## 3 Satellite Signal Measurements in Various Weather Conditions  
The measurements conducted and analyzed in this chapter are to answer the question how the changing weather conditions (in conjunction with Earth's atmosphere) affect the accuracy of positioning in satellite systems, and what potential impact they may have on an autonomous machine movements based on GNSS technology usage. In theory, the ionosphere, dependent mainly on solar activity, has the greatest influence on the accuracy of position determination. For example, a solar storm can cause difficulties in the power supply or the operation of all electronics. Such extreme weather conditions were not encountered during the measurements. However, the pressure, temperature, or humidity varied, as well as solar activity, which was taken into account.
# Structural Damage Features Extracted by Convolutional Neural Networks from Mode Shapes
## 4 Discussions and Conclusions
By observing the features extracted by the CNN, a simple method of damage location can be derived, that is to calculate the difference between the sample to be classified and the sample of intact scenario to get the mode shape difference function and then map it via a power function to magnify the fluctuation feature, so as to produce a distinguishable peak in the damaged segment and finally locate the damaged segment. The formula is as follows:  
\[f(x)=\phi^{d}(x)-\phi(x) \tag{3}\]  
\[f(x)=\phi^{d}(x)-\phi(x) \tag{4}\]  
\[F(x)=\left[f(x)\right]^{n} \tag{5}\]  
\(f(x)=\) mode shape difference function  
\(\phi=\) mode shape of the intact structure  
\(\phi^{d}=\) mode shape of the damaged structure  
\(x=\) spatial location  
\(n=\) power factor  
From the above discussion, it can be concluded that:  
1. the CNN method based on the mode shape has a good prediction accuracy of damaged rods, but there are always some rods with low prediction accuracy. Due to the different sensitivity of modal data to the damage of different types of rods, their prediction accuracies are also different;
2. the CNN method based on the mode curvature difference has similar good prediction accuracy to the mode shape when locating the rod with high damage degree. However, the prediction accuracy is low when locating the rods with small damage degree. Like the mode shape, there are always some rods with low prediction accuracy due to the different sensitivity of modal data to the damage of different rods;
3. the origin data has more damage information for the CNN. Though the first order mode shapes look indistinguishable, the multi-layer CNN can extract the damage information from the data, which are indistinguishable for naked eyes;
4. using the first order mode shape as the input of the CNN to locate the damaged rod, the partial work of the CNN is to update its internal weights to get the difference between the input sample and the weighted average of the training samples in shallow layers, then the data will produce a distinguishable peak feature in the damaged segment after processed by multiple convolutional layers behind. This CNN process is similar to calculating the difference between the sample to be classified and the weighted average of the training samples and then mapping it via a quadratic function. **Author Contributions:** Conceptualization, K.Z., S.T., and G.C. ; methodology, K.Z., S.T. and G.C. ; software, S.T. and F.C. ; validation, S.T. and F.C. ; formal analysis, K.Z., G.L. and G.C. ; investigation, K.Z., G.C. and G.L. ; data curation, K.Z. and G.L. ; writing--original draft preparation, K.Z., G.C. and F.C. ; writing--review and editing, G.L., G.C. and F.C. ; visualization, S.T. and F.C. ; supervision, G.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflict of interest.
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 4 Discussion
Our attribution analysis showed that precipitation, NDVI, temperature, relative humidity, and solar radiation could explain 82% of the variance in canopy interception in China over the last two decades (Table 1). Among these factors, precipitation, NDVI, and temperature were considered as generally predominant in China (Figures 8 and 9), while more specifically dominant factors varied depending on the region. In Northwestern China, precipitation was considered to affect the variation of canopy interception most, with a relative contribution of over 40% in most of the areas or even reaching to 80-100% in some regions (Figures 8a and 9). This is consistent with the results of previous studies reporting that the arid and semiarid regions in Northwestern China have been experiencing an upward trend in precipitation since the 1990s [60; 61]. Such increases in precipitation might mean that more rainwater could be intercepted by the vegetation canopy as, in these dry regions, canopies do not easily saturate due to the high potential evapotranspiration [62; 63; 64]. Temperature was considered as the main dominant factor affecting interception in the subtropical or tropical forest regions of China (Figures 8c and 9). It has been previously reported that temperature in Southern China increased significantly since 1998; specifically, the average daily mean temperature increased by 0.7 \({}^{\circ}\)C in 2009-2018 compared to 1961-1970 [65]. The elevated temperature would accelerate the canopy's evaporation rate, thus improving the interception storage of the vegetation canopy [12; 24]. Our results identified the NDVI as another main factor affecting the variation of interception in China (Table 1), especially in the Loess Plateau (Figures 8b and 9). This index has been widely used as a proxy to indicate variations in vegetation coverage [66]. It has been shown that the establishment of a series of ecological conservation projects in the past decades has contributed to widely increasing vegetation coverage in many regions of China [41; 42; 67], especially in the Loess Plateau where the Grain for Green Project has been implemented since the 1990s [35; 68]. For instance, [PERSON] et al. [69] reported that during the 1999-2013 period, the vegetation coverage obviously increased from 31.6% to 59.6%. Such increases might facilitate the growth of vegetation canopy and enhance canopy interception of rainwater [70]. Although the NDVI could be used as a good indicator of regional vegetation changes, many other canopy structure variables still influence the rainfall interception process, including leaf shape, canopy thickness, canopy surface area, and canopy forms [11; 52; 58; 71]. Ignoring these variables would limit the accuracy of the model results for regional canopy interception, therefore they should be considered when modeling regional canopy interception in future studies.
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
### Contributions and Structure of the Paper  
Based on the above research, to ensure the quality of service of users and satisfy the transmission power constraint, we propose a novel resource allocation scheme to maximize the energy efficiency of the NOMA downlink system. First, to ensure users' fairness and improve the data rate of users with poor channel quality, we propose a low complexity greedy algorithm to allocate subchannels. Secondly, given the problem that the existing particle swarm optimization algorithm is prone to fall into local optimization and the later search accuracy is not high, we modify the algorithm. Finally, we use the modified particle swarm optimization algorithm to solve the power allocation problem between and within subchannels.  
The subsequent arrangement of the article is as follows. Section 2 introduces the NOMA downlink system model and proposes the optimization problems we need to solve. In Section 3, we first introduce our proposed low complexity subchannel allocation algorithm. Then we explain the improvement measures of the particle swarm optimization algorithm. Finally, we present how to use the improved particle swarm optimization algorithm for power allocation. In Section 4, we analyze the simulation results of the proposed scheme. Finally, Section 5 is the conclusion of this paper.
## 3 Materials and Methods
### Electrical Resistivity Tomography Surveys  
ERT data was acquired with a Syscal Pro resistivity meter (IRIS instruments, Orleans, France). The system features an internal switching board for 72 electrodes and an internal 250 W power source. The Wenner-Schulmberger array was chosen because it is properly sensitive to both horizontal and vertical structures and has a relatively good signal strength [51]. The configuration has high performance and stability in high electrical resistivity environments such as dry gravels and it is useful for the investigation of horizontal or slightly inclined layers that can present lateral facies changes and/or verticalized structures, as is the case of the studied site [52; 53].  
The design of geophysical surveys and the selection of the multi-electrode arrays was planned considering the length available for the acquisition, depth of investigation, the resolution required, and the expected structure derived from hydrogeological background knowledge. Lithological logs of boreholes drilled for DEMOWARE Project (borehole S3 to S7) and from a new building project (borehole S8) have been an invaluable support for our research (Figure 4).  
The distribution of the 17 ERT acquired profiles was affected by the site physical barriers (buildings, fences, roads... ) and the availability of space to extend the arrays along an almost straight line. Acquiring ERT using straight lines increases the efficiency of the survey as it is not necessary adapting on-site the array to a different geometry setting. The objective was to cover the study area with a representative network of the variability of electrical resistivity values with profiles distributed as homogeneously as possible in the area. As a result, six cross-sections were acquired perpendicular, seven transversal,and two obliques to the direction of the main water streams. Additionally, two detailed cross-sections (P11 and P12) were gathered near piezometers S3 to S7 and cross-section P1. Their purpose was to increase the resolution in the area with more geological data and correlate resistivity values and lithologic changes (Table 1).  
The 235 m long 2D ERT cross-sections allowed us to reach a research-depth close to 50 m--allowing to characterize the expected maximum aquifer thickness of 30 m--and a resolution of one point every five meters in both directions. The 94 m length 2D ERT detailed cross-sections allowed to reach a research-depth close to 20 m--the aquifer boundary was assumed at 10-15 m depth--and a resolution of two meters apart between geoelectrical values.
# Impact of Satellite Attitude on Altimetry Calibration with Microwave Transponders
###### Abstract
Since our method is completely based on readily available data, it can be easily implemented by calibration teams without the need for any special pre-processing performed prior to the distribution of altimetric products from the processing Agencies. The proposed calibration methodology is comprehensive and can be applied to every past, current and future altimetry mission with available attitude measurements. We use our approach to evaluate and study attitude effects on range and datation biases for the calibration of Jason-3. The selection of Jason-3 is motivated by the long time series of transponder calibrations made at the CDN1 Cal/Val, spanning from 2016 to 2022. Jason-3 was also selected because it has been the reference altimetry mission since 2016, up until Sentinel-6A Michael Freilich (Sentinel-6A MF) succeeded in this role in 2022. Furthermore, its \(\sim\)10-day repeat track orbit is more frequent compared to other satellites, such as Haiyang-2 (\(\sim\)14 days) or Sentinel-3 (\(\sim\)27 days). Based on this fact, Jason-3 offers a time series of range and datation biases with finer temporal resolution, which increases the reliability of spectral analysis. Finally, the necessary datasets for calibration processing (SGDR version F, attitude information, etc.) are publicly available and cover its entire lifespan. This paper is structured as follows. Section 2 describes the ESA PFAC infrastructure, its instrumentation, Cal/Val site distribution and current Cal/Val activities. Section 3 discusses the determination of attitude angles, the fundamental methodology for the calculation of range and datation biases using a transponder and the application of the revised processing accounting for attitude effects. The most important findings are presented and discussed in Section 4. Finally, Section 5 provides the main conclusions of this investigation.
## 2 Methodology
### Video Summarization Architectures  
#### 2.4.1 LSTM-Based Architecture  
Recurrent neural networks (RNNs) [39] are a type of neural network used with sequential or time series data. They differ from standard neural networks, which assume that inputs and outputs are independent, in that they remember information from earlier inputs and use it to impact the current input and output. A major drawback of RNN networks is that they are susceptible to the vanishing gradient problem [40]. The gradient of the loss function approaches zero as the network's number of layers with activation functions increases, making the network more challenging to train. Due to the vanishing gradient problem, RNNs are not able to remember long-term dependencies. A long short-term memory network (LSTM) is an advanced RNN network that allows information to persist [6]. It is capable of handling the vanishing gradient problem with a chain structure that contains memory blocks called cells.  
These cells can forget information that is no longer useful before passing it to the next cell. The output of one cell is taken as input of another. This chain structure is what allows the LSTM to retain only the useful information without suffering from the vanishing gradient problem. The LSTM network can remember the information between different frames of the video while only retaining the important information.  
The LSTM architecture used in this work was a four-layer LSTM network with 50 nodes in each layer. The proposed LSTM architecture is shown in Figure 6 (left).