esa-sceva/satcom-chunk-collection huggingface dataset:
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 2 Study Site and Dataset Description
### Dataset Description
#### 2.2.3 In Situ Measurements  
In situ measurements of SSM were performed only in wheat and grassland reference plots. For maize reference plots, only the irrigation practices were recorded. For grassland and wheat plots, the SSM was measured in the top 5 cm of soil by means of a TDR (Time Domain Reflectometry) probe well calibrated using gravimetric SSM measurements. For each reference plot, twenty-five to thirty measurements of volumetric SSM well distributed in the plot, were performed within a 2-hour window of the ALOS-2 and S1 acquisition time. All SSM measurements within each plot were averaged to provide a mean value for that plot. The range of SSM values was 7.0 to 36.3 vol.%.  
The soil roughness parameter defined by the root mean square surface height (_Hrms_) was not directly measured. However, a qualitative observation on roughness was made for each plot at each field campaign performed the same day of SAR acquisition. Roughness qualitative observations were sorted into three classes: smooth areas (sowing) with _Hrms_ less than 1 cm, medium roughness areas (slightly plowed soil) with _Hrms_ between 1 cm and 2 cm, and rough areas (well plowed soil) with _Hrms_ higher than 2 cm. The roughness value attributed for each reference plot at each field campaign is certainly approximate, but our experience in collecting roughness measurements over the past 20 years let's assume that this attributed value is fairly reliable.  
For grasslands and wheat plots, two datasets were built: one for the L-band and one for the C-band (177 elements in the L-band and 335 elements in the C-band). In both datasets, each element represents a reference plot at a given SAR acquisition date with the associated mean SSM value, SAR information (mean of incidence angle and mean of backscattering coefficients), and mean NDVI-value. The majority of elements have roughness values between 1 and 2 cm. For data in L-band, 12 elements of the dataset have roughness values lower than 1 cm, 156 elements have roughness values between 1 and 2 cm, and 9 elements have roughness values higher than 2 cm. For data in the C-band, 16 elements of the dataset have roughness less than 1 cm, 268 elements have roughness between 1 and 2 cm, and 51 elements have roughness higher than 2 cm. For maize plots, the dataset contains only rainfall records and SAR data in the C-band, since the swath of SAR ALOS-2 images does not cover our maize reference plots.
# Prediction of Structural Damage Trends Based on the Integration of LSTM and SVR
## 2 Theoretical Basis for Structural Health Damage Trend Prediction
### Support Vector Regression (SVR)  
SVR is a prediction model based on the principle of structural risk minimization with a good learning ability for small samples [36]. SVR uses a kernel function to map input variables into high-dimensional space, thereby transforming low-dimensional nonlinear problems into linear regression in high-dimensional space [37] and then predicting the data trend. The insensitive function SVR (\(\epsilon\)-SVR) is introduced below.  
For certain training samples \((x_{i},y_{i})\), \(x\in R^{d}\), \(y_{i}\in R\), and \(i=1,\ldots,n\). The goal of SVR linear regression is to solve the following regression function:  
\[f(x)=w\cdot x+b \tag{10}\]  
Figure 1: Schematic diagram of LSTM.  
where \(w\in R\), \(b\in R\), and \((w\cdot x)\) are the dot product of \(w\) and \(x\) and meet the principle of structural risk minimization, namely:  
\[Q(w)=\frac{1}{2}(w\cdot w)+CR_{emp}(f) \tag{11}\]  
where \(C\) refers to the regression error penalty factor, and \(R_{emp}(f)\) refers to the loss function.  
Most of the main structural damages are caused by the stiffness decrease from the perspective of general structural damage characterization. When the structure has different degrees of stiffness damage, the system output acceleration is used to identify the gradual damage of the stiffness changes. The LSTM-SVR is used as the structure health prediction model in this paper. Since the instantaneous frequency can effectively reflect the health status changes in the structure [38], the model is divided into signal processing, data preprocessing, and LSTM-SVR network prediction.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 4 Comparison of Forest AGB Maps over Large Regions
### Tropical Africa  
The [PERSON] et al. [99] and [PERSON] et al. [100] datasets had higher AGB than the other seven datasets shown in Figure 10 over tropical African forests, which was in agreement with comparable results in Southeast Asia. The majority of pixels had AGB \(>280\) Mg/ha, and few pixels had AGB \(<180\) Mg/ha, probably because both datasets used the MODIS land cover types to separate forest and non-forest, and the regions covered with woody savannas and savannas were masked (Figures 10 and 11). The [PERSON] et al. [105] dataset differed from others in AGB spatial and frequency distributions over tropical Africa. In the regions with higher AGB, the [PERSON] et al. [89] and [PERSON] et al. [105] datasets had lower AGB compared to the other datasets (Figure 10). The [PERSON] et al. [19] and [PERSON] et al. [18] datasets were both spatially and statistically similar, while the [PERSON] and [PERSON] [77] dataset, although somewhat similar, had higher AGB in the high AGB regions. In accordance with [PERSON] and [PERSON] [77], [PERSON] et al. [99], and [PERSON] et al. [100], the [PERSON] et al. [103] dataset had higher AGB in the evergreen broadleaf forests (Figures 10 and 12). However, as most of the tropical regions were covered with woody savannas and savannas and not evergreen broadleaf forests, the percentage curve (Figure 11) in [PERSON] et al. [103] showed that most pixels had AGB \(<50\) Mg/ha due to lower AGB estimates for regions covered with woody savannas and savannas.  
Figure 8: Frequency distribution of AGB with 10 Mg/ha bins in eight maps covering Southeast Asia.  
Figure 9: The average value of the fuzzy numerical index in eight forest biomass maps of evergreen broadleaf forest, the mixed forest, and woody savannas over Southeast Asia.  
Statistical analysis of the spatial similarities and differences of these AGB datasets in different forest types showed that the nine AGB datasets in Figure 10 were spatially similar in evergreen broadleaf forests, but somewhat dissimilar in regions covered with woody savannas and savannas (Figure 12). In particular, the [PERSON] et al. [99] and [PERSON] et al. [100] datasets were quite different to other datasets in the woody savannas and savannas.  
Figure 11: Frequency distribution of AGB with 10 Mg/ha bins in nine maps covering tropical Africa.  
Figure 10: Remote sensing-based forest biomass maps across tropical Africa.
# Multi-Objective Robust Optimization Reconstruction Algorithm for Electrical Capacitance Tomography
## 3 Multi-Objective Robust Optimization Imaging Model
### Imaging Model  
The inverse problem in the measurement technology solves the unknown permittivity distribution, \(\mathbf{u}\), based on the measured capacitance, \(\mathbf{b}\), and the sensitivity matrix, \(\mathbf{A}\). We can use the following equation to formulate this process [3]:  
\[\mathbf{A}\mathbf{u}=\mathbf{b}+\mathbf{\varepsilon} \tag{1}\]  
where \(\mathbf{\varepsilon}\) defines the noise vector; \(\mathbf{A}\) is a matrix with a size of \(m\times n\); and \(\mathbf{u}\), \(\mathbf{b}\), and \(\mathbf{\varepsilon}\) are vectors with sizes of \(n\times 1\), \(m\times 1\), and \(m\times 1\), respectively.  
To achieve a stable and physically meaningful solution for the ill-posed inverse problem formulated in Equation (1), the incorporation of prior knowledge becomes a critical necessity in computational mathematics. This requirement stems from the inherent non-uniqueness and sensitivity to measurement errors that characterize such inverse problems. Among various methodologies, the regularization technique emerges as a mathematically rigorous framework that systematically integrates prior constraints through the construction of a well-posed optimization paradigm. The generalized formulation can be defined by the following:  
\[\min_{\mathbf{u}}\left\{f(\mathbf{Au},\mathbf{b})+\sum_{j=1}^{\gamma}\alpha_{j}\text{Reg}_{j}( \mathbf{u})\right\} \tag{2}\]  
where \(\text{Reg}_{j}(\mathbf{u})\) represents the regularization term; \(f(\mathbf{u},\mathbf{b})\) is the data fidelity metric; \(\alpha_{j}>0\) is the regularization parameter; and \(\gamma\) defines the number of regularizers.  
As a single-objective optimization problem, Equation (2) demonstrates strong interpretability and scalability, making it particularly suitable for bridging model-based approaches with learning-based algorithms. The advantages of this model lie not only in its intuitive mathematical structure but also in providing a flexible framework that can be adjusted and extended according to specific needs. In practical applications, the single-objective optimization model can be adapted to diverse problems by introducing different data fidelity terms and regularization terms, thereby enhancing its applicability. Furthermore, this model can be effectively solved by utilizing existing algorithms.
## 2 Optical Hyperspectral Image Cryptosystem  
This section will discuss the whole optical hyperspectral image cryptosystem in detail first.  
The flowchart of the optical signature and authentication cryptosystem by using the triangular association encryption algorithm (TAEA) and 3D Arnold transform in the Gyrator domains is illustrated in Figure 1. The intact encryption approach is completed by using 3D Arnold mapping, triangular association, Gyrator transform, and integrity authentication, respectively. The specific process is as follows:  
1. The image as the signature formed by the identity information of the sender 'Lena' or another one is attached to the hyperspectral data as the \((N+1)\)th layer;
2. Set the side length to be equal to N in the block mobile method of 3D Arnold. Except the \((N+1)\)th layer, the N-layer hyperspectral data are scrambled by using the block mobile method of 3D Arnold (see Figure 1);
3. Except the \(N+1\)th layer, the N-layer hyperspectral data are encrypted by using the triangular association encryption algorithm (see (4)-(7));
4. Then perform the Gyrator transform for the first N layers;
5. Get the encrypted image with the signature. The signature is still the original identity image, which will not be transmitted to the receiver directly. The signature information is communicated in advance and is known only to the sender and receiver;
6. Take real integers from the first N layers. Let the side length be equal to N + 1 in the block mobile method of 3D Arnold. The N + 1 layers hyperspectral data are performed, using the block mobile method of 3D Arnold (see Figure 1). Then, the algorithm of image integrity authentication is performed for every layer (see (8));
7. Add the single-layer secret information obtained in step-6 to the first N layers encryption information obtained in step-5 to form the ciphertext of N + 1 layers;
8. The encrypted image information of N + 1 layers obtained in step-7, which has the function of signature and integrity authentication, can be transmitted to the recipient.  
Figure 1: The flowchart of the hyperspectral image encryption algorithm.  
Next, the 3D Arnold map and block mobile method, triangular association encryption algorithm, image signature, image integrity authentication and Gyrator transform are briefly introduced in this section.
###### Abstract  
Wireless devices, such as smartphones, tablets, and laptops, are intended to be used in the vicinity of the human body. When an antenna is placed close to a lossy medium, near-field interactions may modify the electromagnetic field distribution. Here, we analyze analytically and numerically the impact of antenna/human body interactions on the transmitted power density (TPD) at 60 GHz using a skin-equivalent model. To this end, several scenarios of increasing complexity are considered: plane-wave illumination, equivalent source, and patch antenna arrays. Our results demonstrate that, for all considered scenarios, the presence of the body in the vicinity of a source results in an increase in the average TPD. The local TPD enhancement due to the body presence close to a patch antenna array reaches 95.5% for an adult (dry skin). The variations are higher for wet skin (up to 98.25%) and for children (up to 103.3%). Both absolute value and spatial distribution of TPD are altered by the antenna/body coupling. These results suggest that the exact distribution of TPD cannot be retrieved from measurements of the incident power density in free-space in absence of the body. Therefore, for accurate measurements of the absorbed and epithelial power density (metrics used as the main dosimetric quantities at frequencies \(>\) 6 GHz), it is important to perform measurements under conditions where the wireless device under test is perturbed in the same way as by the presence of the human body in realistic use case scenarios.  
exposure assessment; millimeter waves; antennas; near-field interactions; dosimetry; power density +
Footnote †: journal: Applied Physics Letters  
_Applied Physics Letters_
# Feature-Attended Federated LSTM for Anomaly Detection in the Financial Internet of Things +
## 5 Detail of Correlation-Based Federated Learning
### Aggregation Optimization
#### 5.2.1 Feature Extraction  
In this stage, the information entropy using \(t\) as a constant interval is used for extracting traffic features. In this paper, source IP address and destination IP address are used as examples of entropy calculation since the historical financial behavior of IP devices and the financial interactions between them are crucial. While traffic characteristics can be selected according to specific requirements in practical financial applications, the specific values of \(t\) and \(T\) should be determined based on the actual network situation.  
The total flow of the edge server \(e_{j}\) in a unit time is expressed as \(N_{j}^{flow}(N_{j}^{flow}\in N_{+})\), and \(e_{j}\) has \(N_{j}^{src}(N_{j}^{src}\in N_{+})\) sorts of source IP address and \(N_{j}^{dst}(N_{j}^{dst}\in N_{+})\) sorts of destination IP address. While the source IP address is expressed as a random variable \(Y\), the number of occurrences of a certain source IP address could be expressed as \(y_{u}(u\in N_{+})\). Then, the information entropy of edge server \(e_{j}\), which is recorded as \(b_{e_{j}}^{i}(src)\), \((i\leq T,i\in N_{+})\) could be calculated according to Formula (14).  
\[b_{e_{j}}^{i}(src)=\sum_{u=1}^{N_{j}^{src}}\frac{y_{u}}{N_{j}^{flow}}\log_{2} \frac{y_{u}}{N_{j}^{flow}} \tag{14}\]  
In the same way, the destination IP address could be expressed as a random variable \(Z\), and the number of occurrences of a certain destination IP address could be expressed as \(z_{u}\). Then, the information entropy of edge server \(e_{j}\), which is recorded as \(b_{e_{j}}^{i}(dst)\), could be calculated according to Formula (15).  
\[b_{e_{j}}^{i}(dst)=\sum_{u=1}^{N_{j}^{dst}}\frac{z_{u}}{N_{j}^{flow}}\log_{2} \frac{z_{u}}{N_{j}^{flow}} \tag{15}\]  
Then, the feature values \(b_{e_{j}}^{i}\) of the edge server \(e_{j}\) should be calculated according to Formula (16), in order to retrain the trend of entropy change.  
\[b_{e_{j}}^{i}=\left|b_{e_{j}}^{i}(src)-b_{e_{j}}^{i}(dst)\right| \tag{16}\]  
Finally, the center server \(C\) could calculate the feature value in the \(i\)-th unit time at a global vision according to Formula (17).  
\[b_{C}^{i}=\frac{1}{n}\sum_{j=1}^{n}b_{e_{j}}^{i} \tag{17}\]
## 4 Case Study  
### Project Overview  
This study took the underground space utilization project of Nanning rail transit Line 5 (Guangxi University station to Guangxi University of Finance and Economics) as the case study for validating the developed risk coupling analysis method based on the dynamic Bayesian network and N-K model. In order to establish a dynamic Bayesiannetwork, a large dataset was collected during the excavation of the DFP adjacent to the existing underpass tunnel. The total length of the case's main structure is 811.2 m, the standard width of the main pit is 28.8 m, and the pit's depth is 8.6-10.5 m. The main pit enclosure structure of the project adopted the support form of \(\phi\)800 bored piles with internal support, and \(\phi\)600 double pipe rotary piles were used to prevent water between the piles. The foundation pit support system adopted two supports, as shown in Figure 6. The surrounding facilities of the project are extremely complicated, and the project is located above the interval between Guangxi University Station and Xiuling Road Station of Line 5. The clear vertical distance is 5-8.7 m. The soil at the pit bottom is reinforced before excavation to reduce the pit uplift and tunnel uplift and strengthen the monitoring in the pit and tunnel. Various monitoring items and the distribution of monitoring points are shown in Figure 7. The geological and enclosure structure profile is shown in Figure 8. Notably, pore water represents the majority of the groundwater in the site, which is stored in the compressive conglomerate layer. In addition, buildings such as Nanning Vocational and Technical College, Guangxi Finance and Economics College Teaching Building, and Construction Bank Mingxiu West Road Branch are adjacent to the project pit. The overview of the surrounding buildings and environment is shown in Figure 9.  
Figure 8: Geological section of the envelop enclosure of the comprehensive underground utilization project of Guangxi University of Finance and Economics.  
Figure 9: The surrounding buildings and environment of the underground project of Guangxi University of Finance and Economics.  
The Nanning Line 5 Guangxi University Station-Xiuling Road Station interval of tunnel line is laid along Mingxiu Road, located directly below the underground space utilization project. The length of the left line of the interval is 1263.597 m, and the length of the right line is 1261.266 m. The total length is 2524.863 m. This interval tunnel burial depth is \(\sim\)9.71-18.72 m, mainly through the round gravel layer, pebble layer, and powder-fine sand layer. The project shield interval tunnel inner diameter is 5400 mm, the lining thickness is 300 mm, the strength grade is C50, and the seepage resistance grade is P12. The shield tunnel cross-section and its detection point layout are shown in Figure 10.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 3 Results
### CorvisST Airflow Pressure
#### 3.2.2 Temporal Distribution  
The duration of the airflow was 21.483 \(\pm\) 0.2881 ms, (95%CI 21.427 to 21.539 ms), whilst the maximum pressure plateau (defined as 99% quantile) lasted for 1.317 \(\pm\) 0.1116 ms (95%CI: 1.295 to 1.339 ms), Figure 6a,b. For pressures greater than 25% of the maximum, the pressure duration was highly consistent with a CoV of less than 5%, Figure 6c. The time point when the respective maximum pressure was recorded was highly consistent across the affected area (CoV 1.3%); the time point varied by 0.3 ms, Figure 6d.
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 3 Methodology
### Transfer Learning and CNN Architectures  
TL is an ML method that allows a model to use knowledge gained from one dataset/task to improve its performance on a related task [34]. This study leveraged TL to help reduce overall model training time, using the VGG-19 CNN model--trained on the ImageNet dataset with nearly 1.2 million photos and 1000 classes [35]. Figure 4 represents the VGG-19 model used in this study and its architecture.  
Figure 4: The VGG-19 architecture.  
Figure 3: Data Preprocessing Algorithm.  
For this study, the weights of the model's feature layers were frozen (not trained on). This allows for faster training convergence and effective TL. In Figure 4, the classifier layer is shown in green. The classifier layers are as follows: following the feature layers, a dense layer consisting of 4096 neurons, a ReLU activation layer, and a 50% dropout layer were added (to prevent model overfitting by randomly removing half of the neuron connections from our model during each training iteration). This group of three layers was added twice. The CNN architecture used in this study combines multiple layers of convolution, max pooling, and fully connected layers to effectively extract features. It is optimized for image classification tasks and can easily be fine-tuned for a wide variety of medical imaging tasks.
# Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
## Appendix A Additional Numerical Experiments
### Estimating Constant vs. Sinusoidal Current  
Applying a higher-amplitude constant current and a time-varying sinusoidal current both result in multiple-spike voltage data. We test the capability of the EnKF with parameter tracking in distinguishing between these two applied currents in the resulting parameter estimates given the synthetic voltage data shown in Figure A2. Data were generated using the same procedure as in Section 4.1. Note that using a constant current where \(I(t)=-10\) mA/cm\({}^{2}\) and a sinusoidal current where \(I(t)=-10\sin(0.2t)-10\) mA/cm\({}^{2}\) both results in similar, multiple-spike voltage data, with fourteen spikes over \([0,200]\) msec in the former case and thirteen in the latter.  
Figure A3 shows the resulting applied current estimates in each case, estimated using parameter tracking with \(N=100\) ensemble members and increasing parameter drift standard deviations (from top to bottom) \(\sigma_{\xi}=0.05\), \(0.5\), \(1\), and \(5\). Initial ensembles for the model states and applied current parameter were drawn from uniform prior distributions, with \(I_{0|0}^{p}\sim\mathcal{U}(-15,10)\) for each \(p=1,2,\ldots,N\). While not shown, the filter well tracks the voltage and unobserved gating variables \(n(t)\), \(m(t)\), and \(h(t)\) in each case. However, while the EnKF mean is able to well track the constant current for each of the \(\sigma_{\xi}\) values considered, the parameter tracking algorithm is unable to track the sinusoidal current if \(\sigma_{\xi}\) is too small. In particular, note that using \(\sigma_{\xi}=0.05\) results in the filter approximating the sinusoidal current as a constant, converging to about \(I(t)=-10\) mA/cm\({}^{2}\). Choosing a larger standard deviation in the parameter drift allows the algorithm more flexibility in tracking the true sinusoidal shape of the underlying current, with the EnKF mean well tracking the true current when \(\sigma_{\xi}=5\) here. In both the constant and sinusoidal cases, note that an increase in \(\sigma_{\xi}\) corresponds directly to an increase in the width of the uncertainty bounds around the EnKF mean estimate.