esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Thermography Data Collection and Preparation
### Analysis of Similarities Between Real and Synthetic Thermographic Image Datasets
In comparison, the mean pixel value change rate distribution SID of Pre-Transformation in the synthetic thermographic image dataset is 0.347, while the mean SID of Post-Transformation is 0.086. After transformation, the mean of the synthetic thermographic image dataset moves approximately 0.155 (74%) closer to the mean of the real thermographic image dataset compared to the pre-transformation dataset. Thus, the transformation module improves the similarity between the synthetic and real thermographic image datasets in terms of their means. This demonstrates that the transformation module effectively adjusts the central tendency of the synthetic thermographic image dataset to align more closely with that of the real thermographic image dataset. The standard deviation of the pixel value change rate distribution RID in the real thermographic image dataset is 4.193. In comparison, the standard deviation of the pixel value change rate distribution SID (Pre-Transformation) in the synthetic thermographic image dataset before transformation is 0.902, while the standard deviation of SID (Post-Transformation) after transformation is 2.201. Following the transformation, the standard deviation of the synthetic thermographic image dataset becomes approximately 1.299 (39.5%) closer to that of the real thermographic image dataset compared to the pre-transformation dataset. This indicates that the transformation module improves  
Figure 8: Change Rate Normal Distributions for Real and Synthetic Thermographic Image Datasets. the similarity between the synthetic and real thermographic image datasets in terms of standard deviation. Since the standard deviation represents the spread of the data distribution, it can be concluded that the transformation module adjusts the distribution of the synthetic dataset to better match that of the real thermographic dataset. Across all specimen IDs, the transformation module proves to be an effective method for narrowing the gap between the synthetic and real thermographic datasets, particularly by improving their similarity in terms of the mean and the standard deviation.
## 2 Materials and Methods
### Magnetic Resonance Data Collection and Process  
MRI images were acquired using a 3T Philips scanner (Ingenia 3.0T MRI system, Amsterdam, The Netherlands) with a T1-weighted turbo spin echo sequence (slice thickness = 10 mm, interslice gap = 0 mm, TR = 680 ms, TE = 20 ms) to scan the leg in the axial plane. Muscle and tendon segmentation was performed in the ImageJ software (v. 1.44b, National Institutes of Health, Bethesda, MD, USA). Each single muscle and tendon was manually outlined along its borders, and the area of each segmented region was then calculated.  
Muscle volume was determined by summing the volumes of each axial slice (cross-sectional area \(\times\) slice thickness) [26]. The physiological cross-sectional area (PCSA) of the quadriceps femoris was estimated by dividing the muscle volume by the length of the vastus lateralis which can be considered to be a good representative proxy for the mean fascicle length of the four heads of the quadriceps femoris muscle [27]. The AmAt ratio, representing the muscle-to-tendon structural proportions, was defined as the PCSA divided by the maximum tendon cross-sectional area. The tendon length was measured in sagittal plane images where the knee joint was in a neutral position from the apex of the patella and the tuberoosities tibiae.  
The center of rotation of the knee was determined through [PERSON] graphical analysis [28] from sagittal MRI images taken at knee flexion angles of 180\({}^{\circ}\), 160\({}^{\circ}\), and 140\({}^{\circ}\). Images at different joint angles were superimposed with transparency in order to determine the center of rotation; then, the moment arms for the patella tendon at different joint angles were determined. The patellar tendon action line was considered as a straight line between the apex of the patella and the tuberoosities tibiae. The shortest perpendicular distance from the center of rotation to the tendon action line was considered to be the patellar tendon moment arm length. A second-order polynomic curve was fitted over the three calculated moment arm length data at the three different knee joint angles to estimate dynamic moment arm length changes with the angular displacement in the knee during the trials.  
Figure 2: The placement of surface electromyography (sEMG) sensors on the quadriceps femoris muscle. The sensors were positioned over the rectus femoris (1), the vastus lateralis (2), and the vastus medialis (3).
## 2 Materials and Method
### Data reduction, Quantification, and Visualization
#### 2.6.2 Quantification and Image Visualization  
The classical least squares (CLS) fitting procedure was used to determine the phase proportion at each pixel of an image. This method is based on the assumption that a spectrum from a polyphase material is a linear mixture of the spectra from the pure phases. Within the multidimensional spectral array, the CLS fitting procedure finds a linear combination of reference spectra from the pure phases, which best fits the raw spectrum at each position (pixel). The CLS fitting procedure is explained in more detail elsewhere [24]. The resulting image is created by false-coloring each pixel of the image relative to the fraction of each component in the spectrum. In the unmixed color-coding algorithm that is used in this work, the phase with the highest intensity contribution in a Raman spectrum will be displayed in a color assigned to that particular phase. The brightness of the color displays the overall intensity of the spectrum. The analyzed hyperspectral Raman images are displayed in the \"normalized scores\" mode, where the proportions of the phases are normalized to 100%. Additionally, the images are smoothed, which improves the visualization of grain boundaries (compare Figure 6a,b). Such a procedure is straightforward if pure reference spectra are available. Due to the small grain size of the mineral phases and the limits of the spatial resolution, most spectra of an image are composed of contributions from multiple phases. Therefore, the temperature-dependent in-house reference spectra (E1A and E1B) were used as input reference spectra for the CLS fitting routine.  
Figure 5: (**a**) The black body radiation as a function of temperature. (**b**) The Raman intensity of the A\({}_{1}\) quartz band near 464 cm\({}^{-1}\) (lower inset diagram) and the intensity of the black body radiation at 1100 \({}^{\circ}\)C as a function of the confocal pinhole.
## 3 Simulation Results  
In this section, we analyze the effect of DAE-EDMR and T-DAE-EDMR from various perspectives. We compare the performance of our proposed methods with that of the conventional algorithms, which are semi-definite relaxation [16], the nonlocal patch tensor-based method [18], and conventional NN-based methods referred to as deep-learning-based methods A [19] and B [22]. We also provide the result for the undenoised EDM to show the denoising performance of each algorithm. Overall, we will demonstrate the effectiveness of each scheme in denoising the noisy EDM and assess its impact on the localization performance.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 3 Methodology
### Entropy-Regularized Federated Optimization Algorithm  
We adopt a standard federated optimization workflow (similar to FedAvg) augmented with entropy regularization on each client. Algorithm 1 summarizes the ERFO procedure. At a high level, in each communication round \(t\), a subset of \(K\) clients (or all clients, depending on the scenario) receives the current global model \(w^{t-1}\) from the server. Each client then performs local stochastic gradient descent (SGD) on the augmented objective \(\tilde{F}_{i}(w)\) (Equation (4)), which entails computing the entropy gradient \(g_{H}\) and adding the term \(\lambda_{t}g_{H}\) to the usual loss gradient during each update step. Here we allow the regularization weight \(\lambda_{t}\) to potentially vary with the round \(t\) (to accommodate both constant and decaying schedules; see next subsection). After \(E\) local epochs (or a certain number of SGD steps) on each client, the resulting model updates are sent back to the server and averaged to form the new global model \(w^{t}\). This iterative process continues for \(T\) rounds or until convergence.  
In the algorithm, the key difference from standard FedAvg is in the local update (lines 7-11), where the gradient of the entropy term, \(g_{H}\), is computed at the start of each epoch and added (with weight \(\lambda_{t}\)) to the minibatch gradient. For efficiency, one could update \(g_{H}\) less frequently than every epoch (since \(g_{H}\) depends on the deviation \(w_{i}-w^{t-1}\), which changes gradually as the local model \(w_{i}\) is updated). However, in our implementation we recompute it each epoch to ensure accuracy. The extra computational overhead of computing \(g_{H}\) is modest: it requires computing the absolute difference between current and initial weights, normalizing to obtain \(p\), and a pass to compute Formula (5) for each weight. This is \(O(d)\) per computation, which in our experiments is negligible compared to the cost of processing data (especially for neural network models where \(d\) is on the order of millions, and each forward/backward on a batch also costs \(O(d)\) operations).  
The algorithm uses \(\lambda_{t}\) to denote the regularization weight in round \(t\), allowing either a constant value or a schedule that changes over time. In the next subsection, we will detail two specific choices (fixed and decaying \(\lambda_{t}\)) and when each is used. We also note that the server may choose to perform learning rate warmup on the clients, i.e., starting with a smaller \(\eta\) in early iterations and then increasing to the base learning rate. Warmup can stabilize training in the presence of the entropy term, which might initially introduce large gradients if the global model \(w^{0}\) is poorly tuned. In our implementation, we use a short warmup (e.g., scaling \(\eta\) linearly over the first few batches) especially for the decayed-\(\lambda\) variant (UNSW-NB15), where \(\lambda_{0}\) is relatively large initially.
## 6 Conclusions  
1. The proposed method uses five indexes: rock quality designation (RQD), uniaxial compressive strength (Rc), the integrity coefficient of rock mass, groundwater seepage, and joint condition as initial evaluation indexes, constructs a concept lattice according to training samples, generates a corresponding Hasse diagram, then uses concept lattice reduction to reduce the initial evaluation indexes. The reduced indexes are RQD, Rc, and groundwater seepage. By calculating correlation coefficients between the evaluation indexes, we prove the effectiveness of reduction.
2. Our method uses a symmetrical alpha stable distribution to replace the commonly used Gauss distribution in the probabilistic neural network and optimize parameter \(\alpha\), \(\gamma\) of the alpha stable distribution with a genetic algorithm, resulting in \(\alpha\) and \(\gamma\) values of 0.2848 and 1.5963. This makes the enhanced probabilistic neural network more adaptable and improves the accuracy rate of the model prediction result.
3. Taking the roadway of the Jianlinshan mining area in the Daye iron mine as an example, we applied the prediction model for roadway surrounding rock stability based on the concept lattice and improved probabilistic neural network. The accuracy rate was up to 90%, which is 20% higher than the accuracy rate of the prediction model based on the original probabilistic neural network. In other words, the proposed prediction model can meet the demands of practical engineering and provides a new method for predicting the stability of roadway surrounding rock.  
[PERSON] was in charge of contributing the analysis method and the program codes, analyzing the data, and writing the paper. [PERSON] and [PERSON] were in charge of the supervision of this research and helping in revising. [PERSON] was responsible for the data analysis. All authors have read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 51574183) and the National Natural Science Foundation of China (No. 51704213).  
We acknowledge the valuable comments by two anonymous reviewers.  
The authors declare no conflicts of interest.
## 4 PFAC Calibration Results
### Cal/Val Results at Crossovers  
The groundtracks of the Sentinel-3A and Jason-3 intersect at a point (Lat = 34.63893946, Long = 23.98522449) about 20 km south of Gavdos island (Figure 3). To compare sea-surface heights observed by these two satellites at that crossover location with some confidence, their flyover had to be separated by no more than 2 days. Between March 2016 and March 2020, this condition was fulfilled 19 times: 9 times the temporal separation of Sentinel-3A and Jason-3 was 1 day and 2 days (48-h) for the remaining cases. For each pair of altimeters, linear regression was carried out on orbital data to accurately determine the geodetic coordinates of their crossover location at sea. Then, the exact SSH value at the crossover location for each satellite altimeter was computed, taking into consideration the geoid models and the satellite direction in the region of crossover. Accordingly, 10 sampling points astride the crossover location have been used. These 20 altimeter observation points correspond to a distance of about 5 km along the satellite ground track. Figure 28 presents the outcome of this crossover analysis. It can be seen that the dispersion of the relative bias is of the order of \(\pm\)4 cm but the average bias has been calculated to be +1.7 cm. This implies that Sentinel-3A measures the SSH +1.7 cm higher compared to Jason-3. In addition, it seems that the uncertainty of this simplified and localized version of the multi-mission crossover analysis is similar to the FRM uncertainty (Tables 4 and 5) of the sea-surface and transponder calibrations. Note here that in crossover analysis no in-situ measurements were used. Figure 27: Transponder results for Sentinel-3A and Sentinel-3B satellites during their tandem phase. The bias results are given in mm to reveal slight differences between Sentinel-3A and Sentinel-3B. This section presented the latest calibration results for Sentinel-3A, Sentinel-3B and Jason-3 satellite altimeters as determined by diverse techniques at the PFAC. Table 7 provides a concise summary of these Cal/Val results. The results of this investigation can be summarized as follows: (1) the bias in sea-surface height determination by the satellite altimeters is within their specification of \(\pm\)1.5-2.0 cm [17], (2) no significant drift in altimeters' performance has been monitored, (3) confidence on calibration results has been improved as diverse and independent Cal/Val techniques produce similar results, (4) the difference between sea-surface and transponder Cal/Val methodologies is within their FRM uncertainty, (5) Sentinel-3A measures sea-surface height higher than Jason-3, and (6) Sentinel-3B measures sea-surface height higher compared to Sentinel-3A. Figure 28: Relative sea surface height bias of Sentinel-3A and Jason-3 altimeters over their intersection point, 20 km south of Gavdos.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology
### Impact of Failure Mechanisms on Predicted Service Life  
The predicted service life is the age at which the component reaches a state where it is no longer feasible or economical to refurbish and must be replaced. It is typically calculated by multiplying the standard life expectancy (\(SLE\)) in standard deterioration conditions by coefficients that reflect the impact of various failures observed in the building. The partial impact of a given failure mechanism on a building component's overall service life is expressed through a life expectancy limiting coefficient (\(LELC\)), as follows (3):  
\[LELC=1-\left(\frac{SLE-LEFC}{SLE}\right)\times IC \tag{3}\]  
\(LELC\)-Life Expectancy Limiting Coefficient  
\(SLE\)-Standard Life Expectancy  
Figure 1: Predicted Service Life Expectancy and Performance Deterioration of Building Components.  
_LEFC-Life Expectancy under Failure Conditions_  
_IC-Impact Coefficient_  
The impact coefficient reflects the extent of influence a particular failure factor has on the component's overall deterioration mechanism. Each failure factor affects the component's overall life expectancy differently, and accordingly its impact coefficient is determined. The coefficient value ranges from 0 (failures that do not affect the component's service life) to 1 (failures that have maximal effect on the service life.  
Calculating the LELC of a component in specific failure is based on the predicted service life using typical deterioration patterns for the component (in both standard deterioration conditions and failure conditions). The LELC decreases as the impact coefficient (IC) increases and as the life expectancy of the component in failure conditions (LEFC) decreases. In other words, the more severe and impactful the failure, the lower the LELC value, increasing its effect on the component's overall lifespan.  
It is important to note that this model considers the impact of individual failure mechanisms on the component's service life expectancy separately, and does not account for the combined effects of various failure mechanisms and interactions between them (whether independent, mitigated, or synergistic).
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 2 Materials and Methods
### Data Processing  
#### 2.3.1 SIF Data Preprocessing  
The determination of the instantaneous intensity of SIF throughout the day using the sun-induced chlorophyll fluorescence (Auto SIF) is susceptible to the influence of the intensity of the effective solar radiation. Normalization of SIF by PAR can reduce the impact  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Presentation** \\ \hline Spectral range & 640–800 nm, HR 200–1000 nm \\ Spectral resolution & 0.39 nm, HR 1 nm \\ Spectral response & Up to 70\% quantum efficiency at 780 nm, up to 50\% \\ Optical transmission mode & Bifurcated fibers, prisms, multi-channel MPM \\ Temperature control system & Temperature range 25 \(\pm\) 1.00 \({}^{\circ}\)C, humidity level \(<\) 60\% \\  & (except under extreme conditions) \\ Fiber optics & 600 \(\mu\)m, 1000 \(\mu\)m \\ \hline \end{tabular}
\end{table}
Table 2: Basic parameters of Auto SIF.  
of incident radiation intensity on canopy SIF intensity, and improve the effectiveness and accuracy of SIF spectrum detection of crop biometric information [52].  
\[\text{PAR}=\int_{\lambda=400}^{\lambda=700}\text{I}_{\lambda}\text{d}\lambda \tag{4}\]  
\[\text{SIFy}=\frac{\text{SIF}}{\text{PAR}} \tag{5}\]
# Experimental Investigation into the Number of Phases in Debris Flows
## 4 Laboratory Experiments
### Displacement Experiment of Fluid (Water) Under Constant-Head Condition  
To further investigate the potential for the relative motion of fluid and solids in debris flows, we conducted displacement experiments [81], which were run by displacing the fluid (water) in the experimental slurries with (red) Rhodamine B solution under constant-head conditions [76; 82; 83; 84] (Figure 3).  
The tested slurry columns of two sizes, contained in PVC cells, stood 10 cm (for the slurries of \(d_{\rm max}\) = 2 and 10 mm) and 20 cm (for the slurries of \(d_{\rm max}\) = 30 mm) (\(H\)) tall with respective diameters of 10 and 39 cm, and respective constant cross-sectional areas (\(A\)) of \(\sim\)79 and \(\sim\)1195 cm\({}^{2}\). They were overlain by 4 and 6 cm high Rhodamine B solution (serving as displacing fluid [85]) columns, respectively, which were maintained using the recharge container and collector A for overflow placed on electronic scales. Therefore, constant hydraulic gradients (\(J\)) of 1.4 and 1.3 were established within the slurries with the two different \(d_{\rm max}\) values. The fluid (water) displaced from the slurry column (effluent) was captured below the lower end of the column using collector B, which was placed on an electronic scale.  
The temporal color changes and cumulative quantity of effluent (displaced fluid) (converting weight to volume by assuming that the density of water is 1 g/cm\({}^{3}\)) (\(V_{\rm we}\)), and the cumulative quantity of the injected Rhodamine B solution (displacing fluid) (the difference between the quantity in the recharge container and collector A) (\(V_{\rm wd}\)) were measured at 5 min intervals throughout the 600 min testing period.  
Using the obtained measurements, the percentages of \(V_{\text{we}}\) and \(V_{\text{wd}}\) in the initial amount of water contained in the tested slurry (\(V_{\text{wi}}\)) (\(P_{\text{e}}\) and \(P_{\text{d}}\)) were derived as follows:  
\[P_{\text{e}}=\frac{V_{\text{we}}}{V_{\text{wi}}}\times 100\% \tag{2}\]  
\[P_{\text{d}}=\frac{V_{\text{wd}}}{V_{\text{wi}}}\times 100\% \tag{3}\]  
The potential for the relative motion of fluids and solids is characterized by the displacement ratio (\(DR\)). \(DR\) is defined as the percentage of the volume of water displaced from a unit slurry volume (i.e., representative elementary volume, REV [82]) under a unit hydraulic gradient (\(J\)) relative to that originally contained in the REV.  
\[DR=\frac{V_{\text{we}}}{A\times H\times(1-C_{\text{v}})\times J}\times 100\% \tag{4}\]  
\[DR=\frac{V_{\text{we}}}{V_{\text{wi}}\times J}\times 100\%=\frac{P_{\text{e}}}{J} \tag{5}\]  
Thus, for each of the 12 slurries (Table 1), the time series of \(P_{\text{e}}\), \(P_{\text{d}}\), and \(DR\) were obtained.
# Development and Testing of a Two-UAV Communication Relay System
## 3 Hardware Platform
### Communication Subsystem
The telemetries transmitted in one of the 50 sub-channels that were obtained by dividing the 915 MHz band into 50 equal portions. On the occurrence of the alternation of a transmission window, both Tn-1 and Tn-2 simultaneously hopped to a new sub-channel, which was determined by a random channel hopping sequence shared between Tn-1 and Tn-2. As shown in Table 1, T1-1 and T1-2 were configured to use a different hopping sequence than T2-1 and T2-2. Therefore, although multiple devices might be transmitting radio waves at the same time, the sub-channels in which they are transmitting are different, as illustrated in Figure 7.