esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 5 Evaluation  
The experimental implementation of our proposed hiding method was initially evaluated in a test environment that simulated a real 5G network. The test setup consisted of virtualized open source software components and a simulated radio interface based on the networking library ZeroMQ. Open5 GS was deployed as the 5G Core, and the gNB from srsRAN Project was used as the 5G RAN. The UE was simulated with the srsUE from srsRAN 4G. For the experimental tests, the covert sender was located in the gNodeB and the covert receiver in the UE. The overt traffic stream was generated with iperf. For different bandwidths as well as different intervals of the covert transmission, the covert capacity was evaluated (cf. Figure 3). Additionally, these tests were conducted with a real radio interface. The basic test setup was not changed. Only the virtualized radio interface was replaced with a physical radio interface provided through software-defined radios. For the evaluation of the proposed covert channel, two USRP B210s were used in the test environment.  
The covert capacity was proportional to the bit rate of the overt traffic. The percentage of covert bits in the data stream was 0.0075% for the 5 PDU interval, 0.0037% for the 10 PDU interval, and 0.0025% for the 15 PDU interval. Therefore, the results of the experiment can easily be transferred to higher bit rates such as 100 Mbps or 1 Gbps. Furthermore, the covert capacity obviously decreased with increasing intervals between the covert transmissions. However, the experiments also showed that the same covert capacity was achieved for transmissions with random intervals equally distributed between 5 and 15 PDUs, i.e., with a mean of 10, and for transmissions with a static interval of 10 PDUs between successive transmissions of covert data. The advantage of the randomized intervals is primarily the irregularity of the covert transmission, which improves undetectability. In practical terms, this means that at a bandwidth of 20 Mbps, approx. 815 words with 5600 characters can be transmitted via the covert channel in one minute with randomized intervals between 5 and 15 PDUs. Nevertheless, it is necessary to modify the software of the base station or the mobile device to implement the proposed hiding method. This is a major challenge in the field, as only proprietary closed source products are currently used in public mobile networks. Therefore, the proposed covert channel can only be exploited by the vendor of  
Figure 3: Average covert capacity at different bandwidths of overt traffic.  
the base station software, e.g., for covertly leaking information that is only accessible for an internal attacker.
## 5 Conclusions  
Based on the four time-domain solution formulations derived by Farassat, the recently developed FODNOPP, with four corresponding codes, can accurately calculate aerodynamic discrete noise for single- and counter-rotating open rotors with subsonic/transonic/ supersonic helical tip speeds.  
The maximum harmonic sound pressure level of the overall noise generated by both propfans of the counter-rotating propfan model CRPFc, with a transonic helical tip Mach number 1.13, is 112.5 dB. Furthermore, the maximum harmonic sound pressure level of the overall noise of the front propfan of CRPFc is 5.5 dB lower than that of the rear propfan.  
FODNOPP is crucial for exploring the aerodynamic acoustic characteristics of single- and counter-rotating propellers, propfans, and rotorcraft rotors. It is vital for the low-noise design of open rotors in the aerodynamic inverse design problem phase.  
The calculation accuracy of the intersection curves between the blade surfaces and the collapsing sphere in code b and code c requires further enhancement. Additionally, integrating a module of the permeable surface method for computing open-rotor aerodynamic noise into FODNOPP is a potential advancement.  
Methodology, H.W. and P.S.; program, H.W.; CRPFc geometry, Y.Z.; validation, H.W.; writing--original draft preparation, H.W.; writing--review and editing, PS. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
Author [PERSON] was employed by the company Aero Engine Corporation of China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Accessible IoT Dashboard Design with AI-Enhanced Descriptions for Visually Impaired Users
## 3 System Design and Architecture
### Core Components  
#### 3.2.1 Semantic HTML5 and WAI-ARIA Front-End  
The AccessiDashboard front-end is developed with semantic HTML5 markup to maximize native accessibility. Instead of relying on generic <div> elements for layout, it uses appropriate elements such as <header>, <nav>, <main>, <section>, and <footer> to define the page structure. For example, the main content region containing the sensor data visualizations is within a <main role=\"main\"> container, and each distinct panel of information (each sensor or each data widget) is enclosed in a <section> or <article> element with an appropriate heading.  
Data tables (when used to display numeric readings) are marked up with <table> along with <thead> and <tbody> to separate headers and data rows, and <th scope=\"col\"> or <th scope=\"row\"> to properly associate headers with cells. This ensures that screen reader software can read out the row and column headers when a cell is focused, conveying the context of each data point. Additional ARIA attributes like aria-labelledby or aria-describedby link the chart to more detailed descriptions.  
Each sensor \"widget\" on the dashboard is identified with a heading (e.g., an <h2> with the sensor name or type) and contained in a region with an ARIA label. For instance, a section for temperature data is coded as <section role=\"region\" aria-label=\"Temperature Sensor Data\"> wrapping the temperature chart or text. This way, screen reader users can quickly navigate by regions and find the section they are interested in. Interactive controls, such as buttons or selectors for changing views, include aria-label or visible labels. If a button's purpose is not clear from text alone, an aria-label is added (e.g., the button that switches to the accessible view has aria-label=\"Switch to accessible dashboard view\").  
Frequent data updates are handled carefully to not disrupt screen reader users. Because the HTML structure remains consistent (only values or textual descriptions are updated within existing elements), a screen reader can maintain its place. Where dynamic updates occur (like a live-updating status), ARIA live region roles (e.g., role = \"status\") are used so the screen reader announces changes without losing focus. The site avoids auto-focusing or DOM (Document Object Model) [37] reordering during updates, which could disorient users relying on assistive tech.  
Overall, by using semantic elements and ARIA roles, the dashboard makes the implicit structure explicit. This approach lays the groundwork such that, even if the AI description features were not present, the dashboard would already meet a high standard of accessibility compliance, enabling straightforward navigation and content recognition via screen readers.
# Generating Job Recommendations Based on User Personality and Gallup Tests
## 5 Results and Discussion
The next set of results highlights whether certain methods perform better than others for specific work specializations. From what is shown in Figure 13, our proposed approach outperforms the text similarity-based baseline in all specialization areas. However, the weighted Manhattan method is preferred among graduates who work in Business and Management, Education, and Services, whereas the weighted Euclidian method is preferred among graduates who work in Engineering, Information Technology, and Social Sciences. The two methods are equally valued among graduates who work in Natural Sciences, Mathematics, and Statistics. Similar results are found in the radar plot of Figure 14, which displays the respective average score per group of graduates, based on the area they have studied. Once again, the two proposed alternatives outperform the baseline in all areas apart from the group of graduates of Information Technology for which the three methods have comparable performance. Figure 12: The distribution of ratings per method for graduates who work in the specialization they studied and those who do not. While the study provides valuable insights into the performance of job recommendation methods, several limitations should be acknowledged. First, the sample size of 100 graduates, though sufficient for initial comparisons, may lack the diversity and statistical power to generalize findings across broader populations or niche specializations. For instance, subgroups like graduates in Natural Sciences or Information Technology were underrepresented, potentially skewing results (e.g., the comparable performance of methods in IT). Second, the evaluation relied on self-reported scores, which may introduce subjective bias, and the analysis focused narrowly on specialty alignment and domain areas, overlooking other factors like demographic diversity, job market dynamics, or longitudinal satisfaction. Finally, while the proposed methods outperformed the baseline, their reliance on Manhattan and Euclidean metrics may oversimplify the complexity of personality-job alignment. To address these limitations, future work could expand the participant pool to include more graduates across diverse specializations and geographic regions, ensuring robust subgroup analyses. Testing advanced similarity methods (e.g., machine learning-driven  
Figure 14: The performance of the proposed methods against the baseline across the different areas of specialization that the graduates have studied. Figure 13: The performance of the proposed methods against the baseline across the different areas of specialization that graduates work in. metrics, hybrid models combining personality traits with contextual job-market data) could enhance recommendation accuracy. Additionally, incorporating objective metrics (e.g., job retention rates, employer feedback) and longitudinal studies would strengthen validity, while explainability frameworks could clarify why certain methods succeed in specific domains (e.g., Manhattan's dominance in Business vs. Euclidean in Engineering).
# Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network
## 5 Discussion  
When calculating the magnetic moment magnitude from the three components, the error in the magnitude may be smaller than the errors in the individual components if the error signs are opposite. Simulation experiments indicate that when the SNR is greater than \(-5\) dB, the model's performance remains relatively stable. The results indicate that the model proposed upon maintains a reasonable level of accuracy even under low SNR conditions. An advantage of this algorithm is that it relies entirely on scalar magnetic field data, enabling the aircraft to operate with just a single scalar magnetic field sensor, thus reducing hardware requirements. Another advantage is that the model allows for the direct estimation of the target's magnetic moment size and direction. Additionally, the model demonstrates strong stability in the presence of environmental noise, maintaining high accuracy in magnetic moment estimation even under harsh conditions.  
Compared to the STAR algorithm, which requires magnetic field gradient signals from multiple directions around the target, this model uses a simpler signal format and requires fewer sensors. While traditional linear algorithms may struggle to capture the complex relationship between the target magnetic moment and the magnetic anomaly signal, CNN is capable of learning this intricate mapping through activation functions. Furthermore, enhanced stability is provided by the CNN, and it is less sensitive to noise.  
However, due to the two-dimensional structure of the input data, the application scenario may be somewhat limited, with the model primarily suited for the magnetic moment estimation of static targets. In practical applications, multiple factors must be considered to ensure the algorithm's effectiveness. For example, when the target size or  
Figure 15: The error of the model on the test set under different SNRs.  
number is unclear, or when the sensor is positioned too close to the target, the target may no longer be accurately represented by a magnetic dipole model, leading to inaccurate magnetic moment estimation. Furthermore, the scanning area should be designed to capture the most complete signal possible. It is anticipated that future improvements will help reduce the limitations caused by the data structure.
## 4 Methodology
### Data Preprocessing  
Data were acquired using the Google Earth Engine [56] software. The maps were prepared by Arc-Map 10.8 [57]. The S2-MSI L1C, SSP-L2, ERA5-CRP images were preprocessed to extract the parameters from the selected Rawal watershed DEM. GIS clipping tools were used to select the target boundaries from the image to extract the area of interest. A total of 4998 points were extracted from each monsoon month in the time period of July 2018 to August 2022, giving a total of 284,889 or approximately 0.3 M sample points. These sample points were extracted from the Rawal stream network as the watershed region covers a land and water region. To make a dataset with all the features, the four categories of data were joined based on the matching dates and latitude-longitude. The hydrological and topographical data is consistent or stable data that generally remains the same regardless of the time and is joined on the basis of matching latitude-longitude. Once the sample points are extracted and the dataset with the twenty-two parameters is created, a set of preprocessing techniques is performed that include:  
1. Replacing the missing values: The missing values are replaced using imputation techniques. The numerical data is imputed with the average or mean. The categorical data is imputed using the most frequent value method.
2. Replacing the categorical data: The categorical data is converted to numeric form by using the encoding technique. For example, geology (Cenozoic: 1, Upper Paleozoic (Dev, Car, Per): 2), soil type (Be: 1, Rc: 2), lithology (Ss: 1, Sm: 2) and land cover/land use (trees: 10, shrubland: 20, grassland: 30, cropland: 40, built-up: 50, barren/sparse vegetation: 60, snow and ice: 70, open water: 80, herbaceous wetland: 90).
3. Splitting the dataset: The data are split into train and test sets with a 60:40 ratio.
# Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR
## 4 Simulation Experiment
### Planning Trajectory Performance Evaluation
#### 4.2.2 Lateral Offset Validation  
As shown in Figure 15, during the lane change overtaking process, the maximum lateral distance of deviation from the target lane at three vehicle speeds increases with the vehicle speed. This increase ensures a considerable enough safe redundancy distance when the vehicle is driving at high speed. The maximum lateral distance is \(3.96\) m, indicating that when the ego-vehicle and the ambient vehicle are driving side by side, the ego-vehicle approximates the middle of the adjacent lane of the ambient vehicle, which ensures the safety of the vehicle driving in the process of high-speed overtaking.
# Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation
## 2 Point-Mass Model of UAV Dynamics
### Consensus Algorithm for Multi-Agent Formation Control
#### 4.2.1 Basic Information on Consensus Algorithm  
Following [10; 20; 75; 76; 77; 78; 79], we define graph \(\mathcal{G}\) as a pair \((\mathcal{V},\mathcal{E})\), where \(\mathcal{V}=1,\ldots,n\) is a set of nodes (agents) and \(\mathcal{E}\in\mathcal{V}\times\mathcal{V}\) is a set of edges in which each edge is represented by an ordered pair of different nodes. Edge \((i,j)\) shows that node \(i\) is a neighbor to node \(j\), and node \(j\) can receive information from node \(i\). A graph is called _directed_ if, for each \((i,j)\in\mathcal{E},(j,i)\in\mathcal{E}\). The path from node \(i_{1}\) to node \(i_{l}\) is a sequence of ordered edges with the form \((i_{k},i_{k+1})\), \(k=1,...,l-1\). An undirected graph is connected if there are paths to all other nodes for any \(i\in\mathcal{V}\).  
Let graph \(\mathcal{G}\) contains \(n\) nodes. The adjacency matrix \(\mathcal{A}=\big{[}a_{ij}\big{]}\in\mathbb{R}^{n\times n}\) is defined as \(a_{ii}=0,a_{ij}=1\) if \((j,i)\in\mathcal{E}\), and \(0\) otherwise. The Laplace matrix \(L=\big{[}l_{ij}\big{]}\in\mathbb{R}^{n\times n}\) is defined as \(l_{ii}=\sum_{j=1}^{N}a_{ij}\) and \(l_{ij}=-a_{ij},i\
eq j\). A directed tree is a directed graph in which every node has exactly one parent except for one node, called the root, which has no parent and has a directed path to every other node. A spanning tree of a directed graph is a directed tree formed by graph edges that connect all the nodes of the graph. Let us assume that the communication graph \(\mathcal{G}\) is undirected and connected; examples of undirected connected graphs are depicted in Figure 1.  
Assume that \(\xi_{i}\in\mathbb{R}\) contains information about the state of the \(i\)-th agent. For information states with first-order dynamics, the following fundamental first-order consensus algorithm is known:  
\[\hat{\xi}_{i}=u_{i} \tag{59}\]  
where \(u_{i}\in\mathbb{R}\) has the following form:  
\[u_{i}=-\rho_{0}\sum_{j=1}^{n}a_{ij}\big{(}\xi_{i}-\xi_{j}\big{)}, \tag{60}\]  
and \(\rho_{0}>0\) is a certain constant.  
It is assumed for algorithms (59) and (60) that consensus is achieved asymptotically among several agents when for any \(\xi_{i}(0)\) and for all \(i\
eq j\) it is valid that \(\lim_{t\to\infty}\big{\|}\xi_{i}(t)-\xi_{j}(t)\big{\|}=0\).
# Design and Application of a Distribution Network Phasor Data Concentrator
## 5 Actual Installation and Operation  
The designed DPDC is currently located in the Lingang area of Pudong New Area, Shanghai, China. The area has a full-voltage distribution network of 220 kV or less. The distribution network is characterized by typical urban power grids. The feeder lines are cable lines or cable/overhead hybrid lines. It includes substations of various voltage levels, multiple electric vehicles charging stations, multiple photovoltaic power stations, and wind power plants. The DPDCs and \(\upmu\)MPMUs have been installed in some 220 kV substations, 35 kV substations, 10 kV switchyards, and photovoltaic grid points. The distribution network WAMS constructed by the project has functions such as distribution network state estimation, fault diagnosis and location, and island control. Figure 10 shows some actual installation scenes.  
Figure 10: Field installation. (**a**) The front layout of the cabinet; (**b**) a \(\upmu\)MPMU mounted on the column.  
Figure 9: DPDC data delay test.  
(a) Is the installation of equipment in a 35 kV substation, 7 uMPMUs and one DPDC are assembled in two cabinets. (b) Shows a uMPMU installed on the feeder, which is placed in a dedicated box. The information of the line is measured by the voltage and current transformers and the data of the uMPMU is sent to the substation. The WAMS main station of the distribution network is deployed in the Shanghai Pudong Power Supply Company of the State Grid. The D5000-WAMS front-end machine developed by the NARI Group can view real-time data.  
The designed DPDC has been running for several months. During the time, DPDC works stably and the data transmission is normal. The project is still in progress and more DPDCs and uMPMUs will be deployed in the future.
## 7 Conclusions and Future Work  
This study used a stack ensemble deep transfer learning model with data augmentation approaches to develop a high-accuracy brain tumor classification system. The suggested model uses VGG19, AlexNet, and Ensemble transfer learning models to identify and classify tumors from brain images. The proposed study utilized Kaggle data sets divided into four classes: meningioma, glioma, pituitary, and no tumor. The dataset was split into three parts: training (70%), testing (20%) and validation (10%). The recommended model's  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Deep Learning Model** & **Model Accuracy** \\ \hline Ref. [40] & 2D CNN & 96.40\% \\ \hline Ref. [41] & Parallel DCNN & 97.33\% \\ \hline Ref. [42] & 25 Layers CNN & 86.23\% \\ \hline Ref. [43] & Deep CNN & 98.05\% \\ \hline Ref. [44] & One-Stage Deep Learning Model (YOLO) & 95.01\% \\ \hline Ref. [45] & Deep Convolutional Generative Adversarial Network (DCGAN) & 83.20\% \\ \hline Ref. [46] & VGG16 + CNN & 97.30\% \\ \hline Ref. [47] & AlexNet & 98.02\% \\ \hline Ref. [48] & CNN & 98.01\% \\ \hline Proposed Model & Stack Ensemble (AlexNet, VGG19) + & 98.70\% \\ SETL\_BMRI & Data Augmentation & \\ \hline \hline \end{tabular}
\end{table}
Table 4: We compare our proposed model with prior research to assess its effectiveness.  
training phase achieved a high success rate of 97-99%. The VGG19 test model achieved accuracy of 97.02%, compared to 94.05% for the AlexNet test model. The proposed model SETL_BMRI outperforms in terms of performance by achieving 98.70 accuracy. In future studies, several image segmentation methodologies will be utilized to provide the most accurate approximation of impacted brain areas, by distinguishing them from unaffected brain regions.  
Conceptualization, K.M. and S.N.; methodology, S.N. and U.L.; software, K.M. and U.L.; validation, K.M. and S.N.; formal analysis, S.N. and U.L.; investigation, K.M. and U.L.; resources, S.N. and U.L.; data curation, S.N.; writing--original draft preparation, S.N. and M.N.S.; writing--review and editing, S.N., M.N.S. and U.L.; visualization, K.M. and S.N.; supervision, I.A.G., K.M.N. and S.N.; project administration, I.A.G., K.M.N. and S.N.; funding acquisition, M.N.S., I.A.G. and K.M.N. All authors have read and agreed to the published version of the manuscript.  
Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia (project number: ISP-2024).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Dataset of brain tumors: [[https://www.kaggle.com/datasets/masoudickparvar/brain-tumor-mri-dataset](https://www.kaggle.com/datasets/masoudickparvar/brain-tumor-mri-dataset)]([https://www.kaggle.com/datasets/masoudickparvar/brain-tumor-mri-dataset](https://www.kaggle.com/datasets/masoudickparvar/brain-tumor-mri-dataset)), accessed on 30 July 2023.  
The authors extend their appreciation to the Deputyship for Research& Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number ISP-2024.  
The authors declare no conflicts of interest.