esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate Sybil Attack Detection Based on Fine-Grained Physical Channel Information
## 4 Sybil Attack Detection System
### Denoising  
The raw CSI data are subject to noise interference due to state transitions inside Tx and Rx, such as transmission power changes, transmission rate adaptation and additive DC noise of the device. These internal state transitions introduce high-amplitude pulses and burst noise in the CSI. Figure 5a shows the original CSI amplitude when the environment changes. It can be seen that the amplitude is extremely noisy. Accordingly, we should eliminate the interference noise before applying the dynamic detection algorithm.  
CSI amplitude values of 30 subcarriers have different sensitivities to changes in the WLAN environment. The traditional noise elimination method uses a low pass filter, which cannot eliminate  
Figure 4: Sybil attack detection system of multiple detection APs.  
Figure 5: Different data processing methods. (**a**) Low pass filter; (**b**) PCA; (**c**) Savitzky–Golay (SG) filter.  
Figure 3: Overview of the Sybil attack detection system. CSI, Channel State Information.  
the impact of burst pulse signals in the environment. What is more, the passband of a conventional low pass filter, such as a Butterworth filter, typically requires less than 1/20 of the sampling rate to suppress the residual energy of the noise. If the cut-off frequency of the low pass filter is too high, the residual noise may distort the flow. Besides, some vibrations may contain not only low frequencies, but also high frequencies. Figure 5a shows the subcarrier signal after the low pass filter with a cut-off frequency of 80 Hz. It eliminates part of the high frequency noise, but has different effects on different carriers, leading to waveform delay. The method of Principal Component Analysis (PCA) is often used in the literature in these cases. PCA can automatically mine the correlation between CSI streams and reorganize the CSI stream to extract the change components caused by human activities. PCA can only show volatility without numerical features. Still, this method also amplifies the effects of changes in the environment. When the environment is still, its PCA value will still have much volatility as shown in Figure 5b. We use the Savitzky-Golay filter to de-noise the received data.  
The Savitzky-Golay filter is a filtering method based on local polynomial least squares fitting in the time domain. The biggest feature of this filter lies in the fact that the noise can be filtered at the same time to ensure the shape of the signal and the same width. In attack detection, the waveform needs to be able to fully reflect the fluctuations of the line of sight. Therefore, we can apply the SG filter algorithm to 30 subcarriers at the same time. As shown in Figure 5c, we can see that the CSI amplitude waveform has good volatility and is smoother than the low pass filter when the SG filter is applied.
## 1 Introduction  
In environmental monitoring, instrumental analyses using the chromatography technique is the best choice, but it is also a laborious, expensive, and time-consuming method. There is a growing need for rapid screening methods which can be applied at the previous state of the instrumental analysis. In vitro bioassay is currently considered as screening tool in a tiered approach, but there is still uncertainty about the reliability in environmental monitoring, because the results of in vitro bioassay may be affected by unidentified factors in the field samples [1].  
The tiny size of nanoparticles (NPs), along with their substantially different characteristics, has led to their increased manufacture and application in commercial products. The increasing use of nanoproducts, such as in dental and skin care, self-cleaning textiles, drug development, information and communication technologies, and water treatment, makes organisms vulnerable to NP exposure [2; 3; 4]. The cellular toxicity of metal-based NPs has attracted the attention of many researchers around the world. Several studies have reported that metal-based NPs' treatment of non-lethal levels induce the adverse effects, such as mitochondrial dysfunction, membrane damage, intracellular oxidative stress, and abnormal cell morphologies [5; 6; 7; 8; 9; 10; 11; 12], which alter specific functions of the cell.  
Endocrine-disrupting compounds (EDCs) comprise a wide range of natural and synthetic compounds that can mimic or interfere with the functions of endocrine systems, regulating a variety of biological functions in vertebrates, such as growth, metabolism, cell growth and proliferation, cell function and differentiations, sexual development and behavior, and development of the immune system in both sexes [1; 13; 14; 15]. The yeast two-hybrid system (YES assay) has been wildly utilized to screen estrogenic compounds in environmental media. Considering NPs' unique physicochemical properties, including their high surface area relative to their volume, high interface energy, and high surface-to-charge ratio density [5], various interactions between NPs and EDCs in the water environment can be predictable. However, there has been no study about the effects of NPs in the environmental media on the results of YES assay. Additionally, little is known about the effects of NP exposure at the non-lethal level on the yeast, although the lethal toxicity of metallic NPs on the yeast has been well documented [16]. In the present study, we investigated the effects of metallic NPs on the performance of YES assay with a Japanese medaka estrogen receptor (mER).
# Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model
## Modeling the Network
### Sensitivity Analysis  
To evaluate the relative importance of the parameters in the network, the Garson's factor was used [78]. The equation provided for the network with a hidden layer is:  
\[Q_{ik}=\frac{\sum_{j=1}^{L}\left(\frac{w_{ij}}{\sum_{i=1}^{N}w_{ij}}\
u_{jk} \right)}{\sum_{i=1}^{N}\left(\sum_{j=1}^{L}\frac{w_{ij}}{\sum_{i=1}^{N}w_{ij}} \
u_{jk}\right)} \tag{3}\]  
where \(\sum_{r=1}^{N}w_{rj}\) is the sum of the connection weights between the \(N\) input neurons and the hidden neuron \(j\), and \(\
u_{jk}\) is connection weight between the hidden neuron \(j\) and the output neuron \(k\)[79].  
Sensitivity of each parameter is then presented in Figure 7. The first (and the most important) observation is that nearly all the parameters are contributing effectively in the overall performance of the SWs. Among them, the column's moment of inertia and ultimate stress of the infill plate have most dominant effects. Strength and lateral stiffness of a SPSW depend on the development of diagonal tension field in the infill plate. To develop a uniform diagonal tension field, the boundary elements should have enough flexural stiffness to anchor the tension field [80]. As a result, the flexural stiffness of the boundary elements will have the greatest impact on the load bearing capacity (as seen in this figure). Due to the yielding of the infill plate during loading, its ultimate stress also affects the behavior of the SPSW. Moreover, the thickness and width of the infill plate have significant contribution in the strength. Therefore, the proposed ANN can accurately estimate the behavior of SPSWs. On the other hand, the most important parameters affecting the strength, stiffness and drift ratio of RCSWs are the wall's length (effectiveness = 17.8%), the wall's height (effectiveness = 14.6%), and the horizontal column's reinforcement ratio (effectiveness = 10.3%).
## 2 The Proposed Image Fusion Method  
### Fusion Pretreatment  
The sonar image fusion in the overlapping area is to make full use of the complementary information from the source image to enhance the feature information, such as the size and edge outline of underwater target, the landform, and texture. The sonar image fusion pre-processing includes noise filtering, image registration, multi-scale decomposition, and reconstruction. In general, median filtering is used to de-noise the side-scan sonar image [26]. In addition, the method of speeding up robust features is registered to obtain images of the overlapping area. Moreover, in order to enhance the directional selectivity and obtain better spectral characteristics of image information, NSCT transformation is used to decompose the source image into low frequency and high frequency sub-band components. Then, different low frequency and high frequency fusion criteria are adopted to select and determine the signal source. Finally, a new fusion image is generated according to the combined signal reconstruction.
## 4 Method
### Markov Decision Process in TSC  
The quintuple of the Markov decision process is an abstract overview of the reinforcement-learning process, which can be defined differently in different scenarios. In the hierarchical traffic-signal control problem, the MDP of managers and workers are: \(\mathcal{M}^{M}=(S^{M},A^{M},R^{M},T,\gamma^{M})\), \(\mathcal{M}^{W}=(S^{W},A^{W},R^{W},T,\gamma^{W})\), respectively. In particular, we adopt SUMO as the experimental environment in this paper. We use various parameters in SUMO to define \(\mathcal{M}^{M}\) and \(\mathcal{M}^{W}\) in detail.  
\(S^{M}\) and \(S^{W}\) are the state spaces of manager and worker, respectively. However, TSC is a partially observable environment, which means that the manager and the worker can only obtain an observation \(O^{M}\) and \(O^{W}\) of their own surrounding environment. We define the worker's local observation as \(O^{W}=(wait[l],\mathit{pressure}[l])\) where \(l\) is each incoming lane for this worker. The _wait_ is the accumulated waiting time of the vehicle closest to the intersection at the current moment. _Pressure_ refers to max-pressure [30], which defines the traffic pressure in this direction. More specifically, _pressure_ is equal to the number of vehicles on the target road minus the number of vehicles on the current road. For the manager, we define its local observation as \(O^{M}=(\mathit{pressure}_{1},\mathit{pressure}_{2},\mathit{pressure}_{4})\), which is the vehicle pressure situation in four directions in this region.  
We define a manager's action as the goals assigned to each subordinate worker. In order to help workers to measure the completion of the goal, we keep the form of the goal consistent with \(O^{W}\). At the same time, goals also represent the future state that managers expect workers to achieve. For workers, the action space includes the individual switching commands of the traffic lights. For example, red lights in the east-west direction, go straight in the north-south direction or turn left in the east-west direction.  
Finally, the most important thing is the setting of the reward function. The reward function represents the task we expect the agent to solve. As a manager, we expect to assign the correct goals to each subordinate. Therefore, we can take the state change between two decisions as the reward \(R_{t}^{M}=\sum_{l}\mathit{pressure}_{t+k,l}-\mathit{pressure}_{t,l}\), where \(l\) is each direction. For workers, their reward function is divided into two parts: \(R^{M}=R_{e}+R_{g}\). \(R_{e}=-\sum_{l}(\mathit{pressure}_{l}+wait_{l})\) is the environmental reward, which is the feedback of the agent's action. \(R_{g}=\sigma(O^{W},g)\) is the intrinsic reward, which represents the completion of the worker's goal and \(\sigma(X,Y)=X^{T}Y/(|X||Y|)\). \(T\) is state transition functions, which are determined by the environment.
## 3 Study Area and Data Sources
### Available Datasets  
#### 3.2.1 Sentinel-1A  
Launched on 3 April 2014, Sentinel-1A offers open-source SAR images with world-leading orbital and attitude control, ensuring excellent and stable interferometric capabilities. Its IW imaging mode boasts a 250 km swath width, with a spatial resolution of 20 m in the azimuth direction and 5 m in the range direction. Due to damage to Satellite B, its revisiting period is currently 12 days, making it a top-tier data source for InSAR research. In this study, 15 Sentinel-1A images (from 2 October 2024 to 12 April 2025) underwent time-series PS/DS-InSAR processing to capture the spatial-temporal deformation distribution in the study area. Additionally, four images (from 23 February 2025 to 31 March 2025) were used for CR identification and deformation monitoring based on CR. Table 2 provides the image parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Satellite** & **Band** & **Acquisition** & **Orbit/View** & **Pixel** & **Incidence** & **Heading** & **Time Span** \\
**Name** & **Mode** & **Angle** & **Spacing (m)** & **Angle (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** & **Angle (\({}^{\circ}\))** & **Time Span** \\ \hline Shenqi & C & Strip & Ascending/right & 1.3/1.8 & 38.18 & 12.30 & \begin{tabular}{c} 20241121–20250413 \\ (Total: 5 scenes) \\ \end{tabular} \\ \hline Fucheng & C & Strip & Ascending/right & 1.3/1.8 & 38.18 & 12.30 & \begin{tabular}{c} 20241026–20250409 \\ (Total: 15 scenes) \\ \end{tabular} \\ \hline \hline \end{tabular} \
\begin{tabular}{c c c c c c} \hline  & & & & & & & \\ Sentinel-1A & C & IW & Desending/right & 2.3/14.0 & 30.6 & 193.37 &
\begin{tabular}{c} TS-InSAR: \\ (Total: 15 scenes) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: **“Fucheng-1”, “Shenqi”, and Sentinel data parameter information.**  
Figure 2: **(a) Location of Gansu Province in the People’s Republic of China. (b) Color map showing the elevation of a selected region of Gansu Province. The blue dashed line shows the coverage of the Sentinel-1A image taken in descending orbit for Path 33, Frame 462. The yellow solid line indicates the coverage of the “Fucheng-1” and “Shenqi” images taken in ascending orbit. (c) The gray irregular shapes represent the specific mining locations in the Jinchuan mining area. The purple triangles indicate the positions of the nine CRs deployed in the Jinchuan mining area.**
# A LiDAR-Based Backfill Monitoring System
###### Abstract
Among the methods for digitizing and visualizing three-dimensional scenes, LiDAR sensors and related technologies have rapidly developed in recent years, driven by the surge in autonomous driving applications [15]. LiDAR emits laser beams into the surrounding environment and receives the reflected laser signals. By measuring the round-trip time of the laser, it can determine the position and shape of objects in three-dimensional space. These data are compiled into a point cloud, which is a collection of points distributed in space. Through a series of point cloud processing algorithms, the scene can be modeled with high precision [16; 17]. In underground structures, where dust concentration is high and light conditions are poor, traditional visual monitoring schemes are not suitable. LiDAR is not sensitive to ambient light brightness and has strong anti-interference capabilities, making it widely used in underground environments. Methods for processing chronological LiDAR data obtained in underground tunnels typically involve comparing the latest scan with previous ones to identify and calculate tunnel deformations [18; 19; 20; 21; 22; 23]. LiDAR can be used to estimate the volume of blasted rock [24] and load volume in trucks [25; 26] by adding the top surface of materials scanned by LiDAR to the container boundaries. In this work, LiDAR technology was applied to obtain the three-dimensional spatial form of the backfill process. To digitize and visualize backfill progress, a simulation environment of the paste backfill process was constructed using CARLA, a flexible and open-source simulator designed for autonomous driving research, allowing for the customization of sensor suites and environmental conditions [27]. Algorithms to compute the backfill progress were tested within this simulation environment, and a LiDAR-based backfill monitoring system was designed and assembled to perform the aforementioned functions.
# Gaussian Process Modeling of Specular Multipath Components
## 4 Results
### GPR of SMC Amplitudes
#### 4.3.1 Predictability  
In order to test the ability to predict the SMC amplitudes using GPR, we divide the data set into two subsets, for training and testing. It is desirable for the training data to cover the largest possible range of angles. Therefore, we choose segments 4, 5, 6 for training, and 1, 2, 3, 7 for testing (c.f. Figure 1).  
Figure 4a-d show the prediction results using the normalized amplitudes for EPB (at \(\mathbf{a}_{2}^{(1)}\)), LOS (at \(\mathbf{a}_{1}^{(1)}\)), NGW (at \(\mathbf{a}_{5}^{(1)}\)) and SW (at \(\mathbf{a}_{3}^{(2)}\)), respectively. Again, the red solid bold line depicts the predicted mean, the black and blue solid lines show the upper and lower \(\pm 2\sigma\) limits (comprising 95% of the data points), plotted over the positions along the trajectory. It is shown that the predicted mean of the SMC amplitudes coincides very well with variations of the estimated SMC amplitudes. Moreover, the predicted upper and lower limits contain most of the SMC amplitudes obtained from measurements for the test data.  
Table 2 shows the prediction quality evaluated with the SMSE and MSLL as defined in Section 3.4. The predictability for the LOS amplitude is clearly very reliable, indicated by the small SMSE value and large negative MSLL values. The predictions of the SMC amplitudes for EPB, NGW and SW are still reasonable good in comparison to the LOS amplitude given that the SMSE values are all smaller than 1 and MSLL values are all negative. However, the amplitude prediction for EPB outperforms the predictions for the NGW and SW. A possible explanation might be that the DMC standard deviation is smaller (see for Table 1) for the estimated SMC amplitudes of EPB and the material of the reflective surface is more homogeneous (only plasterboard instead of a combination of plasterboard, metal, and glass).
# Eocene Sedimentary-Diagnetic Environment Analysis of the Pingtai Area of the Qaidam Basin
## 4 Clay Minerals
The presence of smectite reflects cold climate characteristics [42], the existence of illite-smectite mixed layers reflects the gradual transformation of smectite into illite in an alkaline environment rich in K\({}^{+}\), and chlorite is formed in an alkaline environment rich in Mg\({}^{2+}\)[34; 43]. Smectite coexisting with hematite indicates a semi-arid climate [44], whereas illite-smectite mixed layers represent a gradual change of climate to a humid environment [45]. Since the Paleogene, the sedimentary sources in the Pingtai area have mainly been from the western section of Saishiteng Mountain [16]; thus, the deposits are considered near-source, and the source area and sedimentary area have the same sedimentary-diagenetic environment. As a result, the characteristics of the clay mineral assemblages in the samples reflect the sedimentary-diagenetic environment in the study area. The clay minerals in the lower Eocene (Lulehe Formation depositional period) clastic rocks in the Pingtai area are mainly composed of high contents of a combination of smectite, chlorite and illite without illite-smectite mixed layers or kaolinite. The middle to upper Eocene (sedimentary period of the Xiaganchaigou Formation) is dominated by high contents of illite-smectite mixed layers, chlorite and illite without smectite or kaolinite. These assemblage characteristics indicate that the sedimentary and diagenetic environment of the Pingtai area in the early Eocene was dominated by alkaline media poor in K\({}^{+}\) and rich in Mg\({}^{2+}\), Na\({}^{+}\) and Ca\({}^{2+}\). Chlorite and illite contents gradually decrease from bottom to top, indicating that the cold and dry climate gradually changed to a cold climate that alternated between dry and wet conditions. In the middle-late Eocene, smectite disappeared, illite-smectite mixed layers appeared and the contents of illite and chlorite decreased slightly, indicating that the diagenetic medium was still alkaline. However, K\({}^{+}\) increased notably and smectite began to transform into illite, indicating that the climate was relatively humid.
# Predicting Hospital Admissions to Reduce Crowding in the Emergency Departments
## 2 Literature Review
Applying a logistic regression model on results generated by a Naive Bayes classifier from data collected within the first 30 min from arrival yielded good results in their study, identifying more than 73% of admissions with a 90% specificity and over 35% with 99.5% specificity, or what is the same, a false-positive rate of 0.5% (AUC = 0.91). The method has also been applied to predict admissions at the moment of arrival (after 0 min), successfully identifying 50.6% of the hospitalizations with a 10% false-positive rate and obtaining an overall AUC = 0.79 [12]. Other researchers have explored manipulating the data slightly to obtain models that would show better performance measures and higher accuracy. [PERSON] et al. (2018) [29] [[https://orcid.org/0000-0003-0617-8873](https://orcid.org/0000-0003-0617-8873)]([https://orcid.org/0000-0003-0617-8873](https://orcid.org/0000-0003-0617-8873)) showed that it was possible to increase the prediction accuracy of admission at the time of arrival for patients visiting the ED with a logistic regression model by dividing the observations into two groups, one containing those related to patients under 70 years old (AUC = 0.86), and a second one including the rest, i.e., those containing observations of patients above 70 years old (AUC = 0.77) [29]. The random forest technique has also been used in this field to develop an e-triage model to predict the likelihood of acute outcomes that may lead to admission. [PERSON] et al. (2018) obtained models with AUC ranging from 0.73 to 0.92 for different datasets [30]. Although the objective of their study was more focused on the demonstration that the current triage systems could be improved through the use of information extracted from patient data upon arrival at the ED, it also showed the potential of these models to predict hospital admission with significant accuracy. Furthermore, a study from 2018 [21] set the objective to determine which, among the most popular predictive techniques, could provide the best performance. Training models on triage information yielded a test AUC of 0.87 for all logistic regression, gradient boosting, and deep neural networks (DNN) models. However, the study went further on to prove that combining triage information with patient history could significantly improve predictive performance for all three methods, achieving an AUC of 0.91 for the logistic regression model, and 0.92 for the gradient boosting and the DNN models. Models trained on patient history information exclusively did not yield better results than those trained with triage data alone. They also explored that the gradient boost and DNN outperformed logistic regression on the full dataset while there was no significant difference in performance between them [21].
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 7 Trade-Off Analysis  
The performance of chipless RFID tags based on planar array configurations is evaluated using several key parameters. These parameters include linear detectability, spherical detectability, RCS, and the Q-factor. Understanding the trade-offs among these factors is crucial to optimise the design and operation of chipless RFID systems.