esa-sceva/satcom-chunk-collection huggingface dataset:
# Excavation and Construction Technology of Diversion Tunnel under Complex Geological Conditions
## 6 Design of Excavation Construction Methods
### Excavation of Unfavorable Geological Tunnel Section at the Entrance  
The entrance section of the diversion tunnel is a single-track tunnel with a length of 150.62 m, with a horseshoe shaped cross-section and an excavation diameter of 15 m. The surrounding rock is mainly composed of sand and gravel, with a small number of boulders. The excavation of the section adopts a two-layer excavation method, with an upper excavation height of 7.5 m and a lower excavation height of 4.5 m. The excavation is carried out from the tunnel to the entrance, and the slope support of the tunnel face and double row of 60 mm @ 500 mm, L = 10 m pipe shed support are completed at a distance of 15 m near the entrance. Then, the excavation is carried out from the outside to the inside.  
1. Upper excavation support. First, within the 140\({}^{\circ}\) range of the top arch \(\Phi\)32 self-propelled anchor rods are used for advanced support, and then a hydraulic hammer is used for short footage (L \(\leq\) 1 m) excavation. For sections where the surrounding rock cannot self-stabilize, the top arch and side walls are excavated first, and a buttress is reserved in the middle of the face. After the completion of steel grating and shotcrete support, the excavation is carried out, and the strong support is followed up every cycle. The strong support method is anchor rod + hanging net + spraying steel fiber concrete + steel grating.
2. Lower excavation support. Use a backhoe to directly excavate and then support and follow up.
3. Key points of construction technology. It is necessary to strictly control the upper circulating footage to not exceed 1.0 m, and excavation and support should be carried out alternately and gradually; in the upper support, 3-4 anchor rods are used to lock the feet on both sides of the steel grating; the exposed part of the self-propelled anchor rod or advanced anchor rod should be connected to the steel grid and subjected to joint forces; the sprayed concrete is relatively thick and needs to be sprayed multiple times, with each spraying thickness not exceeding 0.1 m.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 3 Results
### Applications
#### 3.2.3 Landslide-Dammed Lake  
The area and volume of landslide-dammed lakes corresponding to dam heights of 5, 10, and 15 m were simulated using each DEM. The HMA DEM lake was most similar with the UAV DEM lake (Figure 8), followed by the AW3D30. The SRTM DEM produced lakes of much greater area and volume than the UAV DEM, and the 5 m high SRTM dam had a comparable volume to the 10 m dam scenario for the other DEMs. The 15 m dam was not simulated for the SRTM, nor were any dams for the GDEM2, since the simulated dams were circumvented laterally by the lakes.  
Figure 6: **(a)** Marsyangdi River channel upstream of Lower Pisang village. The river channels derived from each DEM are shown. **(b)** Buildings of Lower Pisang village with the different river channels and UAV Orthophoto background. **(c)** Terrain elevation above the Marsyangdi River channel, which exhibits an elevation drop of approximately 9 m over the reach shown.  
The simulated floods with peak discharges of 937, 468, and 234 m\({}^{3}\) s\({}^{-1}\) were contained within the river banks and did not inundate the village of Lower Pisang (Figure 9). Similarly, the worst-case scenario (Simulation 4, Qp = 1874 m\({}^{3}\) s\({}^{-1}\)) did not inundate the main village. Simulation 4's flood wave reached Lower Pisang in five minutes, and depths peaked within nine minutes. By contrast, for Simulation 3 with the lowest Qp, these times were 12 and 25 min, respectively.  
Figure 8: (**a**) Area–volume relationships derived for 5, 10, and 15 m hypothetical dams blocking the Marsyangdi River. Second-order polynomial trend lines are fitted with the exception of the SRTM (linear trend). (**b–d**) The extent of lakes forming behind simulated dams blocking the Marsyangdi River for the UAV DEM, HMA DEM, and AW3D30 and SRTM DEM, respectively. Background is the UAV orthophoto.  
Figure 7: Pre-earthquake (**a**) and post-earthquake (**b**) RapidEye images of the Marsyangdi River upstream of Lower Pisang village (false-colour composites of bands near-infrared, red, and blue). (**c**) DEM difference of the UAV DEM (26 April 2018) minus the HMA DEM (15 March 2015) showing a landslide (inset) that partially blocked the river. The inset background in (**c**) is the UAV orthophoto. A photograph of the landslide is provided in Figure S1.
## 2 Materials and Methods
### CNN-LSTM Model  
The CNN-LSTM model is a hybrid model of two neural network models. We first used CNN to extract data features and LSTM to further extract temporal features. The specific structure was as follows: the CNN model used a Conv1D layer and multiple input data as the time series; the kernel moved in one dimension along the time axis, then we input the data into LSTM layer and used the LSTM layer to obtain the long-term characteristics of the pitch axis data. Finally, it output the predicted value. It is shown in Figures 3 and 4.  
According to the above, the main factors affecting the pitch axis position include the ideal position calculated from the sun vector, the pitch axis operation error, the motor current, and the motor speed. The pitch axis motor current (C; unit: A), initial position (I; unit: \({}^{\circ}\)), pitch axis motor speed (S; unit: \({}^{\circ}\)/s), and pitch axis operation error (E; unit: mV) were set as input data. The expected position of the pitch axis (Pt, unit: \({}^{\circ}\)) was output through two CNN network layers and one LSTM layer. The initial position value was equal to the ideal position value calculated using Formulas (1) and (2) as shown in Figures 5 and 6.  
Figure 5: Proposed CNN-LSTM model architecture with input sequence, two CNNs layer, an LSTM layer, a dropout layer, dense layer, and an output.  
In Figure 6, \(C\) represents the pitch axis motor current, \(I\) represents the ideal sun position, \(S\) represents the pitch axis motor speed, \(E\) represents the pointing error, and \(P_{t}\) is the predicted position value of the output.  
In the CNN network, we set the data input feature to 4. We conducted performance tests using input time sliding of 3, 5, or 10. Taking into account performance factors, the best performance was found for a time sliding of 3. The time sliding window was set at 3, the stride was set at 1, the kernel size was set at 1, and the activation function used RELU. The activation function of the LSTM layer was RELU. We used a grid search for hyperparameter optimization. We tested the performance of the SGD, Adagrad, and Adam optimizers [17] in this application. Finally, the optimizer selected Adam, the loss function selected MSE, and the drop layer was set to 0.35. The following network architecture design was sampled. It is shown in Figure 7 and Table 1.  
Figure 6: CNN-LSTM model operation diagram, the yellow parts represent the layer of CNN, and the green parts represent the layer of LSTM.  
Figure 7: Proposed CNN-LSTM, model architecture parameter.
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Parametric Investigations  
#### 3.1.1 Effect of Fractals without Miniaturization  
The reflection coefficient plots of three fractal operations on the modified circular patch monopole antenna (with substrate size = 47.92 \(\times\) 46.15 mm\({}^{2}\) without miniaturization) are compared in Figure 8. The modified circular (semi-circular triangle) patch results in a tri-band reflection coefficient curve. The lower band of iteration 0 covers the n77 and n78 5G sub-6 GHz bands. Additionally, it also covers the C-band and X-band. When iteration 1 is applied in this antenna, it results in a 4.01 dBi improvement in the gain value, at the cost of a reduction in the reflection coefficient value. Iteration 1 also adds an additional tuning frequency
## 3 Results and Discussion  
Synthesis and Self-Assembling Behavior of \(\beta\)-Annulus Peptide Bearing dT\({}_{20}\) at the N-Terminus  
We designed a \(\beta\)-annulus peptide bearing dT\({}_{20}\) at the N-terminus that was directed to the interior of the capsid (Figure 1) to encapsulate mRNA via hybridization between dT\({}_{20}\) and the poly(A) tail at the 3\({}^{\prime}\)end of mRNA. A 5\({}^{\prime}\)-terminal animated 20-mer thymidine nucleotide (dT\({}_{20}\)-NH\({}_{2}\)) was reacted with \(N\)-succinimidyl 3-(2-pyridyldithio)propionate (SPDP) to obtain a dT\({}_{20}\) bearing pyridyl disulfide group at the 5\({}^{\prime}\)-end (PySS-dT\({}_{20}\)). A Cys-\(\beta\)-annulus peptide (CINHVGGTGGAIMAPVAVTRQLVGS) containing Cys at the N-terminus was synthesized by standard Fmoc-based solid-phase methods according to our reported procedure [28]. PySS-dT\({}_{20}\) was attached to the Cys-\(\beta\)-annulus peptide with a disulfide-exchange reaction. A reversed-phase HPLC chart of the reaction mixture showed one peak at 26.7 min, which was different from the retention time of dT\({}_{20}\)-NH\({}_{2}\) and PySS-dT\({}_{20}\) (Figure 2A). The purified product was assigned by MALDI TOF-MS as dT\({}_{20}\)-modified \(\beta\)-annulus peptide (dT\({}_{20}\)-SS-\(\beta\)-Annulus) (Figure 2B). DLS of a 50 uM solution of dT\({}_{20}\)-SS-\(\beta\)-annulus in PBS (pH 7.4) exhibitedformation of \(77\pm 21\) and \(40\pm 8\) nm assemblies (Figure 2C). TEM images of the aqueous solution stained with phosphotungstic acid showed the formation of spherical assemblies of approximately 40 nm in diameter (Figure 2D). The concentration dependence of the size distribution in DLS measurement indicated that dT\({}_{20}\)-SS-\(\beta\)-annulus formed assemblies with sizes ranging from 30 to 100 nm in a concentration range of 5-125 \(\upmu\)M (Figure S1). This tendency was similar to the findings in our previous report [35]. Notably, \(\beta\)-annulus peptide modified with highly charged dT\({}_{20}\) formed relatively stable artificial viral capsids.
## 2 Materials and Methods
### Methods
#### 2.3.4 Estimating GPP by VI and Statistical Analysis  
Our hypothesis is that GPP is a function of the VI and \(\text{PAR}_{in}\), as shown below.  
\[\text{GPP}\propto\text{VI}\times\text{PAR}_{in}. \tag{3}\]  
Because different remote-sensing chlorophyll-related VIs have unique ranges and the environment has various conditions, the relationship for the \(\text{VI}_{i}\)-\(\text{PAR}_{in}\)-GPP model was not constant.  
\[\text{GPP}_{VI_{i}}=a_{i}\times\text{VI}_{i}\times\text{PAR}_{in}+b_{i}. \tag{4}\]  
In Equation (4), the coefficient values of \(\text{a}_{\text{i}}\) and \(\text{b}_{\text{i}}\) were changed according to the specific \(\text{VI}_{\text{i}}\). In each iteration, we randomly selected 70% of the observations (calibration set), including 16-day interval data of the mean \(\text{GPP}_{\text{EC}}\), time-series rebuilt \(\text{VI}_{\text{i}}\), and mean \(\text{PAR}_{\text{in}}\), to calibrate the coefficients \(\text{a}_{\text{i}}\) and \(\text{b}_{\text{i}}\). The remaining 30% (validation set) of the observations were used to validate the relationship between \(\text{GPP}_{\text{VII}}\) and \(\text{GPP}_{\text{EC}}\). The correlations of \(\text{GPP}_{\text{VII}}\) and \(\text{GPP}_{\text{EC}}\) in the validation set were described by the determination coefficient (\(R^{2}\)), root-mean-square error (RMSE), and related RMSE (rRMSE, which is defined as the ratio of RMSE and mean observed GPP). We then repeated this iteration 100 times. The mean \(R^{2}\), RMSE, and rRMSE values of the 100 iterations in the validation set were used to quantify the temporal relationship between \(\mathrm{GPP_{VI}}\) and \(\mathrm{GPP_{EC}}\), while the mean \(\mathrm{a_{i}}\) and \(\mathrm{b_{i}}\) values in the calibration set were the general coefficients of \(\mathrm{VI_{i}}\)-\(\mathrm{PAR_{in}}\)-\(\mathrm{GPP}\).  
Then, we mapped the GPP distribution based on the spatio-temporally continuous \(\mathrm{VI_{i}}\) and the relationship of \(\mathrm{VI_{i}}\)-\(\mathrm{PAR_{in}}\)-\(\mathrm{GPP}\) at each site. We simplified the expression for the relationship of \(\mathrm{VI_{i}}\)-\(\mathrm{PAR_{in}}\)-\(\mathrm{GPP}\) into \(\mathrm{GPP_{VI}}\); for example, \(\mathrm{GPP_{EVI}}\) represents the GPP estimated by EVI and \(\mathrm{PAR_{in}}\) in the later sections.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 2 An Overview of Rail Defects and of the Techniques for Their Detection
### Rail Defects
#### 2.1.1 Rail Manufacturing Defects  
Rail manufacturing defects usually comprise inclusions or incorrect local mixings in rail steel; these generate localized stresses under operative load, which, in turn, can trigger a rail failure process [1; 11]. Damages of this category include _transverse defects_ (TD) and _longitudinal defects_ (LD) [1; 12; 13]. The former type of defects consists of a progressive fracture developing in the railhead parallel to the transverse direction [1] (see Figure 2); the latter type, instead, is represented by an internal progressive fracture propagating longitudinally in rails. Longitudinal defects can be further subdivided into _vertical_ and _horizontal_ split heads (see Figure 3).  
Figure 3: Three-dimensional representation of longitudinal defects: (**a**,**b**) horizontal fissures; (**c**,**d**) vertical fissures.  
Figure 2: Three-dimensional representation of transverse defects: (**a**) section with shell; (**b**) lateral view.  
2.1.2 Defects Due to Improper Use or Handling of Rails  
Defects related to improper use or handling of rails are usually caused by train wheels spinning on rails (this produces the so-called _wheelburn_ defect, shown in Figure 4) or by sudden train brakes [1].
# Spectrophotometric Determination of Biopolymers in Alabama Benchmark Soils
## 3 Results  
### Soil Cellulose, Acetyl Bromide Lignin, and ACE Protein  
The soil cellulose (SC), acetyl bromide lignin (ABL), and ACE protein (ACEP) content in the nine soils are shown in Figure 2 and are in the order lignin \(>\) cellulose \(>\) proteins. The lowest values for SC (0.53 (\(\pm\)0.08)) mg/g, ABL (5.38 (\(\pm\)1.50)) mg/g, and ACEP (2.3 (\(\pm\)2.30)) mg/g were recorded for the Maryyn sandy loam. The highest value for SC was recorded for Sumter clay at 9.79 (\(\pm\)0.390) mg/g, the highest for ABL (21.64 (\(\pm\)0.691) was observed for Houston clay, while the highest value for ACEP (9.48 (\(\pm\)0.880)) mg/g was recorded for Altavista silt loam.  
Mean values of cellulose in the soils ranged from 0.53 (\(\pm\)0.082) mg g\({}^{-1}\) to 9.78 (\(\pm\)0.39) mg g\({}^{-1}\), AB lignin from 5.38 (\(\pm\)1.50) mg g\({}^{-1}\) to 21.64 (\(\pm\)0.691) mg g\({}^{-1}\), and ACE protein from 2.3 (\(\pm\)0.075) mg g\({}^{-1}\) to 11.20 (\(\pm\)0.83) mg g\({}^{-1}\). The differences between the mean values of the soil biopolymers are shown in Figure 3.  
The coefficient of variation (CV) of the three biopolymers in the soils was highest for ACE protein (56.8%), then cellulose (55.2%), and then lowest for AB lignin (44.7%). Of the three biopolymers, the lowest values were recorded for cellulose in the low-pH soils, while  
Figure 3: Mean values of cellulose, AB lignin, and ACE protein (mg/g) in soils. For each biopolymer, mean values covered by the same colored bar are not significantly different. (Tukey, \(p\leq 0.05\)).  
Figure 2: Cellulose, acetyl bromide lignin, and autoclaved citrate extractable protein in soils: Marvyn sandy loam (MSL), Altavista silt loam (ASL), Luverne sandy loam (LSL), Compass loamy sand (CLS), Decatur silt loam (DSL), Cahaba sandy loam (CSL), Houston clay (HCL), Vaiden clay (VCL), and Sumter clay (SCL). Data are represented as scatter plots.  
the high-pH soils had the lowest ACE protein. Clay soils had higher cellulose and lignin concentrations than other soil types. Principal component analysis discriminated the three biopolymers in terms of biopolymer forms and soil type (Figure 4). Lignin and cellulose content strongly correlated with clay soils and proteins with silt loam or loamy sand soils. Low soil pH tends to favor the accumulation of soil protein while high soil pH favors the buildup of lignin.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 4 Experimental Setup
### Baselines
* **FedCurv--**an elastic weight consolidation approach that penalizes changes to important parameters via a Fisher-information-based quadratic term \(\frac{\lambda}{2}\sum_{j}\Omega_{j}(w_{j}-w_{j}^{(t)})^{2}\) (using the recommended \(\lambda\)). * **Ditto--**a personalized FL framework in which each client trains its own model \(w_{i}\), minimizing \(F_{i}(w_{i})+\frac{\lambda}{2}\|w_{i}-w^{(t)}\|^{2}\) alongside the global model to balance personalization and generalization. * **FedAdam--**a FedOpt method applying the Adam update at the server (with server \(\eta=10^{-3}\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\)) while clients train locally with [PERSON]. * **FedYogi--**similar to FedAdam but using the Yogi optimizer at the server (with the same hyperparameters and default \(\tau\)) to temper growth of second-moment estimates. * **ERFO (Ours)--**elastic-regularized federated optimization, which augments each client's objective with a round-dependent entropy penalty \(\frac{\lambda_{t}}{2}\|w-w^{(t)}\|^{2}\) (fixed or decayed) to encourage high-entropy updates without extra communication.
## 3 Real-Time Hybrid Simulation (RTHS)
### Experimental Setup  
The experimental and numerical substructures and transfer system were interrelated according to the schematic diagram shown in Figure 4 to develop the RTHS technique. The analytical substructure was affected by the input signal, which corresponds to the human-induced measured gait loads, \(F_{human,m}\big{(}x_{p},t\big{)}\). Simultaneously, the numerical footbridge responds to this spatiotemporal load excitation according to its dynamic properties, which generates the dynamic response \(U_{r}\big{(}x_{p},t\big{)}\). Structural accelerations were multi-axially reproduced using HSI-MTF, \(\ddot{U}_{m}\big{(}x_{p},t\big{)}\), and were induced in the experimental test subjects, as described in Section 3.5. The reaction forces exerted by human gait under dynamic HSI effects were measured by an instrumentation system coupled to the HSI-MTF, which is composed of multi-axial cell loads. The human-induced gait loads, \(F_{human,r}\big{(}x_{p},t\big{)}\), represent the total feedback force, \(F_{human,m}\big{(}x_{p},t\big{)}\), of the numerical substructure, which closes the dynamic loop. This process was programmed in Simulink software (V21.0) and executed with a SpeedGoat processor using a host computer with the \(Xpc\) protocol of MATLAB (V21.b) and a sampling frequency of 1024 Hz. SpeedGoat has a high processing capacity to guarantee the real-time evaluation of the hybrid simulation.
## 3 Discussion  
### Empirical Case Study and Analysis  
To corroborate this method's efficacy, the wind speed data of the SDWPF wind turbine dataset over a period of one year was analyzed. The initial step involved purifying extensive historical data from the wind farm's SCADA system, employing interpolation and clustering algorithms. The refined dataset is depicted in Figure 4, setting the stage for a comprehensive evaluation.
# Prediction Method of Swirling Flame Lean Blowout Based on Flame Image Morphological Features
## 4 Results and Discussion  
### Prediction Method on LBO  
Four models, Models (a)-(d), are trained with different data sets selected. After the determination of the time series information length \(k\), some images cannot be included in the data sets, due to lacking sufficient time-series information (\(t<k\)), complete extinction (\(S=0\)), and incomplete description of the blowout state (\(t>t_{\text{end}}\), where \(t_{\text{end}}\) is the last \"zero-node\" moment). In total, 90% of each data set is used as the training sets, and the remaining 10% is used as test sets so as to train and test Models (a)-(d). A summary of these models is listed in Table 2, with the data sets they used and their performances. The detailed performance results of these models are exhibited in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Cases Used** & \(\mathbf{R^{2}}\) & **RMSE** \\ \hline (a) & 4 & 0.9890 & 2.64 \\ (b) & \(1,4,7\) & 0.9877 & 2.83 \\ (c) & 4–6 & 0.9791 & 3.53 \\ (d) & 1–9 & 0.9766 & 3.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the models, with the data sets used and the performance results. \(R^{2}\) is the coefficient of determination on the test sets, and RMSE is the root mean square error on the test sets.  
Figure 8: Performances of the prediction models with different data sets. Each red points represents the model prediction value and corresponding true value of a data, the abscissa represents the true value, and the ordinate represents the predicted value. Black line is the reference line where the prediction values and corresponding true values are equal, respectively. (**a**) performance of Model (a); (**b**) performance of Model (b); (**c**) performance of Model (c); (**d**) performance of Model (d).  
According to Figure 8, the prediction performance of each model is very good. Although the prediction accuracy of the model has decreased to a certain extent with the gradual diversification of the swirl flow conditions, it still remains at a good level. In particular, Model (d) can still have good prediction ability with unified modeling of multiple complex working conditions, indicating that it has excellent generalization performance. In addition, the model has a clear degree of differentiation near the blowout state transition. In the case of being far from the blowout state transition, the positive prediction result tends to be conservative, that is, it will be predicted lower than it actually is. This makes the model itself have a considerable prediction margin, which can reduce or even eliminate the artificial design margin in the process of applying the model.
## 4 Experimental Results
### Qualitative Evaluation  
The qualitative evaluation proved that our predicted masks are notably close to the ground-truth masks, as shown in Figures 13 and 14 below, which display four sample images from the test set for the lung segmentation task and infection segmentation task, respectively.  
Figure 12: Fourfold obtained dice for different chosen thresholds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dice Coefficient** & **IoU** & **Precision** & **Recall** & **AUROC** & **AUPRC** \\ \hline
0.91 & 0.85 & 0.92 & 0.90 & 0.95 & 0.91 \\ \hline \end{tabular}
\end{table}
Table 5: Fourfold cross-validation results of the Infection segmentation model calculated as the mean of folds.  
Figure 13: Visual qualitative comparison of the lungs segmentation results between the ground truth and our proposed model on four slices from different CT scans. First column: original CT scan. Second column: ground truth. Third column: predicted lung masks.