esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology of Monitoring  
### Intelligent Active Deviation Correction System  
The system is mainly composed both of Beidou GNSS real-time data monitoring branch system and the feedback control branch system. The monitoring system tracks GNSS satellite and collects data in real time. Moreover, the deformation of the tower is monitored and sent to the feedback control system in real time. The GNSS displacement measurement system of Beidou adopts the national geodetic coordinate system CGCS-2000, which takes east, west, north and south as the coordinate axis. The tower displacement measurement focuses on the deviation of the central axis of the tower on the north bank south.  
With Matan Hongshui He Bridge as the engineering background, the two Beidou measuring points of the tower integrating with main cable and buckle are located in two sides of the transverse bridge. The overall layout of the lifting construction system is shown in Figure 3 below. The space deformation of cable tower (such as reverse) is measured.
## Theory and Methods
### XGboost  
XGBoost is an improved algorithm based on the gradient-enhanced decision tree, which can effectively construct enhanced trees and run parallel computing. Compared with the traditional GBDT (gradient boosting decision tree) algorithm that only uses the first-order derivative information, the XGBoost performs the second-order Taylor expansion on the loss function and provides higher efficiency of solving the optimal solution [44].  
Figure 2: The flowchart of global navigation satellite system-reflectometry (GNSS-R) soil moisture retrieval procedure.  
The flowchart of the XGBoost algorithm [42] is summarized in Figure 3.  
Advantages of XGBoost [44]:  
1. Using the second-order Taylor expression to approximate the objective function, making it easier to find the optimal solution;
2. It can handle sparse and missing data;
3. Generating a decision tree using the structural score;
4. The split node uses the candidate set so that the algorithm runs fast;
5. Define the complexity of the tree and apply it to the objective function to grasp the complexity of the model;
6. Over-fitting can be prevented by samplings of column features.  
The XGBoost also has some disadvantages: The complexity is slightly higher for using XGB to do the feature importance sorting because XGB uses level-wise to generate decision trees. It splits the  
Figure 3: The flowchart of XGBoost algorithm.  
leaves of the same layer at the same time, thus performing multi-thread optimization, which can avoid overfitting. The traversal selects the optimal segmentation point. When the amount of data is large, the method is time-consuming [57; 58].
## 3 Proposed Methodology
### Federated Classification Method with ResVGG-SwinNet
#### 3.7.2 Federated-Learning Setup  
Federated learning trains the ResVGG-SwinNet model using local datasets on each client. Federated learning protects data while distributing training. The clients train their data locally, and only the model changes (weights) are shared with a central server, which aggregates the updates and distributes the new global model to the clients.  
Figure 2: FL- based ResVGG-SwinNet classification model architecture.  
Each client, \(k\), trains its model on its subset of data, \(\mathcal{D}_{k}\). The client calculates the gradient of the loss function, \(\mathcal{L}\), about its data to update its local model weights, \(\xi_{k}\)  
\[\xi_{k}^{t+1}=\xi_{k}^{t}-\gamma\
abla_{\xi_{k}}\mathcal{L}(\mathcal{D}_{k},\xi_ {k}) \tag{20}\]  
\(\xi_{k}^{t}\) represents model weights at iteration \(t\), \(\gamma\) represents learning rate, and \(\
abla_{\xi_{k}}\mathcal{L}(\mathcal{D}_{k},\xi_{k})\) represents loss function gradient based on client data.  
Federation Averaging (FedAvg): After local training, clients communicate their updated model weights to the central server. The central server uses Federated Averaging (FedAvg) to aggregate model changes and update the global model by averaging all client models:  
\[\xi^{t+1}=\frac{1}{N}\sum_{k=1}^{N}\xi_{k}^{t+1} \tag{21}\]  
where \(N\) represents client count. The aggregated model, \(\zeta^{t+1}\), is returned to the clients, and the process continues until convergence.  
Global Model Update: The global model, \(\zeta^{\text{final}}\), is produced by numerous rounds of local updates and aggregations. The final model predicts and classifies, with output for each label, \(y_{l}\), calculated using:  
\[y_{l}=\text{softmax}(\mathcal{F}_{\text{S}}(\mathcal{F}_{\text{V}}(\mathcal{F }_{\text{R}}(z)))) \tag{22}\]  
Softmax computes the probability distribution across all classes, enabling the model to categorise network traffic as usual or a particular DDoS attack.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Comparison Study  
Table 4 compares the cadmium and methylene blue adsorption capacity of CHS-500 and CHS-700 with other adsorbents converted to carbonaceous materials. The adsorption capacities of CHS-500 and CHS-700 are amongst the highest reported values. The results of CHS-500 and CHS-700 suggest that carbonized hemp is a promising material for water purification, with some of the advantages of hemp seeds including that there are several functional groups that enhance adsorption capacity and that they are inexpensive. From Table 4, it can be seen that hemp seeds performed better than other sorption materials.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multirow{2}{*}{**Temperature (K)**} & \multicolumn{6}{c}{**Thermodynamic Parameters**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**AH\({}^{\circ}\) (KJ/mol)**} & \multicolumn{2}{c}{**\(\Delta\)S\({}^{\circ}\) (KJ/mol)**} & \multicolumn{2}{c}{**\(\Delta\)G\({}^{\circ}\) (J/mol.K)**} \\ \cline{3-8}  & & **Cd(II)** & **MB** & **Cd(II)** & **MB** & **Cd(II)** & **MB** \\ \hline PHS & 288 & & & & \(-10.59\) & \(-8.462\) \\  & 298 & 39.60 & 35.22 & 0.1460 & 0.7814 & \(-9.540\) & \(-7.360\) \\  & 398 & & & & & \(-8.481\) & \(-6.329\) \\ \hline CHS-500 & 288 & & & & & \(-5.349\) & \(-4.729\) \\  & 298 & 19.79 & 20.03 & 0.1219 & 0.5382 & \(-3.794\) & \(-3.082\) \\  & 308 & & & & & \(-2.506\) & \(-2.184\) \\ \hline CHS-700 & 288 & & & & & \(-4.930\) & \(-4.190\) \\  & 298 & 15.75 & 13.66 & 0.1070 & 0.3921 & \(-2.895\) & \(-2.905\) \\  & 308 & & & & & \(-1.576\) & \(-1.102\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Thermodynamic parameters for the adsorption of Cd(II) and MB.  
Figure 7: Illustration of the possible interactions between the adsorbents and pollutants.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Cd(II)** & **References** \\ \cline{2-3}
**adsorbents** & **q\({}_{\text{(max)}}\) (mg/g)** & \\ \hline MgO-AC & 649.9 & [49] \\ Oil palm shell & 227.3 & [50] \\ Nutshells & 142.8 & [51] \\ Geopolymer & 67.6 & [52] \\ MnO\({}_{2}\) carbon nanotubes & 41.6 & [53] \\ CHS-700 & 35.08 & This study \\ CHS-500 & 31.12 & This study \\ Cornstalk & 33.8 & [54] \\ _Ola europan_ L. & 24.8 & [55] \\ Black cumin seeds & 13.6 & [33] \\ Buffalo weed & 11.5 & [56] \\ Chicken feathers & 7.8 & [57] \\ Natural clay & 2.50 & [58] \\ \hline  & MB & \\ \cline{2-3} Adsorbents & **q\({}_{\text{(max)}}\) (mg/g)** & \\ \hline Corn cob & 333.0 & [59] \\ Pepper stem & 178.4 & [60] \\ Orange peels & 98.9 & [61] \\ _Cornairnnt sativum_ & 94.9 & [62] \\ D-Tructose & 83.0 & [63] \\ CHS-700 & 53.98 & This study \\ CHS-500 & 49.89 & This study \\ Orange and lemon peels & 38.0 & [64] \\ Lignin and chitosan & 36.0 & [65] \\ Nano-activated carbon & 28.1 & [48] \\ Rice husk waste & 27.8 & [66] \\ Black cumin seeds & 16.8 & [46] \\ Peaunt shell waste & 15.4 & [67] \\ Grape wood & 4.8 & [68] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison study of carbon-based materials for the adsorption of methylene blue and cadmium.
## 2 Sensors
### Light Detection and Ranging (LiDAR)
#### 2.4.2 LiDAR Camera Calibration  
Two main strategies exist to find relative orientations and positions of a LiDAR with respect to a camera. The first strategy relies on using targets that could be extracted from LiDAR point clouds and images. The second approach relies on optimizing the time-dependent data from both sources by automatic registration schemes, e.g., a photogrammetric point cloud could be registered on a LiDAR point cloud by employing point cloud registration methods [105]. Alternatively, a photo-realistic presentation of the point cloud could be used to generate an artificial synthetic image [106]. Image registration methods [107] could be subsequently applied to find common points between images and LiDAR point clouds.
# Shipborne GNSS-Determined Sea Surface Heights Using Geoid Model and Realistic Dynamic Topography
## 2 Theoretical Principles
Derivation of offshore DT is further discussed in Section 4, with accompanying data processing examples. Similarly, Section 5.2 further examines the low-pass filtering of the residual values. The above computations result in filtered residual values \(r^{F}\), which should be corrected for vessel sailing-related effects, such as squat and static draft:  
\[r^{F+C}(\varphi,\lambda,t)=r^{F}(\varphi,\lambda,t)-C(t), \tag{5}\]  
where the term \(C\) denotes the vessel sailing-related corrections collectively. Section 5.3 presents an example of their empirical derivation. Ideally, the resulting filtered and corrected residuals \(r^{F+C}\) should be near-zero, but such results cannot be expected, due to measurement errors and deficiencies in the used models. Thus, the residual values provide means for validation. Assuming that the estimated DT, geoid model, and vessel sailing-related corrections are accurate, the residuals primarily represent errors of the GNSS-determined SSH. Finally, the corrected SSH can be restored from the filtered and corrected residuals:  
\[SSH^{F+C}(\varphi,\lambda,t)=r^{F+C}(\varphi,\lambda,t)+\hat{DT}(\varphi, \lambda,t)+N(\varphi,\lambda), \tag{6}\]  
whereby these results are now suitable for further SSH data applications.
## 3 Materials and Methods
### Data Analysis Approach
#### 3.5.4 Feature Selection  
Feature selection was performed to improve the model's ability to generalize to new data and prevent overfitting [85]. Subsets of features were generated based on the insights gained from correlation analysis and VIF calculations, and then utilized for further model development. For tree-based models such as Random Forests (scikit-learn, version 1.2.1) and XGBoost (version 1.7.6), an inherent feature selection mechanism is embedded in the training process. For the Support Vector Machine (SVM) (scikit-learn, version 1.2.1), a more refined dataset, produced after the VIF calculation, was employed for model training.
# Buckling Test of Composite Cylindrical Shells with Large Radius Thickness Ratio
###### Abstract  
A buckling test of composite cylindrical shells with a radius-thickness ratio (\(r/t\)) = 893 under axial compression was conducted to investigate the effects of the radius-thickness ratio (\(r/t\)). It is known that the buckling load of cylinders shows large differences and scatter between theory and experiment. The ratio of the experimental buckling load and theoretical buckling load is called the knockdown factor (KDF). Many investigations have been conducted to find the cause of the degradation and scatter in the KDF, but as yet, no cause has been found. In 1968, NASA/s buckling design criterion, NASA SP-8007, gave an empirical KDF curve that decreased with the increasing \(r/t\) (up to 2000) for metal cylinders. The same curve has been applied to composite cylinders. Recently, [PERSON] derived a flat lower-bound KDF in terms of A- and B-basis values (99% and 90% probability with a 95% confidence level) through a statistical analysis of experimental buckling loads. The result, however, based on experimental results up to \(r/t=500\) and, thus, the dependency on a large range of \(r/t\), is not clear. Thus, the authors focused on a larger range of \(r/t\). Cylindrical shells made from carbon fiber-reinforced plastic (CFRP) were tested. The nominal radius, thickness, and length were \(r=100.118\) mm, \(t=0.118\) mm, and \(L=200\) mm and, thus, the \(r/t=848\) and length-to-radius ratio (\(L/r\)) = 2.0. Shape imperfections were also measured by using in-house laser displacement equipment. The buckling load was slightly affected by the \(r/t\), but the reduction in the KDF was insignificant.  
buckling; shell; knockdown factor +
Footnote †: journal: Applied Sciences
# Geophysical Study of a Large Landslide Affecting the Urban Area of Albuuuelas (S Spain)
## 5 Results  
### Geophysical Results at the Borehole Site  
In this study, the information provided by the borehole drill has been used as starting point for the estimation of the _Vs_ profile at the center of the landslide. According to the stratigraphy of the place, three main different layers of materials can be considered: silt and conglomerates, sandstone and red silt. In addition, a surface layer of alteration and/or accumulated fills are also observed.  
At the borehole site, H/V and array measurements were taken in order to combine the analysis of the seismic passive techniques with the geotechnical information and assess the reliability of the obtained results. According to that, the joint-inversion of the H/V and dispersion curves has been accomplished considering the number of layers and the ranges of thickness deduced by the stratigraphic information obtained from the borehole drill.  
The Dinver software of the Sesarray package (www.geopsy.org) has been used for carrying out the joint-inversion and estimating the corresponding _Vs_ profile at the borehole site.  
In Figure 4a, the _Vs_ profile estimated from the joint-inversion of H/V and array measurements at the borehole site is presented. The model with the minimum misfit (black), the models below the minimum misfit + 10% (dark gray) as well as all the tested models (light gray) are shown in the left column. The ellipticity and the dispersion curves corresponding to the best estimate model (darkest gray), the models below the minimum misfit + 10% (dark gray) and all the analyzed models (light gray), are shown together with the experimental curves (black) on the right side.  
In Figure 4b,c, the stratigraphic information obtained from the borehole drill is compared to the best _Vs_ profile (i.e., the model with the minimum misfit) estimated at the first 60 m.  
As a result, a shallow layer of fills is estimated with an S-wave velocity of 160 m/s and an approximately thickness of 2 m. Below, a first layer, associated with the silt and conglomerate materials, is characterized by a _Vs_ of 280 m/s and thickness of 4 m. The second estimated layer reaches a depth of 15 m, with a S-wave velocity of 520 m/s. This layer is associated with the sandstone materials. Finally, the third layer presents a _Vs_ of 750 m/s and reaches a maximum depth of 52 m, corresponding with the red silt materials.  
The obtained results show that the model with the minimum misfit correctly identifies the different layers. The velocity values estimated for each layer are in agreement with the properties of the materials observed in the stratigraphy column. In the case of the thicknesses, it is observed that a slight underestimation of 3-5 m in the most superficial layers, which falls within the margin of the minimum misfit + 10% observed in Figure 4a.
## 2 WSN Basic Concepts and Preliminaries
### WSN Basic Concepts  
We consider the perception model of a sensor, as shown in Figure 2, where two circles are surrounding the sensor; the first one is the sensing area with R\({}_{\text{S}}\) as the disk radius. R\({}_{\text{T}}\) is the radius of the communication range. We consider R\({}_{\text{T}}=2\times\) R\({}_{\text{S}}\), it is proven that if the communication range is at least twice the sensing range, it's a sufficient condition to ensure that a full coverage of a convex area implies connectivity among active nodes [17]. However, this assumption of omni-directional sensingability does not hold true for some types of sensor nodes, such as cameras, ultrasonic sensors, etc., which have a directional sensing region [8]. Figure 3 shows that if the circular area of the sensing range is a square, the diameter of the circle becomes the diagonal of the square.  
Sensors get charged up by batteries, thus they can be used for a limited period. Hence, to make the battery work longer, energy conservation and coverage protocols are required [19]. The event detection probability is inversely proportional to the Euclidian distance separating the sensor from the event. There are two sensor detection models in WSN to find effective coverage. The binary detection model which assumes that there is no uncertainty, and the probabilistic sensing model with stochastic detection [6; 20]. A probabilistic coverage better complies with the performance of real sensors in the environment [8]. The binary sensing model is the simplest and most widely analyzed model of coverage provided by a sensor [21; 22]. In this model, a sensor detects all events within its sensing range. In this paper, we use the binary detection model shown by Equation (1):  
\[\mathrm{P_{d}}\left(\mathrm{P_{s^{\prime}}},\mathrm{P_{e}}\right)=\ \left\{\begin{array}{l}1\ \mathrm{if}\ \| \mathrm{P_{e}}-\mathrm{P_{s}}\|<\mathrm{R_{S}}\\ \\ 0\ \mathrm{if}\ \mathrm{R_{S}}<\|\mathrm{P_{e}}-\mathrm{P_{s}}\|\end{array}\right. \tag{1}\]  
where: \(\mathrm{P_{d}}\) is the detection probability of an event occurred at position \(\mathrm{p_{e^{\prime}}}\) and detected within the sensing range of a sensor placed at location \(\mathrm{p_{s}}\). \(\|\mathrm{P_{e}}-\mathrm{P_{s}}\|\) is the Euclidian distance between the sensor and the event.  
For simplicity, and as considered in many related works such as [7; 9; 10; 11], we assume omni-directional sensing ability of the sensor, and a binary detection model. The choice of the sensor detection model does not limit the applicability of the proposed approach. The detection model is simply an input parameter to the proposed placement algorithm.
## 3 Tapped Delay Line Model
### A2G TDL Model
#### 3.2.3 Doppler Spectrum per Tap  
The individual channel tap corresponds to the superposition of several physical propagation paths that arrive at the receiver within one delay bin. Therefore, each channel tap experiences the \"narrowband\" characteristics, among which the Doppler spectrum of each tap is one important feature in the modeling. The Doppler spectrum of each tap maybe time variant or invariant. The Doppler spectrum strongly depends on the propagation environment, as well as the tap delay.  
The Doppler spectrum of individual channel taps can be modeled by accurate calculation based on geometry of transmitter, receiver and scatterers. The authors of [37; 38] present a theoretical analysis on Doppler PDF to describe any time-variant, single-bounce, mobile-to-mobile scattering channel. In such kinds of approaches, the scatterer plane and the antennas velocities are considered in order to derive the joint Doppler PDF. In contrast, the simpler statistical approach to model Doppler spectrum of individual channel tap is to use specific function that fits the best to the observed Doppler spectrum. The typical Doppler spectrum function of the tap includes the flat, Jake, Bell, and Gaussian spectra. The Jake spectrum, also known as the U-shape spectrum, is well recognized as a result of isotropic and uniformly distributed scatters. In contrast, the Bell and Gaussian spectra are typically for non-isotropic scatters that are closely located, resulting in dense multipath propagation with similar propagation delays and angles of arrival. The analytical expressions of all simulated Doppler spectra and the used model selection criteria are presented in Appendix A.
# Generation of Monoclonal Antibodies for Sensitive Detection of Pro-Inflammatory Protein S100A9
## 2 Materials and Methods
### Confirmation of the Antigen Binding Efficiency of Purified Monoclonal Antibodies  
The antigen-binding activities of the five purified monoclonal antibodies were tested using indirect ELISA as follows: 100 \(\upmu\)L of 250 ng of commercial hS100A9 (Sino Biological) in PBS was immobilized on a 96-well plate for 37 h at 2 \({}^{\circ}\)C. After that, the wells were filled with 200 \(\upmu\)L of TBST containing 2% BSA for 1 h at 37 \({}^{\circ}\)C and then washed three times with TBST. Subsequently, 150 ng of purified monoclonal antibody in 100 \(\upmu\)L of PBS with 0.1% BSA was added and incubated for 1 h at 25 \({}^{\circ}\)C. After washing three times with 200 \(\upmu\)L of TBST, bound protein was probed with 5000-fold diluted HRP-conjugated goat anti-mouse IgG antibody in PBS with 0.5% BSA for 1 h at 25 \({}^{\circ}\)C. The wells were washed three times with 200 \(\upmu\)L of TBST and developed with 100 \(\upmu\)L of TMB solution. After incubation for 10 min, the reaction was stopped with 50 \(\upmu\)L of 1 N sulphuric acid, and the absorbance was read at 450 nm using a microplate reader.  
Afterwards, an isotyping test was performed using a Rapid ELISA mouse mAb isotyping kit (Pierce), and the cells were frozen in liquid nitrogen in FBS containing 10% of DMSO. 5B3, 7C8, 9D9: H chain-IgG2b, L chain-kappa; 14C4, 22A10: H chain-IgG1, L chain-kappa.
## 5 AGEI Application to a Case Study
### AGEI Procedure Calculation for Configuration A
#### 5.1.2 Energy to Provide Domestic Water Heating and Air Cooling and AGEI Calculation  
The second useful effect of Configuration A is the \"domestic water heating\", for two residential buildings and one communal kitchen. This heating is achieved using, respectively, 48 existing electrical boilers and 1 existing LGP boiler. The third useful effect of Configuration A is the \"indoor climate control\", obtained by cooling and dehumidifying an outdoor air flux of 12,000 m\({}^{3}\)/h throughout the entire year. This is achieved using an existing ATU. Detailed specifications for these systems are provided in Table 10.  
The calculation of the monthly average AGEI\({}_{\text{A}}\) is carried out by applying (16), as shown below, and the resulting value is the same for each month.  
\[AGEI_{A} = \frac{n}{m}\sum_{j=1}^{m}\sum_{i=1}^{u_{j}}\frac{E_{ij}}{f_{i}}= \frac{3}{51}\cdot\left(\frac{48\cdot 0.95}{2.13}+\frac{0.70}{1.07}+\frac{0.50}{1 }+\frac{3.50}{2.13}\right)=1.43\]
## 4 Results and Discussion
### Experimental Results of 1D Raw Data
#### 4.2.2 Results for Deep Learning Models with 1D Raw Data  
Deep learning models were utilized on the raw data, with the results summarized in Table 5. The deep learning approaches yielded encouraging outcomes. Among the models, the CNN showed the best performance, reaching 84.91% accuracy. MobileNet and EfficientNetB4 obtained accuracies of 82.08% and 83.02%, respectively.