esa-sceva/satcom-chunk-collection huggingface dataset:
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 5 OpenFOAM FVTM Comparison
### Comparison  
The numerical temperature values are taken at the same positions of the thermocouples (Figure 22) and the comparison with test data is reported in Figure 23.  
Since the HERMES program is a CubeSat mission, it is not forced to follow the ECSS space standards entirely, indeed a tailored version of such standards is used in CubeSat scenarios [4], nonetheless the team philosophy aims at following the ECSS standards as far as it is possible, thus the test data is compared to the FVTM results with the goal to reach the correlation standards of ECSS-E-ST-31C 4.5.3.3.c [3], for which the model is correlated if  
Figure 23: Numerical and test data comparison. Horizontal light blue line represents \(\pm\)5 °C interval imposed by ECSS for correlation.  
Figure 22: Thermocouple positions showed in the FVTM.  
the predicted temperature falls within \(\pm 5\,^{\circ}\mathrm{C}\) from the test value. Indeed, from Figure 23 it is possible to observe that there is a very good correlation with the test results for all thermocouples placed on structural parts. This proves that FVTM is able to correctly predict the actual temperatures.  
For the TCs on the internal components, the correlation is still good as reported in Figure 23 but some thermocouples are just at the boundary whilst others are a few degrees away from correlation, in particular tc1 and tc6 are out of the correlation criteria. It must be noted that the uppermost part of the payload, marked with tc8, and all the other internal thermocouples on the boards and structure are within correlation, thus the reason for this discrepancy cannot be caused by those regions. The cause of the divergence with high probability lies in the thermal connections inside the crystal box and in the sandwich assembly connecting it with the FEE and the components above the FEE (Figure 2). As reported in [20], the elements in that region of the payload are connected with a series of pads for the crystals and glues for the SDDs. All that complexity in the connections leads to a high grade of uncertainty in the actual thermal contact coefficient (see next section) between the elements causing with high probability the divergence. To better investigate the phenomena, a dedicated test shall be performed with a dedicated number of sensors in that region. Unfortunately, it is not possible to place any thermocouple in that region, for example upon the FEE close to SDDs or inside the crystal box. As pictured and reported in [20] there is no space where to place thermal sensors (plus the routing) upon the FEE or inside the crystal box, indeed all the thermocouples in the flight model for the payload unit are placed on the lateral panels. Given the impossibility to place sensors in the region and obtain the dedicated data on the thermal connections needed to update the FVTM, the team has decided to accept the divergence in the comparison with model results. Nonetheless, further analyses on this point are undergoing and will be presented in the next research outcomes.
## 3 Materials and Methods
### Laser Raman  
Raman spectroscopy is based on the Raman scattering effect. By analysing the parameters of the G and D peaks of a sample, information about molecular vibrations and rotations can be obtained. Raman spectroscopy experiments on shale samples were performed using a Renishaw inVia Raman microscope. The argon ion laser was excited with a 532 nm laser, which was focused on the surface of the microcomponent with a 50\(\times\) magnifying objective, producing a spot size of 2 \(\upmu\)m. The laser power was set to 1 mW and the grating was set to 1800 lines/mm. Spectra were obtained in the range of 500-2500 cm\({}^{-1}\). Prior to testing, wave number calibration was performed using a silicon wafer, and Raman spectra were collected for each sample. Curve-fitting and baseline-corrected reflectance and G- and D-band parameters were determined by testing carbonaceous material, coal, shale, oil matrices, specular plasmas, solid asphalt, and animal detritus, including spectral band position, spectral band separation, spectral band intensity ratios, and half-peak widths (FWHM).
# ROAD Statistics-Based Noise Detection for DME Mitigation in LDACS
## 4 Nonlinear Estimators  
As discussed in the system model, the nonlinear device is designed to detect and eliminate DME interference from the LDACS AS receiver. In this paper, the proposed nonlinear device ROAD PB uses ROAD statistics for the detection of DME interference and pulse blanking for the mitigation of interference. The performance of the method is compared with the conventional pulse blanking, which uses the amplitude of the received signal for the detection of DME interference. In addition, nonlinear estimators such as GAE enhanced pulse peak processors and OBE enhanced pulse peak processors are also used for the mitigation of DME interference to compare the performance of ROAD PB.  
The functioning of the nonlinear devices ROAD PB, pulse blanking, GAEPPA, GAEPPL, OBEPPA and OBEPPL in the detection and elimination of DME noise are discussed below.  
Figure 4: Proposed LDACS receiver block diagram.
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 2 Methods and Principles
### CCA and CSEA Modules
#### 2.2.2 CSEA Module  
The architecture of the CSEA module is shown in Figure 6, which mainly consists of two 2D convolutions and the squeeze-and-excitation attention (SE).  
Figure 7 illustrates the structure of the SE module. This module has the capability to dynamically allocate weights across various channels, a process autonomously learned by the network with minimal human oversight. The SE module is adept at explicitly mapping the interrelations among convolutional feature channels, thereby enhancing the network's representational power. It harnesses global information to judiciously highlight salient features while diminishing the less relevant ones. Incorporating the SE module significantly aids in the noise reduction process of seismic data.
## 5 Conclusions  
The CAROLS L-Band radiometric instrument has been built and tested in the context of the SMOS mission. It will be an essential tool for SMOS calibration operations and for the improvement of direct and inverse modeling. The CAROLS radiometer has been validated and qualified with laboratory measurements, showing in particular excellent stability and resolution. The measurements demonstrated that this radiometer has a sensitivity of 0.1 K for a 1 s integration time, and a relative stability of 0.1 K over a period of 30 min. Three campaigns were then carried out. The first four flights made in September 2007 allowed CAROLS, installed in conjunction with other airborne instruments (STORM radar, GOLD-RTR GPS receiver, an infra-red radiometer and a visible wide angle camera), to be certified for use in the French research ATR-42 aircraft. The airborne measurements verified the good sensitivity of the CAROLS data, and its good agreement with the emissivity model during wing wags and circular movements of the aircraft. However, these initial flights allowed an imperfect isolation to be identified between the X and Y channels, and a high level of detected RFI to be identified over land surfaces. The aim of the CAROLS'2008 campaign was to validate various modifications, allowing the imperfections observed in 2007 to be corrected. Complete isolation of the two channels has been validated. Concerning the presence of RFI, our ground measurements identified the need to use a more selective frequency filter, in order to avoid high emission sources very close to our protected bandwidth (1,400-1,427 MHz). The new filter used during the 2008 flights permitted this RFI to be strongly attenuated in the data recorded over South West France. Finally, following validation of the CAROLS data recorded in 2007 and 2008, the initial objective of the 2009 CAROLS campaign was to acquire scientific data for the inversion algorithm, used to provide soil moisture and ocean salinity estimations. The retrieved first and second Stokes parameters are found to be in good  
Figure 15: Time variations of Tb during the SMOSMANIA transect, in 2009, for the nadir antenna (upper figure, red and black lines respectively for the X and Y polarizations), and slant antenna (lower figure, green and blue lines respectively for the X and Y polarizations). A mixed Kurtosis and thresholds to the Tb standard deviation algorithm [25] was applied for the data used here.  
agreement with the simulated values. All comparisons over the ocean show an absolute accuracy \(<\)1 K. The third and fourth Stokes parameters indicate a good degree of coherence with the simulated values, for low and medium incidence angles.  
These measurement campaigns have been successful, and the detailed science results will be published in the near future. The next step will be the analysis of a major experimental campaign held in the spring of 2010, with the aim of validating the SMOS measurements.
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 3 Results and Discussion
### Green Synthesis of ZnO-NPs and Its Characterization
The phytocompounds present in extract of _P. zeylanica_ are responsible for reduction of zinc and stabilization or capping of the particles. A similar report has been documented previously, where the size of ZnO-NPs ranged from 10 to 90 nm [29]. \begin{table}
\begin{tabular}{c c c c c c} \hline
**S. No.** & **20 (degree)** & **Height (a.u.)** & **Area** & **FWHM** & **Size (nm)** \\ \hline
1 & 31.090 & 2283.5 & 35,996.0 & 0.3219 & 25.61 \\
2 & 33.719 & 1854.0 & 24,809.3 & 0.2789 & 29.76 \\
3 & 35.566 & 3608.0 & 60,054.7 & 0.3421 & 24.38 \\
4 & 46.880 & 833.0 & 14,037.3 & 0.3468 & 24.96 \\
5 & 55.963 & 1288.7 & 23,612.0 & 0.3690 & 24.37 \\
6 & 62.197 & 931.4 & 19,461.3 & 0.4272 & 21.71 \\
7 & 67.340 & 852.0 & 18,560.0 & 0.4420 & 21.59 \\ \hline \multicolumn{6}{c}{Average} & & & & \(24.62\pm 2.74\) \\ \end{tabular}
\end{table} 
Table 1: Details of the most intense peaks of ZnO-NPs synthesized using _P. zeylanica_ extract obtained from X-ray diffraction. Figure 1: (**A**) UV–visible absorption spectrum of ZnO-NPs synthesized using _P. zeylanica_ extract. (**B**) X-ray diffraction pattern of ZnO-NPs. The chemical characterization of ZnO-NPs was performed using FTIR spectroscopy. The FTIR spectrum of ZnO-NPs is shown in Figure 2C. A sharp absorption band at 550 to 520 cm\({}^{-1}\) confirms the presence of ZnO. The band ranging from 600 to 400 cm\({}^{-1}\) is attributed to the M-O stretching (Zn-O) [30]. The absorption of zinc oxide nanoparticles at 457 cm\({}^{-1}\) is reported in literature [24]. A very sharp and prominent peak at 1640 cm\({}^{-1}\) is due to the amide I, showing that protein or enzyme of the _P. zeylanica_ extract also contributes in the stabilization and capping of ZnO-NPs [31]. A broad peak at 3300 cm\({}^{-1}\) is due the O-H stretching. The band at 2130 to 2110 cm\({}^{-1}\) is related to stretching vibrations of C\(\equiv\)C of the alkynes [25]. The peaks near 1310 cm\({}^{-1}\) are due to the aromatic amines. The glycosidic linkage of C-O-C was also reflected by peaks at 1230 cm\({}^{-1}\)[32]. Overall, FTIR spectroscopy provided enough evidence regarding the role of phytoconstituents of _P. zeylanica_ in the capping and stabilization of ZnO-NPs.
# Flickering-Free Distance-Independent Modulation Scheme for OCC
## 4 Modulation/Demodulation Scheme
### Switching Frequencies' Selection  
The frequency multiplier, \(\alpha\), directly provides two frequencies for the system: coupled frequency (\(f_{1}=\alpha f_{\text{CAM}}\)) and uncoupled frequency (\(f_{2}=(\alpha+0.5)f_{\text{CAM}}\)). The next step is to determine the other two frequencies needed for the two-bits symbols generation.  
Based on the quasi linear relation between the frequencies in the range from \(\alpha f_{\text{CAM}}\) to \((\alpha+0.5)f_{\text{CAM}}\) and the consecutive frames' ROI correlation coefficient, a fixed step between the frequencies is assumed. This relation is depicted in Equation (10) and can be observed in Figure 10.  
\[\text{step} =\frac{(\alpha+0.5)f_{\text{CAM}}-\alpha f_{\text{CAM}}}{3} \tag{10}\] \[=\frac{\alpha f_{\text{CAM}}+0.5f_{\text{CAM}}-\alpha f_{\text{ CAM}}}{3}\] \[=\frac{f_{\text{CAM}}}{6}\]  
The remaining frequencies are calculated using Equation (10) as follows:  
\[\begin{split} f_{3}&=f_{1}+\text{step}\\ &=\alpha f_{\text{CAM}}+\frac{f_{\text{CAM}}}{6}\\ &=\left(\alpha+\frac{1}{6}\right)f_{\text{CAM}}\end{split} \tag{11}\]  
Figure 10: Relation between the PCC values and the switching frequencies. All the experiments were performed with a camera’s frame rate of 30 fps. The straight red line represents \(\alpha=30\), and band’s size = 36 pixels. The dashed blue line represents \(\alpha=60\), and band’s size = 18 pixels. The dotted black line represents \(\alpha=120\), and band’s size = 9 pixels.  
\[f_{4} =f_{1}+2\text{step} \tag{12}\] \[=\alpha f_{\text{CAM}}+\frac{2f_{\text{CAM}}}{6}\] \[=\left(\alpha+\frac{1}{3}\right)f_{\text{CAM}}\]  
Therefore, the switching signal frequency can be determined using Equation (13)  
\[f_{\text{SW}}=\begin{cases}\alpha f_{\text{CAM}}&\text{DATA}=10\\ (\alpha+1/6)f_{\text{CAM}}&\text{DATA}=11\\ (\alpha+1/3)f_{\text{CAM}}&\text{DATA}=01\\ (\alpha+1/2)f_{\text{CAM}}&\text{DATA}=00\end{cases} \tag{13}\]
###### Abstract  
Water transport pipelines in the mining industry face significant corrosion challenges due to extreme environmental conditions, such as arid climates, temperature fluctuations, and abrasive soils. This study evaluates the effectiveness of three advanced inspection technologies--Guided Wave Ultrasonic Testing (GWUT), Metal Magnetic Memory (MMM), and In-Line Inspection (ILI)--in maintaining pipeline integrity under such conditions. A structured methodology combining diagnostic assessment, technology research, and comparative evaluation was applied, using key performance indicators like detection capability, operational impact, and feasibility. The results show that GWUT effectively identifies surface anomalies and wall thinning over long pipeline sections but faces depth and diameter limitations. MMM excels at detecting early-stage stress and corrosion in inaccessible locations, benefiting from minimal preparation and strong market availability. ILI provides comprehensive internal and external assessments but requires piggable pipelines and operational adjustments, limiting its use in certain systems. A case study of critical aqueducts of mining site water supply illustrates real-world technology selection challenges. The findings underscore the importance of an integrated inspection approach, leveraging the complementary strengths of these technologies to ensure reliable pipeline integrity management. Future research should focus on quantitative performance metrics and cost-effectiveness analyses to optimize inspection strategies for mining infrastructure.  
m +
Footnote †: journal: Applied Sciences  
Article
## 2 Materials and Methods
### C-ICT Framework
#### 2.3.3 C-ICT Step 3: IVA  
In the third stage, the second-level mixing matrices and SCVs, each of which comprises \(K\) second-level components that correspond to \(K\) modalities, are estimated by performing IVA-G on the transposed reduced first-level mixing matrices, as shown in Figure 1c. Let \(\mathbf{B}^{[k]}=(\mathbf{A}_{k}^{[k]})^{T}\in\mathbb{R}^{L_{k}\times M}\) be \(\mathbf{X}^{[k]}\) to be decomposed in the IVA generative model (4). The order of the subspace \(D\) is chosen to be \(min(L_{k})\). Let \(\mathbf{F}^{[1]},\mathbf{F}^{[2]},\ldots,\mathbf{F}^{[K]}\), \(\in\mathbb{R}^{L_{k}\times D}\) denote the estimated second-level mixing matrices and \(\mathbf{U}^{[1]},\mathbf{U}^{[2]},\ldots,\mathbf{U}^{[K]}\) denote the estimated independent source matrices. The estimated sources are formed into SCVs \(\mathbf{U}_{1},\mathbf{U}_{2},\ldots,\mathbf{U}_{D}\), and each \(\in\mathbb{R}^{K\times M}\).  
Figure 2: Cumulative explained variance accounting for different PCs from the FA, GM, and fALFF datasets. For each dataset, the black point on the curve indicates the variance explained by the number of PCs we selected.  
Among the estimated SCVs, we calculate all pairwise Pearson correlation coefficients \(\rho\) within each SCV. We then calculate the corresponding \(p\)-values; i.e., the probability (\(p_{\rho}\)) of obtaining a correlation as large as the observed value when the true correlation is zero. The correlation value \(\rho\) is considered significant if the \(p\) value is less than 0.05. If the \(K\) components within the \(j\)th SCV have significant pair-wise correlation coefficients (i.e., the \(p\)-values are all less than 0.05), then this \(j\)th SCV is referred to as a \"significantly correlated SCV\". All significantly correlated SCVs are used in the next steps. The number of these SCVs is denoted by \(C\).
# Offensive-Language Detection on Multi-Semantic Fusion Based on Data Augmentation
## 4 Experimentation
### Ablation Experiment  
We analyze the impact of different parts in this section. The results are shown in Figure 2, where \"Not Aug\" denotes ablation of augmentation. Similarly, \"Not DSM\" means that a deep semantic module was not used, and \"Not CCM\" means to cancel the character-capture module; \"Not Inter\" denotes that there is no interaction fusion mechanism.  
Based on the results in Figure 2, we observed that:  
(1) Data augmentation has a significant impact on model performance. When data augmentation was used, the performance was comparable to SKS and better than other baselines. When data augmentation was canceled, the performance of the model decreased significantly by about 5%. The reason for this is that data augmentation increases the diversity of data, and the model can learn richer semantic features without the need to introduce external resources.  
(2) DSM has little effect on model performance. When the module was canceled, the model performance only decreased by about 2%. The reason for this is that our proposed model uses two modules to extract the semantic information of sentences. The semantic information extracted by the CCM module can better represent offensive language, and the adaptive fusion layer can better integrate the features extracted by the CCM module.  
(3) CCM has a great impact on model performance. To our surprise, when CCM was canceled, the performance for OLID decreased by nearly 20%, and it also decreased by 15% for the DV dataset. The main reasons for this were as follows. First, the semantic features of the character level have a significant impact on the offensive-language detection task, which can effectively improve performance. Second, the features extracted by the deep network are not suitable to be used as the input for the interactive-fusion level alone. In our experiments, this structure decreased model performance significantly.  
(4) The interactive-fusion level can fuse the semantic features extracted from the two modules of DSM and CCM, which is a useful supplement to the model.  
(5) Our proposed model uses data augmentation, DSM, CCM, and an interactive fusion mechanism to achieve state-of-the-art performance.  
Figure 2: The results of ablation experiment.
## 4 Metal Ion Leaching and Solute Transport
### Landfill Capping Layers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model or Platform/Type** & **Pressure Drivers/Application** & **Porous Medium** & **Data Collection/Input** & **Reference** \\ \hline ADAPT, (extension of CLEAMS with & Agricultural subsurface drainage for nutrient & & [137,176,216] \\ DRIANMOD hydrological component) & transport/and macroscopic flow transfer. & & [137,176,216] \\ \hline AGN/s/non-point source model, & Non-point pollution simulation resulting from (dumped conceptual type) & Watersheds & [22,102] \\ \hline \multirow{4}{*}{ANFIS} & Groundwater quality for irrigation using & & \\  & prediction of irrigation water quality index & & \\  & (W0D), soluble sodium percentage (PSP), & & \\  & sodium adsorption ratio (SAR), potential & & \\  & salinity (PS), Kelley index (HQ) and residual & & \\  & sodium carbonate index (RSC). & & \\ \hline \multirow{4}{*}{ANIMO/mechanistic model} & Nutrient leaching prediction/surface, & & \\  & groundwater quality prediction, & & \\  & agri-environmental indicators testing, nitrogen & & \\  & transformation and leaching. & & \\ \hline ANN combined with SES-Bilst STM and SES-ANFIS models, (LMBP) and (MLP) algorithms, ANN combined with & Water table depletion, saltwater intrusion & & \\  & wedge, water quality prediction in different & & \\  & wedge, water quality prediction & & \\  & groundwater, groundwater level prediction. & & \\ \hline Ann/AGNPS & Phosphorus and nitrogen transport. & Watersheds & & [220,221] \\ \hline ANSWERES/lumped conceptual type & Watershed and soil nutrient planning. & & [102] \\ \hline ANSWERES 2000 (incl. Green and Ampt infiltration model) & Catchment scale, surface runoff and sediment transport model, sediment loss. & Water, soil & Operational time step flexible, e.g., during runoff. & [222,223] \\ \hline \multirow{4}{*}{APEX/single porosity approach} & Sediment and phosphorus loss estimation, & & \\  & phosphorus contribution to the drains, & & \\ \cline{1-1}  & management practice effects simulation on runoff, sediment, and phosphorus loss. & & [224, 225, 226] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Water solute leachate models as prediction tools for surface and subsurface soft water and crop change management (not all mentioned in the corpus of the text) and related information.