esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Guidance and Control Algorithm
### Inertial Alignment Hold
#### 5.3.2 Linear Quadratic Regulator Algorithm  
A linear quadratic regulator (LQR) is used to regulate the relative states in a constant position in the positive along-track direction. The cost function is defined as  
\[J=\frac{1}{2}\int_{0}^{\infty}\left(\mathbf{X}_{e}^{T}\mathbf{Q}\mathbf{X}_{e}+\mathbf{u}_{LQR }^{T}\mathbf{R}\mathbf{u}_{LQR}+2\mathbf{X}_{e}^{T}\mathbf{N}u_{LQR}\right)dt, \tag{22}\]  
Figure 8: Relative states of DADC at the initial and the terminal time in the along-track direction.  
where \(\mathbf{X}_{e}=\mathbf{X}-\mathbf{X}_{d}\) is the relative control error; \(\mathbf{X}\) and \(\mathbf{X}_{d}\) are the relative and desired states, respectively; and \(u_{LQR}\) is the LQR control input.  
The linearized system is described as  
\[\mathbf{X}_{e}=\mathbf{A}\mathbf{X}_{e}+\mathbf{B}u_{LQR}, \tag{23}\]  
where the state and input matrices are obtained from Equation (1).  
The Riccati equation and the optimal gain, \(K\), are obtained by  
\[\mathbf{A}^{T}\mathbf{S}+\mathbf{S}\mathbf{A}-(\mathbf{S}\mathbf{B}+\mathbf{N})\mathbf{R}^{-1}(\mathbf{B}^{T}\mathbf{S} +\mathbf{N}^{T})+\mathbf{Q}=\mathbf{0}, \tag{24}\]  
\[K=\mathbf{R}^{-1}(\mathbf{B}^{T}\mathbf{S}+\mathbf{N}^{T}). \tag{25}\]  
Finally, the control input of LQR is derived as  
\[u_{LQR}=-K\mathbf{X}_{e}=-\mathbf{R}^{-1}(\mathbf{B}^{T}\mathbf{S}+\mathbf{N}^{T})\mathbf{X}_{e}, \tag{26}\]  
where the matrix \(\mathbf{Q}\), \(\mathbf{R}\), and \(\mathbf{N}\) are user-specified values.  
The errors are described in the block diagram of the control algorithm in Figure 9. The relative navigation error is included in the state input and affects the guidance trajectory, control input, and target quaternion of the tracking problem. The pointing error of the ADCS system is generated by the current estimation attitude and degrades the performance at thrust firing and payload alignment. The thrust errors are modeled as the misalignment and magnitude error of the thrust vector and are assumed to be applied at the center of mass of the satellite.
# A Numerical Study of Chemical Compatibility of GCLs
## 1 Introduction  
Geosynthetic clay liners (GCLs) are factory-manufactured hydraulic barriers consisting of a thin layer of sodium bentonite (Na-B) clay (approximately 3-5 kg/m\({}^{2}\)) sandwiched between two geotextiles, which are bonded by needle punching or stitching [1]. GCLs are common elements in waste containment facilities because of their low hydraulic conductivity to deionize water (DIW, typically \(<\)10\({}^{-10}\) m/s) [1, 2, 3, 4, 5].  
Chemical compatibility is generally defined as the degree of change in the engineering properties of the barrier due to its exposure to contaminants [6]. [PERSON] [7] and [PERSON] [3] demonstrated that ionic strength is the main variable affecting the hydraulic conductivity of bentonite in GCLs. However, the mesoscopic structure effect of a chemical solution on the permeability of GCLs is difficult to be observed in the experiments. Many kinds of research have shown that the hydraulic conductivity of GCLs depends on the mobile porosity (or the mobile water) [8, 9, 10, 11, 12], which is sensitive to the chemical solution [13, 14, 15]. However, a direct translation of X-ray data to GCLs is difficult because GCLs are multi-component systems [16, 17, 18]. Meanwhile, the porosity estimates for GCLs depend on subtle interactions within the pore spaces governed by the leachate flux process [19, 20, 21]. At present, few studies have focused on the mesoscopic mechanism of hydration and swelling under the effect of inorganic chemical solutions. Therefore, the influence of chemistry on the mesoscopic structure and the permeability of GCLs needs to be assessed [22, 23].  
The finite-element COMSOL Multiphysics software is very convenient to simulate and analyze the infiltration process of GCLs [24, 25, 26]. In this work, we will establish aCOMSOL numerical model to study the anti-seepage performance of bentonite components. The swelling and permeation process of GCLs in a chemical solution will be simulated as well. The effects of the initial mobile porosity, the swelling ratio, the pore size, and the chemical compatibility of GCLs were studied.  
Figure 1 gives the flow chart of this paper. Firstly, a series of COMSOL models which simulate the GCLs in DIW were established. Based on these numerical models, an equation, including the final mobile porosity, the initial mobile porosity, and the initial particle size for calculating the hydraulic conductivity of GCLs, was proposed. Secondly, the final mobile porosity was expressed by the hydraulic conductivity, the initial mobile porosity, and the initial particle size. Using the fitting method and experimental data, a relationship between the final mobile porosity and the ionic strength was obtained. Finally, a series of COMSOL models were established to investigate the chemical compatibility of GCLs. The mesoscopic mechanism of the physical and chemical processes was screened in COMSOL models. Based on the numerical simulation, a theoretical model which can predict the hydraulic conductivity of GCLs in a chemical solution was proposed.
# Deep Learning-Based End-to-End Carrier Signal Detection in Broadband Power Spectrum
###### Abstract  
This paper presents an end-to-end deep convolutional neural network (CNN) model for carrier signal detection in the broadband power spectrum, so-called spectrum center net (SCN). By regarding the broadband power spectrum sequence as a one-dimensional (1D) image and each subcarrier on the broadband as the target object, we can transform the carrier signal detection problem into a semantic segmentation problem on a 1D image. Here, the core task of the carrier signal detection problem turns into the frequency center (FC) and bandwidth (BW) regression. We design the SCN to classify the broadband power spectrum as inputs and extract the features of different length scales by the ResNet backbone. Then, the feature pyramid network (FPN) neck kuses the features and outputs the fusion features. Next, the RegNet head regresses the power spectrum distribution (PSD) prediction for FC and the corresponding BW prediction. Finally, we can achieve the subcarrier targets by applying non-maximum suppression (NMS). Moreover, we train the SCN on a simulation dataset and validate it on a real satellite broadband power spectrum set. As an improvement of the fully convolutional network-based (FCN-based) method, the proposed method directly outputs the detection results without post-processing. Extensive experimental results demonstrate that the proposed method can effectively detect the subcarrier signal in the broadband power spectrum as well as achieve higher and more robust performance than the deep FCN- and threshold-based methods.  
c pacs: 22
## 2 Related Works
### Graph-Based and Self-Supervised Approaches  
Graph-based learning techniques have gained traction for anomaly detection in WSNs due to their ability to capture spatial relationships between sensor nodes. The authors in [13] introduced a self-supervised learning approach that integrates temporal, spatial,and intermodal features into an Autoencoder-based model. Their method extracts temporal dependencies using fully connected networks, while graph neural networks (GNNs) incorporate WSN topology, capturing both local and global node interactions. The study demonstrates high detection accuracy (90.6%), outperforming conventional models. However, the method struggles with scalability, as processing multimodal data introduces computational bottlenecks, making real-time deployment challenging.  
Similarly, the authors in [14] explored deep learning models for WSN-based anomaly detection, emphasizing energy-efficient processing and real-time inference. Their research discusses challenges such as limited memory, energy-intensive computations, and network latency--issues that are particularly relevant for large-scale deployments. While the study highlights solutions like model compression, federated learning, and edge computing, these techniques often come at the cost of reduced detection precision. Unlike the authors in [13], who rely on centralized graph learning, the authors in [14] propose distributed deep learning frameworks, making their approach more scalable but potentially less accurate for fine-grained anomaly detection.  
Both approaches offer valuable insights, yet they share a common limitation: reliance on unsupervised learning for feature extraction. This often results in high false positive rates, as the models fail to distinguish between intrusions (malicious attacks) and sensor malfunctions. In contrast, our proposed work integrates a Transformer-based Autoencoder with a hybrid classification model (Isolation Forest + XGBoost) to improve the differentiation of anomaly types.
# Can Anaerobically Digested Food Effluent Support _Arthrospira platensis_ Cultivation in Open Ponds?
## 3 Results
### Physicochemical Parameters and Ammonium Content  
Figure 2 illustrates the biomass yields of _A. platensis_ cultures grown with varying percentages of ADFE. Throughout the growth period, cultures grown in 70% and 50% ADFE consistently outperformed those grown with 37.5% ADFE and Zarrouk medium. By day 22, the biomass yield reached 1.38 g L\({}^{-1}\) and 1.24 g L\({}^{-1}\) in the 70% and 50% ADFE media, respectively, representing a 20-35% increase compared to the Zarrouk medium (One-way ANOVA, \(p\) < 0.05). In contrast, slower growth was observed in the 37.5% ADFE (Figure 2E) and the Zarrouk medium (Figure 2D), resulting in significantly lower final yields. These results highlight the improved biomass productivity achieved with 50% and 70% ADFE media, demonstrating their superior efficacy over the traditional Zarrouk medium.  
The N-NH4+ concentrations of the cultures at day 0 and 9 were shown in Figure 2D-G. Ammonium content significantly decreased in all ADFE media between days 0 and 9. In the 37.5% ADFE medium, ammonium decreased by 97.7% (Figure 2E), while in the 50% ADFE-addition medium, it dropped by 60.1% (Figure 2F). The 70% ADFE-addition medium showed the highest ammonium levels, with a 62.8% decrease of ammonium contentment between days 0 and 9 (Figure 2G). These results indicate that ammonium levels decreased significantly across all ADFE treatments (One-way ANOVA, \(p<0.05\)), with higher residual ammonium concentrations observed in the higher ADFE concentrations (50% and 70% ADFE media).  
Figure 2: Biomass yield (Ash-free dry weight; g L\({}^{-1}\)) and ammonium concentration of _A. platensis_ cultures in different media. (**A**) irradiance, (**B**) temperatures, (**C**) rainfall, (**D**) biomass yield in Zarrouk, (**E**) biomass yield in 37.5% ADFE, (**F**) biomass yield in 50% ADFE, (**G**) biomass yield in 70% ADFE. Data are mean \(\pm\) SD, n = 3. Arrows indicate the timing of ADFE addition to the respective media.  
The results demonstrated that media containing ADFE significantly improved the overall productivity of _A. platensis_ cultures compared to the standard Zarrouk medium (Table 4). Overall volumetric and aerial biomass productivity was 70% ADFE-addition medium = 50% ADFE-addition medium (>37.5% ADFE-addition medium > Zarrouk media.  
The maximum specific growth rate (\(\mu_{\max}\)) ranged from 0.093 \(\pm\) 0.03 day\({}^{-1}\) in the Zarrouk medium to 0.13 \(\pm\) 0.01 day\({}^{-1}\) in the 70% ADFE medium, showing no significant variation among media. Notably, \(\mu_{\max}\) in the Zarrouk medium occurred later in the growth period (days 18-22), while the 50%, 70%, and 37.5% ADFE media achieved \(\mu_{\max}\) earlier, between days 13-16. These findings highlight the efficacy of ADFE addition, particularly at 50% and 70% levels, in improving _A. platensis_ productivity.
## 3 Analysis of Simulation Result
### Effect of Voltage on Discharge  
In order to explore the influence of the applied voltage on the surface discharge of C4F7N/CO2, this paper compares the surface discharge of C4F7N/CO2 when the applied DC voltage is 40 KV, 42.5 KV, 45 KV, and 50 KV.  
Figure 7 shows the two-dimensional electron concentration distribution of the streamer near the cathode at different applied DC voltages. By comparison, as the applied DC voltage increases, the electron concentration of the streamer reaching the cathode increases from \(5.53\times 10^{21}\) m\({}^{-3}\) to \(1.15\times 10^{22}\) m\({}^{-3}\). At the same time, the development time of the streamer from the needle electrode to the plane electrode becomes shorter, and the gas mixture is more likely to be broken down, which is consistent with the results in [17].  
Figure 8 shows the electron concentrations at the time indicated in Figure 7. Figures 9 and 10 show the electric field intensity and photon flux at the time indicated in Figure 7. With the increase in the applied voltage, the background electric field in the whole space increases, the ionization reaction of the mixed gas intensifies, and more electrons are produced. Therefore, the electric field intensity of the streamer head increases, and the photon flux also increases.  
Figure 5: Variation in the electric field intensity of the streamer head. (**a**) Stage 1; (**b**) Stage 2; (**c**) Stage 3.  
Figure 6: Variation in photon flux of the streamer head. (**a**) Stage 1 (**b**) Stage 2 (**c**) Stage 3.  
Figure 8: Variation of electron concentrations with applied DC voltage.  
Figure 7: Electron concentrations at different applied DC voltages: (**a**) 40 KV; (**b**) 42.5 KV; (**c**) 45 KV; (**d**) 50 KV.  
Figure 9: Variation of electric field strength with applied DC voltage.
# A Survey of Vehicular VLC Methodologies
###### Abstract  
Visible Light Communication (VLC) has recently emerged as an alternative to RF-based wireless communications. VLC for vehicles has demonstrated its potential for Intelligent Transportation Systems (ITSs) to exchange information between vehicles and infrastructure to achieve ITS core goals, such as improving road safety, passenger comfort, and traffic flow. This paper seeks to provide a detailed survey of vehicular VLC systems. This paper presents an overview of current developments in vehicular VLC systems and their benefits and limitations for experienced researchers and newcomers.  
visible light communication (VLC); vehicular VLC; channel modeling; intelligent transportation system (ITS); vehicle-to-vehicle VLC; infrastructure-to-vehicle VLC
## 3 Results and Discussion
### Effect of the Number of Weeks of Data on the Population Predictor
The day and night time predictors show a very consistent predictive power, with little to no difference at low Phs when using different dataset volumes for training, and a slight trend of decreasing RMSE as the number of weeks increases for longer PHs. Figure 3: RMSE Median and Quartiles metrics for the different Glucose Predictor for all prediction horizons for all the training dataset sizes. Hypoglycemia predictor in blue, Night predictor in green, and Meal predictor in red. This is concordant with the initial hypothesis of the effect of the dataset volume on the predictive power. Similar trends are observed if the Overall prediction error is analysed, as seen in Figure 4. For the shorter PHs (\(PH\in\{15,30,60,120\}\)), the error bars of the two-week dataset are significantly larger than all the other (larger) datasets used for training, suggesting that a critical volume of training data for obtaining consistently good predictions is more than two weeks. For longer PHs, lower RMSE values tend to be achieved when larger datasets are used for training (6, 8 or 12 weeks of data vs. 2 or 4), but this trend is inverted when the dataset volume is increased to 16 weeks. This suggests that using 16 weeks of data from a whole cohort of patients to train models might lead to subpar predictions, since more dynamics are modeled that may not be used to provide accurate predictions of an individual patient. This is also observed in Figure 5, where the median and interquartile ranges for all the Overall predictors are illustrated overlapping. Median RMSE remains mostly unchanged for low PHs, and it reaches its minimum values for 4, 6 and 8 weeks for longer PHs. Interquartile range drastically increases for the 2 and 16 weeks datasets. When comparing the RMSE between PHs, it is curious to observe how better predictions are achieved for the 240 minutes prediction vs the 120 minutes one. Figure 4: RMSE Median and Quartiles metrics for the Overall for all prediction horizons for all the training dataset sizes.
# Potential and Limitations of Open Satellite Data for Flood Mapping
## 2 Description of the Study Areas
### November 2016 Flood in Western Po River Basin (Italy)  
In late November 2016, a severe flood hit the western sector of the Po River basin corresponding to the Piemonte region (NW of Italy). This area, periodically affected by floods, particularly during the autumn and spring seasons [54; 55], has already been investigated with remote sensing technologies [28; 32; 36]. In the period of 21-25 November 2016, heavy rainfall of up to 50% of the mean annual precipitation (e.g., 700 mm of cumulated rainfall at Piaggia station [56]) occurred in the western basin of the Po River and its tributaries, the Tanaro River in particular. The Po River at Caringano gauge (P_Ca in Figure 1C) reached a maximum discharge of approximately 2000 m\({}^{3}\)/s in the late evening of 25 November 2016. However, the mean discharge of the monitoring station in November is 70 m\({}^{3}\)/s. The Po River reached a discharge of 9900 m\({}^{3}\)/s (November average is 680 m\({}^{3}\)/s) after the confluence with the Tanaro. The Tanaro River grew to an estimated discharge of 3800 m\({}^{3}\)/s at Montecastello (T_Mo in Figure 1B) station (average discharge of 220 m\({}^{3}\)/S) [56]). Thus, inundation of cultivated areas, linear infrastructures and some urban areas, such as the town of Moncalieri [57], occurred with estimated damages of 50 M\(\epsilon\). In this case, the Copernicus EMS (EMSR-192 [58]) was also activated, allowing the production of automatic flood maps based on using RADARSAT-2 and Pleiades images in the most critical areas. In our work, we used data from MODIS-Aqua, Proba-V, and Sentinel-1/2 satellites with the support of a DEM of the area. In particular, we selected the two areas that were most heavily affected by the flood: (1) The plain located around the Alessandria town (Figure 1B) was inundated by the Bormida River on 25 November 2016 and by the Tanaro on 25-26 November 2016. Here, the main land use is cultivated land with small farms; the town of Alessandria (95,000 inhabitants) was not affected by inundation; (2) The plain located at south of Turin city (Figure 1C) was flooded by the Po River and the other tributaries (i.e., Chisola and Oitana streams) on 25 November 2016. Here, the main land use is cultivated land, some villages and the urban area of Moncalieri (60,000 inhabitants). To validate our results, we used the flooded area maps made available by the Regional Environmental Protection Agency (ARPA is its Italian acronym) of the Piemonte region.
# Situated AR Simulations of a Lantern Festival Using a Smartphone and LiDAR-Based 3D Models
## 6 Three AR Instancing Scenarios  
Three AR instancing scenarios were applied (Figure 6) based on whether a scene could be manipulated at the element level and how the manipulation was conducted (with or without a pre-defined model structure in the initial state of interaction). The tests were conducted only from the interaction made to an entire scene using an existing app, and to an individual model within a pre-defined layout using a customized app. A homogeneous combination of backgrounds refers to a plain one, or the same site as a festival recall. A heterogeneous combination refers to different backgrounds in the context or site of study, promotion, or design development.  
The three scenarios were applied. The first two were conducted as tests, and led to the development of the last one as a customized app to represent a previous lantern festival. The tests were conducted in four places: a laboratory, the original festival site, a remote site, and a personal working space.  
* The first: to browse. An interaction with the entire site was made. The point cloud of the entire festival scene was decimated, viewed, and interacted with in VR, but no sufficient support of point data conversion to AR was provided.
* The second: to interact and interpret. A forward additive instancing and interaction was made. A general set of instances was used for context-matching from individual elements to a scene. Mesh models were created and were effective in free-instancing. However, no sufficient support to the on/off display option or assistance to precisely controlled the relative location were provided. Different types of background context were applied as needed.  
Figure 5: (**a**) The crowd appeared as multiple moving trails in radical shape around the scanner; (**b**) grouping configurations.  
* The third: to recall, interact, interpret, switch display, and evaluate. An interaction with the pre-defined model layout was made. A structure with a set of pre-loaded instances was used for the recall of former lantern settings. The customized app had the relative location of models pre-defined as a reference of familiar scenes. Context matching was carried out from a remote site. Design alternatives were supported with on/off options for each model for evaluation. Different types of background context were applied as needed.
# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 4 Experiments and Discussion
### Qualitative Experiments
Figure 7: Experiment in which the robot senses that the path is blocked and computes a new path: (**a**) Before replanning and (**b**) after replanning. In (**a**), the robot is accelerating to reach the goal by moving towards the blocking obstacles. The cost map is updated with the three sensed obstacles that are blocking its trajectory. After reducing its velocity to avoid collision with the three static obstacles and the walls, a new path is sent that follows a new trajectory through free space, as seen in (**b**). This is the new plan the robot follows to reach the goal. The waypoint generator is a critical component, as it allows the robot to navigate through multiple rooms and scenarios with different shapes while avoiding static obstacles and blocking situations, which can frequently occur when using only a local planner. Additionally, the proposed design allows the local planner to fully exploit its capabilities by allowing it freedom to maneuver. Our approach outperforms other state-of-the-art approaches in terms of success rate and time to reach the goal, and is able to work correctly in real-world situations.