esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Mean Shift Algorithm with Dynamic Bandwidth  
After the extreme offset, the scattered points gather toward the corresponding tree-top, the density of the treetop increases significantly, and the density of the non-tree top decreases significantly. According to this density discrimination, the mean shift algorithm with dynamic bandwidth is adopted to cluster the offset point cloud, divide the offset point cloud set, and complete the labeling.  
The principle of the mean shift algorithm [29] is to use the probability density to obtain the local optimal solution, and the schematic diagram of the mean shift process is shown in Figure 4. Firstly, the mean shift vector of the current center point is calculated. Secondly, the point is moved to the end of the mean shift vector. Then, it is taken as the new starting point and continued to move until the length of the mean shift vector is less than the allowable error. Finally, the iteration is terminated when all points have been marked. The mean shift vector is shown in Equation (4):  
\[m(p_{t})=\frac{\sum_{i=1}^{n}-g\Big{(}\left\|\frac{p_{t}-p_{i}}{h}\right\|^{2} \Big{)}p_{i}}{\sum_{i=1}^{n}-g\Big{(}\left\|\frac{p_{t}-p_{i}}{h}\right\|^{2} \Big{)}}-p_{t}, \tag{4}\]  
where \(p_{t}\) is the center point; \(p_{i}\) is the point in the bandwidth; \(n\) is the number of points in the bandwidth; \(g(\cdot)\) is the derivative of the RBF kernel function; \(h\) is the bandwidth, which is determined by the following dynamic bandwidth strategy.  
The dynamic bandwidth strategy is given in Equation (5). Firstly, the KDTree is constructed by the offset point cloud. KDTree is constructed to arrange the unordered point clouds in a certain order, which is convenient for fast and efficient retrieval. Second, the average distance of each point to its k nearest neighbors is calculated according to KDTree. Finally, these average distances are averaged, and the resulting average nearest neighbor distance is used as the dynamic bandwidth \(h\).  
\[h=\Lambda\Big{(}\Big{\{}\gamma_{i}^{k}\Big{(}\big{(}\{p_{i}^{\prime}\}_{i=1}^{ n}\Big{)}\Big{)}_{j=1}^{n}\Big{)}, \tag{5}\]  
Figure 4: Schematic diagram of the mean shift process. The red arrow indicates the mean shift vector.  
where, \(\left\{p_{i}^{\prime}\right\}_{i=1}^{n}\) is the offset point cloud; \(n\) is the total number of points. \(k\) is a number of nearest neighbors of a point, equal to 5% of the number of points in the point cloud. \(\mathcal{Y}_{i}^{k}(\cdot)\) is used to calculate the average distance of point \(p_{i}^{\prime}\) to its k nearest neighbors. \(\Lambda(\cdot)\) is used to find the average nearest neighbor distance, which is used as the dynamic bandwidth \(h\).  
After self-adaptive mean shift clustering on the offset point cloud, all the offset points are labeled. As the order of the points is not changed before and after the extreme offset, the label vector after the cluster in the offset point cloud is concatenated with the original point cloud space so that the offset point cloud after the cluster is mapped back to the original point cloud space, and the preliminary ITS is completed.
## 4 Results
### Porosity and Permeability  
The porosity values of the studied samples (\(n=27\)) range from \(\sim\)1 to 20% with an average \(\upmu\) of \(\sim\)11% \(\pm\) 5.3%. The samples are tight in terms of permeability with values ranging from 0.002 to 1.9 mD; an average of \(\sim\)0.3 mD \(\pm\) 0.5. The porosity-permeability relationship of the studied samples is shown in Figure 9A, with the data points being classified based on their textural variations. Mud-supported samples (mudstone and wackestone) have higher porosity (5.33-19.72%, \(\upmu\) = 13.74% \(\pm\) 4.31%) and permeability values (0.0035-1.91 mD, \(\upmu\) = 0.55 mD \(\pm\) 0.54 mD) relative to grain-supported samples (packstone, griastone) (5.05-16.33%, \(\upmu\) = 9.99% \(\pm\) 4.2% for porosity, and 0.003-1.09 mD, \(\upmu\) = 0.24 mD \(\pm\) 0.32 mD for permeability). Reef lithofacies samples (rudstone and framestone) possess the lowest porosity values (0.77-5.77%, \(\upmu\) = 2.89% \(\pm\) 2.58%) combined with very low permeability values (0.003-0.004 mD, \(\upmu\) = 0.004 mD \(\pm\) 0.0003 mD) (Figure 9A). In  
Figure 8: Bioturbation impact on porosity (**A**) and permeability (**B**) values, with weak relation between bioturbation and both porosity and permeability. (**C,D**) Burrow fill impact on porosity and permeability. Samples with coarse-filled burrows have higher porosity and permeability values.  
Figure 7: Filling material of burrows. Thin-section photomicrographs showing the outline of the burrows, yellow dashed line. Main component of filling material in (**A**) a fine-filled burrow in coarse-grained material and (**B**) a coarse-filled burrow with coarse grains including oncoids, peloids (p), and skeletal fragments (s).  
addition, and as mentioned in the last part (Section 4.4), the coarse-filled burrows have higher porosities and permeabilities than fine-filled burrows (Figure 9B).
## 3 Methodology
### Integration of Communication and Computation Utilizing Global Item Models  
In FRSs, considering communication constraints of some clients, only a subset of clients is chosen to participate in the current communication round for federated training [24]. Clients in Scenario I, represented by \(U_{t}^{\prime}\) in communication round \(t\), despite being capable of downloading item models from the server, are unable to upload data due to communication fluctuations.  
Evidently, clients in \(U_{t}^{\prime}\) are unable to participate in normal federated training. However, they may retain unaffected local computation resources that are available for utilization. Additionally, their capabilities of downloading item models should not be wasted. We enable clients in \(U_{t}^{\prime}\) to perform normal local updates utilizing global item models downloaded from the server, while clients in \(U_{t}\) concurrently conduct federated training. Afterward, considering communication fluctuations during upload, the clients in \(U_{t}^{\prime}\) refrain from uploading updated item model parameters to the server, opting instead to store them locally. This approach leverages computation resources and download capabilities of communication-constrained clients in Scenario I for local training based on the global item models downloaded from the server. This integration of communication and computation utilizing global item models is abbreviated as **I2 CG**. The workflow of I2 CG clients is illustrated by the \"I2 CG clients\" in Figure 2.
# Tube-Based Robust Model Predictive Control for Autonomous Vehicle with Complex Road Scenarios
## 5 Conclusions  
This study introduced a Tube-based Robust Model Predictive Control (Tube-RMPC) framework to enhance the trajectory-tracking performance of autonomous vehicles under complex road conditions. Integrating a unified vehicle-tire modeling approach, the proposed method effectively addresses model parameter uncertainties and variations in road adhesion, ensuring enhanced stability through a robust invariant tube mechanism.  
Simulation results demonstrated that Tube-RMPC consistently outperforms conventional MPC and PID controllers across different velocities and road adhesion conditions. Notably, in the S-curve test, Tube-RMPC reduced the peak tracking error by 20.5% and RMSE by 36.2% compared to PID; meanwhile, in the double lane change scenario, its RMSE improvements reached 71.17%.  
With its computational efficiency and robustness, Tube-RMPC offers a viable solution for real-world autonomous driving applications. Future research could explore the integration of learning-based adaptive mechanisms to further enhance control performance and extend the framework to cooperative vehicle control for multi-agent coordination in dynamic environments.  
Conceptualization, Y.C. and Y.S.; methodology, Y.C.; software, Y.C.; validation, Y.C., Y.S., and J.L.; formal analysis, Y.C.; investigation, Y.C.; resources; data curation, J.H.; writing--original draft preparation, Y.C.; writing--review and editing, Y.S. and J.L.; visualization, C.H.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was funded by \"The Guangxi Key R&D Program, Grant No. Gui Ke AB23026104\", \"The Liuzhou Science and Technology Program, Grant No. 2023 PRJ0102\", \"The Construction of Guangxi Construction Machinery Low Carbon Digital Intelligence Technology Innovation Centre, Grant No. 2023 PRJ0101\" and \"Autonomous Research Project of Guangxi Key Laboratory of Auto Parts and Vehicle Technology, Grant No. 2023 GKLACVTKF05\" All experiments in this paper were conducted at the Guangxi University of Science and Technology.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
## 4 Deployment on SELENE Platform
### SELENE AI HW Accelerator  
The HLSinf accelerator is a high-level synthesis open-source FPGA accelerator which creates an efficient hardware IP for ASIC or FPGA targets and is used for inference processes of AI models based on convolutions. The central characteristic of this accelerator is flexibility, as it allows a specific AI hardware accelerator to be designed and implemented to the particular use case.  
It is designed using the channel slicing concept, where a set of input channels are processed in parallel, and a set of output channels are produced in parallel. This allows the programmer to select the degree of parallelism at the design time, where a bigger parallelism implies a bigger accelerator size and more FPGA resources. This speed-up flexibility allows the user to define the best well-suited parallelism considering the available FPGA resources and the degree of parallelism desired.  
This accelerator has been integrated into the SELENE SoC and interconnected with memory and the RISC-V cores using an AXI interconnect. This HLSinf accelerator on the SELENE platform can be customized to support specific data formats and Neuronal Network (NN) layers and currently supports several well-known AI models such as YOLOv3/4, Tiny-YOLO, or VGG16. The accelerator and the CPU cores share the same memory, which minimizes the cost of data movement and allows fine-grain HW/SW co-designs of the AI algorithms between the RISC-V cores and the AI accelerators to be performed. In addition, HLSinf has been designed to run in the EDDL library, providing the support needed to run offloaded AI model layers on the FPGA. It can configure and compile a given subset of network layers for use in an inference process running with EDDL. HLSinf and EDDL allow a perfectly coupled HW/SW co-design approach where some parts of the model run in the FPGA, whereas the rest run in the CPU or GPU when available.  
Figure 3 shows the design of the accelerator in the SELENE Platform. This has been defined around the dataflow model using modules interconnected by data streams. This dataflow model accelerates the overall throughput of the design as it enables task-level pipelining, permitting several operations to start before the previous functions have completed all of their operations.
## 3 Results
### Validation
#### 3.3.1 Application Scenario-Test Bench Interconnection  
Objective: this scenario aims at showing the applicability of the EtherCAT based I/O within other Industry 4.0 adaptations in the test benches, namely the connection of different test benches across enterprise boundaries. The setup was operated, using interconnection with different benches, to show the application of interconnection, configuration and automation standards. The network comprised five test benches of different test bench suppliers. A dedicated test bench was used inside the network for the test script execution. The EtherCAT I/O interface was used only to collect the data and to loop it back to the network (see Figure 11).  
Boundary conditions: the focus of this article is not the interconnection. It is only on the behavior of the I/O.  
System under test description: no actual system under test was used. A dummy system was putting the data back in the loop.  
Figure 10: Configuration of the I/O with the ICD data.  
Test scenario: Figure 11 shows the rough architecture of the test setup. Within the connection of several test benches, only two were communicating. Test bench _Execution_ was leading the test. That means test bench _Execution_ starts the test, addresses the relevant test bench (I/O), and puts it in operation mode. The transition to the operation mode includes the receiving of the configuration for the syntax of the I/O.  
Result: the test included a permanent transmitting and receiving of variables until the user stops. No variables were lost, no variables were received after the loop back with a wrong syntax.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 4 SIF Retrieval Methods in Space Scale
### The Physical Methods
#### 4.2.2 Differential Optical Absorption Spectroscopy (DOAS)  
The DOAS method is designed to measure the specific narrow-band absorption structures of trace gases for the UV and visible spectral range and determine the gas concentrations [104]. Considering chlorophyll fluorescence is a trace gas, the DOAS equation is rewritten in the following form [47], and the parameters are introduced in Table 4:  
\[-\ln\frac{L(\lambda,\theta)}{E^{g}(\lambda,\theta)}=\sum_{n=1}^{N}\sigma^{ \prime}_{n}(\lambda)S_{n}+\sigma_{Ray}(\lambda)S_{Ray}+\sigma_{Mie}(\lambda)S _{Mie}+\sigma_{f}(\lambda)S_{f}+\sum_{m=1}^{M}a_{m}\lambda^{m} \tag{15}\]  
In the DOAS method, \(\sigma_{f}(\lambda)\) is considered as a pseudo-emission across the section, while \(S_{f}\) acts as a fluorescence column relative to the emission cross section [47]. Through this hypothesis, the fluorescence can be obtained. Due to the lack of deep absorption features of oxygen and water vapor near the Fraunhofer lines, a 745-758 nm fitting window was chosen in [PERSON]'s study [47]. Rayleigh  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Meaning** \\ \hline \(S_{n}\) & The density of the absorber \\ \(\sigma^{\prime}{}_{n}(\lambda)\) & Rapid part of the absorption cross section of the absorber \\ N & Number of absorbers \\ \(\sigma_{Ray}(\lambda)\) & Reference spectra of Rayleigh scattering \\ \(\sigma_{Mie}(\lambda)\) & Reference spectra of Mie scattering \\ \(\sigma_{f}(\lambda)\) & Reference spectra of fluorescence \\ \(\sum\limits_{m=1}^{M}a_{m}\lambda^{m}\) & Low-order polynomial, in which \(a_{m}\) is the coefficient of the polynomial, and \(\lambda^{m}\) is the wavelength. \\ \(S_{f}\) & Fluorescence fit factor \\ \hline \end{tabular}
\end{table}
Table 4: Parameters used in Equation (15).  
and Mie scattering are removed using a low-degree polynomial that is also fitted. Equation (15) can be simplified as follows [47]:  
\[-\ln\frac{L(\lambda,\theta)}{E^{g}(\lambda,\theta)}=\sigma_{f}(\lambda)S_{f}+ \sum_{m=1}^{M}a_{m}\lambda^{m} \tag{16}\]  
In Equation (16), \(L\) and \(E^{g}\) are known, \(\sigma_{f}(\lambda)\) is the fluorescence spectrum, \(m\) is set to 3, and the coefficients \(a_{m}\) and \(S_{f}\) are fitted using the least squares algorithm.  
This method needs at least one fluorescence spectrum. The fluorescence input spectrum, which can be obtained through simulation, has an effect on the result. On the right of Equation (16), the first term is small compared with the last term. This case can result in a large error.
## 5 DRLAttack in the Black-Box Setting
### Construction of Spy Users  
In Section 3, we proposed that users can be divided into two categories by analyzing the training dataset: active users and inactive users. This classification is based on the frequency of interactions with the recommendation system and rating behaviors. By creating spy users, the attacking agent simulates the interaction behaviors of both types of real users. This allows the collection of feedback on the recommendation lists provided to these spy  
Figure 2: DRLAttack’s autonomous attack process.  
users, thereby guiding the agent to improve attack strategies and achieve the objective of promoting target items.  
Active users are those who tend to frequently interact with the recommendation system and give positive ratings to recommended items, such as people who are particularly fond of watching movies, shopping, or listening to music in real life. Given that the rating system is a 5-point scale, we select the median rating (3 points) as the threshold to identify active users. Here, a rating of 1 indicates the lowest level of activity, while a rating of 5 indicates the highest level of activity. Active users are defined as those with an average rating value of 3 or higher:  
\[U_{active}=\{u|\frac{1}{|I_{u}|}\sum_{i\in I_{u}}r_{ui}\geq 3\} \tag{10}\]  
where \(I_{u}\) represents the set of items that user \(u\) has interacted with, and \(r_{ui}\) is the rating value that user \(u\) gives to an item \(i\).  
For implicit datasets, where the interaction between users and items cannot be measured by specific rating values, user activity is distinguished by counting the number of items each user has interacted with. To start, calculate the average number of items each user interacts with, represented as \(\bar{N}_{inter}\), as follows:  
\[\bar{N}_{inter}=\sum_{i=1}^{U}N_{inter,i}/U \tag{11}\]  
where \(U\) represents the total number of users, and \(N_{inter}\) is the number of items that user \(i\) has interacted with. Based on \(\bar{N}_{inter}\), whether user \(i\) is considered an active user can be determined by the following logical expression:  
\[U_{active}=\{u_{i}|N_{inter,i}\geq\bar{N}_{inter}\} \tag{12}\]  
As this paper focuses on a data poisoning attack targeting specific items, users who have already interacted with the target item are not considered. Therefore, we first compile a list of users who have not interacted with the target item, represented as \(U_{*}^{A}\). From this set \(U_{*}^{A}\), we then identify subsets of active users \(U_{active}\) and inactive users \(U_{inactive}\). Through our design, each spy user's interaction records not only simulate the behavior patterns of real users but also incorporate a small number of randomly selected items to enhance the diversity and randomness of their actions, thereby reducing the risk of detection and removal. This approach maintains the representatives of spy users while enhancing their stealth, providing reliable reward feedback signals for subsequent attack strategies.
## 3 Results
### Correlation Analysis between Vegetation Change and Climate Variables  
To have a general view and to understand the spatial relationship, first, a Pearson correlation coefficient (PCC) analysis was run based on mean annual values of NDVI, Precipitation and LST (2000-2019). Figure 5 shows the PCC for mean annual NDVI, precipitation and land surface temperature (LST). The pairwise plots in the graph indicate the correlation pattern as positive or negative (orientation of the plots). The first subplot (Figure 5) demonstrates a strong and positive PCC between annual mean NDVI and annual total precipitation (r = 0.88). The second subplot shows a strong and negative correlation  
Figure 4: Annual mean NDVI trends per land cover type (**a**) and per agroecological zones (**b**) over the Horn of Africa during 2000–2019.  
between annual NDVI and LST (r = \(-\)0.84). The third subplot illustrates the relation between annual total precipitation and annual mean LST (r = \(-\)0.82). Thus, vegetation dynamics was strongly associated with precipitation and land surface temperature.  
There is about 87.6% (1,728, 738 km\({}^{2}\)) of the study area showing a strong and negative correlation between annual mean NDVI and LST (Figure 6a) of which 39.52% (683,197 km\({}^{2}\)) is statistically significant at \(p<0.05\). Based on zonal statistics, the correlation between NDVI and LST was the strongest and negative in Kenya, while the correlation was only positive in Djibouti. Similarly, based on agroecological zones, the correlation between NDVI and LST was the most significantly and negatively correlated in the tropical warm-subhumid zone, and the weakest negative correlation was observed in the tropical cool-humid zone.  
About 93.1% (1,848,823 km\({}^{2}\)) of the study area is also showing a strong and positive correlation between annual mean NDVI and annual total precipitation during the last two decades (Figure 6b) of which 28.85% (533,385 km\({}^{2}\)) is significant. The correlation between NDVI and precipitation was the strongest in Kenya and Eritrea, while it was the lowest in Djibouti. Likewise, the correlation between NDVI and precipitation was the most positively correlated in the tropical cool-arid zone, and the weakest positive correlation was observed in the tropical cool-humid zone.  
Figure 5: Pearson correlation coefficient (PCC) based on mean annual values of NDVI, precipitation, and LST during 2000–2019 in the Horn of Africa.
# A Low-Cost Secure IoT Mechanism for Monitoring and Controlling Polygeneration Microgrids
## 3 The Proposed System Architecture
### The IoT Security Protection Scheme
#### 3.2.3 The Application Layer  
The application layer provides global management of the application based on the object's information processed in the database [35]. This layer is composed of a monitoring mobile application for Android. The security of this layer comes from all the security implemented in the perception and network layers and the authentication that is implemented in the mobile application to certify that is the user who is accessing the data and not an intruder. The application has a two-step authentication procedure to register a new user and a fingerprint authentication to log-in this user; both dashboards are shown in Section 4 of this paper.  
Therefore, a home energy management mobile application for Android is developed. This application retrieves the consumption and generation data from the cloud database which allows the user to track their energy consumption and generation, providing information for scheduling some activities might demand more energy (e.g., use the washing machine when the PV and turbine are  
Figure 4: Proposed IoT security protection scheme.  
generating more energy). A listener is implemented to refresh the consumption/generation graphs in real-time. In addition, the application lists all the monitored equipment with the option to be switched on and off to avoid energy waste when are being left on and nobody is at home. The user has the option to delete or register a new device by providing basic information about it as well. In addition, the application lists all the monitored equipment with the option to be switched on and off to avoid energy waste when they are left on and nobody is at home.  
For security purposes, a two-step sign up verification was implemented with the user cell-phone number. After the sign up, the user has the option to log-in using his fingerprint if desired. Both features are explained in detailed in Section 4.4.1. The application dashboard and some capabilities are shown in Figure 5.  
Figure 6 shows the application interface listing the devices that the user has registered. The required information to register a new device is presented as well. A threshold value to be aware of an excess of power consumption could be included in order to turn-off the device when the power consumption reaches such threshold.  
Figure 5: Mobile application capabilities.