esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Meteorological Analysis of the 28 December 2020 Sea Storm Event  
### Meteorological Background at Synoptic Scale  
The analyzed meteorological event was triggered by an intense storm coming from the Northern Atlantic, generated on 26 December east of Greenland and then rapidly moving toward the British Isles and North-western Europe. Satellite images and meteorological maps (sea surface barometric field and 500 hPa geopotential height) well represent the rapidity in translation of the system from the genesis area to the Mediterranean (Figure 5).  
Meteorological map and satellite images from 26 December 2020-12:00 UTC (Figure 5a,d) show the low pressure centered south of Iceland, with a barometric minimum of 965 hPa in the inner part and a 500 hPa geopotential height less than 4850 m over Iceland.  
Satellite image relative to 27 December 2020-12:00 UTC (Figure 5b,e) shows the low pressure centered over Scotland, with a barometric minimum of 958 hPa at the center and a 500 hPa geopotential height equal to 4900 m. These values are representative of an intense mid latitude cyclonic system.  
Figure 4: Sea level pressure maps from 4 November 1966 (**a**), 23 December 1979 (**b**), and 11 January 1987 (**c**). In all cases, a deep Mediterranean low, centered around Sardinia, is active with relevant horizontal barometric gradients on Italy and surrounding seas, triggering strong southerly winds (ERA-20C-1.000\({}^{\circ}\) (European Atmospheric Reanalysis of the 20 century). Maps downloaded by www.wetterzentrale.de, accessed on 6 September 2021.  
Figure 5: Meteorological maps (CFS reanalysis 0.500\({}^{\circ}\) by wetterzentrale.de) (**a–c**) and satellite images (EUMETSAT 2020) (**d–f**) reconstructing the three-day evolution ((**a,e**), 26 December; (**b,d**), 27 December; (**c,f**), 28 December) of the storm during its transition from Iceland to the Mediterranean Sea. Maps downloaded by www.wetterzentrale.de, accessed on 6 September 2021.
## 1 Introduction
We verify the performance of the algorithms through simulation, and the results indicate that the proposed algorithm outperforms several benchmark algorithms, and it is effective in improving long-term overall system energy efficiency. The remainder of this paper is organized as follows. Section 2 introduces the related work in recent years. Then, Section 3 introduces the system model and problem formulation. Section 4 presents the solution approach, and Section 5 verifies the algorithm performance through simulation. Finally, Section 6 provides conclusions and future prospects.
## 5 Conclusions  
This study developed a novel model that incorporates an improved attention mechanism, utilizing efficient one-dimensional convolution to generate channel weights. This design significantly reduces the number of parameters while avoiding the introduction of dimensionality reduction operations, thereby enhancing the model's efficiency. The features extracted through path weight selection were input into the RNN and the enhanced attention module to further improve the model's predictive performance. Experimental results demonstrated that the model achieved excellent predictive performance on the condition monitoring dataset. In the future, this study plans to adopt adaptive techniques to further optimize the network model. For example, a multi-scale adaptive attention mechanism [48] was introduced to extract information from both minor fluctuations and the long-term stability of equipment, thereby enhancing the model's capability to predict stability under different operating conditions. Additionally, the research team plans to collaborate further with West China Hospital to collect operational data from ventilators, monitors, and portable extracorporeal devices and apply the proposed method to the prediction of medical equipment stability.  
Conceptualization, X.L. and M.W.; methodology, X.L. and M.W.; software, X.L.; formal analysis, X.L.; investigation, X.L. and M.W.; resources, M.W.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L. and M.W.; visualization, X.L.; supervision, M.W.; project administration, M.W.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Project No. 2022 YFC2407600, Project No. 2022 YFC3601000).  
Not applicable.  
The research in this paper uses a publicly available dataset. The dataset can be downloaded from the following link: [[[https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset](https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset)]([https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset](https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset))] (accessed on 23 March 2025).  
Figure 9: Network performance with different K. (**a**) represents the MAE for bearings 1-3 to 1-7 with different K values, (**b**) represents the RMSE for bearings 1-3 to 1-7 with different K values.
# Fast 3D Semantic Mapping in Road Scenes +
## 4 3D Semantic Mapping  
### 2D Scene Parsing  
We explore the DeepLab-v3+ deep neural network proposed by [PERSON] et al. [7]. Two important components in the DeepLab series are the atrous convolution and atrous spatial pyramid pooling (ASPP), which enlarge the field of view of filters and explicitly combine the feature maps at multiple scales. The improvement in the DeepLab-v3+ involves the encoder-decoder structure and the augmentation of ASPP module with image-level feature. The former is able to capture sharper object boundaries by regaining the spatial information, while the latter encodes multi-scale contextual information to capture long range information. These contributions make DeepLab successfully handle both large and small objects and achieve a better trade-off between precision and run-time.  
For the semantic segmentation of road scenes, we exploit the Cityscapes dataset and the KITTI dataset and adopt the predefined 19-class label space \(\mathcal{L}=\{l_{1},l_{2},\ldots,l_{19}\}\), which contains _Road_, _Sidewalk_, _Building_, _Wall_, and so on. We use all semantic annotated images in the Cityscapes dataset for training and fine-tune the model with the KITTI dataset. Note that there is not any depth information involved in the training process. In the inference, we keep the original resolution of input image according to different datasets.
## 6 Conclusions  
T\({}_{2}\) spectrum distribution curves, porosity, pore size, and NMR imaging of saturated sandstones with different axial compression ratios were studied using NMR, and the main conclusions obtained are as follows:  
1. When the axial compression ratio is less than 80%, the rock interior mainly contains micropores, and the growth of the mesopores and macropores is low. The variation rate of mesopores and macropores shows a significant increase when the predefined stress is more than 0.8 UCS, which is due to the rapid generation, expansion and transgression of pores. There is a critical value in the process of rock damage (0.8 UCS), and when it is less than this critical value, the microstructure in the rock is mainly composed of pores and micro-cracks, and the length is generally less than 5 \(\upmu\)m. When it exceeds this value, there are obvious cracks or even groups of cracks inside the rock.
2. The variation in porosity can be divided into three stages, with an overall \"\(\surd\)\" shape tendency in porosity growth. The growth trend is mostly flat when the axial compression ratio is between 0% and 70%, which is mainly because the interior of the rock is dominated by micropores, and the growth of macropores and mesopores is low. The porosity in sandstone increases with an increasing axial compression ratio; however, due to the small size and number of macropores and mesopores, the porosity growth rate is very low; the variation rate shows a significant increase when the axial compression ratio reaches the damage threshold.
3. The pore structure of a rock can be visually presented using SEM and NMR imaging. The distribution mode of pores changes from separated points to thick patches and finally to connected networks with an increase in predefined stress.  
B.T.: investigation, methodology, writing--original draft, writing--review and editing. X.D.: data curation, investigation, methodology, project administration, writing--original draft. C.P.: investigation, methodology, writing--original draft, writing--review and editing. X.M.: investigation, writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Shandong Energy Group. Author [PERSON] was employed by the company Energy Administration of Shandong Province. The companies 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.
# A Gamified AI-Driven System for Depression Monitoring and Management
## 4 Results: Simulation-Based Evaluation of Depression Detection Models
### Machine Learning Pipeline
#### 4.3.4 Visualization and Feature Importance  
Figure 5 presents the ROC curves for all models. The neural network achieved the best area under the curve (AUC), followed closely by random forest. The confusion matrices shown in Figure 6 offer additional insight into each classifier's predictive performance and behavior, highlighting their strengths and weaknesses in classifying the data.  
Figure 4: Feature importance based on permutation analysis. Mood score, game accuracy, and temperature are among the top contributors to prediction performance. This highlights the value of integrating behavioral, cognitive, and environmental data.  
Figure 5: ROC curves for all three models. The neural network shows the highest AUC, indicating strong discriminative performance. Random forest also performs well across all thresholds. Logistic regression has lower sensitivity, especially at lower thresholds.  
To assess the contribution of individual features to model predictions, we applied permutation-based feature importance analysis, which measures performance degradation when a feature's values are randomly shuffled. Figure 4 shows results across all three models, using the F1 score as the evaluation metric.  
Across models, _temperature_, _humidity_, and _mod score_ emerged as the most influential predictors. These findings align with research linking psychomotor retardation, affective disturbances, and cognitive dysfunction to depression. In contrast, _temperature_ and _humidity_ had lower importance, indicating a more indirect influence in the simulated context.
# A Predefined-Time Control for the Laser Acquisition in Space Gravitational Wave Detection Mission
## 2 Acquisition Scheme  
### Satellite Platform Description  
Almost all the current GWs detection programs have three satellites, and each has two telescopes, which forms an approximate equilateral triangle constellation with three laser interferometers. Obviously, there are three two-way laser links; namely, three arms need to be established. The two telescopes on one corner of the triangle, together with the corresponding telescope on the other two satellites, constitute two of three giant Michelson-Type interferometers through two-way laser links. The third arm is necessary to give independent information on the two polarizations of GWs for redundancy. These interferometers will detect low frequency GWs by measuring the distance change between the two released test masses which are in drag free mode. Because of the orbital perturbation, the constellation will no longer be a standard equilateral triangle configuration. That is to say, the angle between two telescopes will have a small angle change which will no longer be 60\({}^{\circ}\). Therefore, the telescopes need to adjust their point direction to compensate for the angle change with a very high pointing accuracy. This also indicates that one satellite will track the other two satellites simultaneously which means each satellite needs at least four pointing degree-of-freedoms (DOFs). There are two alternatives which are depicted in Figure 2:  
1. By combining mechanisms controlling the one angle DOF between the telescope with the micro-thrusters controlling the three attitude DOFs of the entire satellite; this scheme is shown in Figure 2a;
2. By two-axis mechanisms controlling each telescope with two DOFs; this scheme is shown in Figure 2b.  
In this paper, we chose the second scheme which is described in detail in Figure 3. The telescope 1 (T1) of satellite A (SA) pointing to the satellite C (SC) is credited as L1, the telescope 2 (T2) of SA pointing to the satellite B (SB) is credited as L2, the T2 of SB pointing to SC is credited as L3 and the opposite directions are credited as L1\({}^{\prime}\), L2\({}^{\prime}\) and L3\({}^{\prime}\), respectively. There are three two-way laser links in total: L1/L1\({}^{\prime}\), L2/L2\({}^{\prime}\) and L3/L3\({}^{\prime}\).
# Application of the Reed-Solomon Algorithm as a Remote Sensing Data Fusion Tool for Land Use Studies
## 2 Methods and Materials
### Reed-Solomon Codes (RS Algorithm)
#### 2.1.2 Deeper Insight into the Reed-Solomon Algorithm  
Reed-Solomon codes are a group of error-correcting codes that were introduced by [PERSON] and [PERSON] [77]. They have many applications, the most prominent of which include consumer technologies such as CDs, DVDs, Blu-ray Discs, QR Codes, data transmission technologies, such as DSL and WiMAX, broadcast systems, such as DVB and ATSC, and storage systems, such as RAID 6. They are also used in satellite communication [78]. The vital and robust method of RS codes are applied in cryptography, steganography and against the salt and pepper noise of medical images [79; 80; 81].  
The original Reed-Solomon algorithm is a simple encoding procedure of a message as a sequence of coefficients in the formula (Equation (1)) [78]:  
\[p_{x}(a)=\sum_{i=1}^{k}x_{i}a^{i-1}. \tag{1}\]  
Assumption: \(a\), \(k\), and \(x\) are positive integers and \(x\in\) <0,1,..., \(a\)>.  
Without describing in depth the mathematical proof and only observing the empirical values of the above formula application, it was found that knowledge of \(a\) and \(k\) parameters meant the obtained value of \(p_{x}(a)\) could be decoded into the exact set of input of positive integer values \(x\) using only the recursive procedure of Euclidean divisions and recording the remainders. This is the practical application of the Chinese remainder theorem.  
Value \(p_{x}(a)\) can be a huge positive integer, which can encode integer values (also representing nominal values). The method is universal, and the only limit is computer resources. Value \(p_{x}(a)\) is reversible. It is possible to reconstruct (recalculate) the original input of different sets of integers representing, for example, a nominal or interval scale only on the basis of certain parameters \(k\) and \(a\), assuming that these input values are positive integers and value \(x\in\) <0,1,..., \(a\) >.
# Application of the Random Decrement Technique in Damage Detection under Moving Load
###### Abstract  
This paper employs the random decrement technique as an output-only method to detect damage from the acceleration signals under a moving load. The random decrement technique is an especial averaging method that produces Random Decrement Signatures (RDS). For this purpose, Arias Intensity (AI) was employed to calculate the energy content of each RDS and substitute each acceleration signal by a scalar invariant value. Normalizing AIs, all RDSs were then updated so as to show a unique energy along the undamaged structure. Once the normalizing factor was computed for the intact structure, the damage was determined by the absolute difference of normalized AIs obtained from each individual RDS along the structure simultaneously. To verify the proposed method, two experimental models of a simply supported beam and a scaled arch bridge were developed under a moving load (vehicle simulation), and acceleration data were recorded. The results of laboratory models proved that the RDSs can accurately detect the damage location using the normalized AI without applying any further frequency filtering. This method needs neither the damage location nor modal parameters in advance, and could properly work in a noisy environment as well.  
random decrement technique; acceleration data; moving load; damage detection; Arias intensity +
Footnote †: journal: Applied Physics Letters
## 4 Results
### Potential Real-World Applications of FL-DSEA  
FL-DSEA proposes a vigorous solution for improving the security of IoT nets for a wide range of cyber threats. In smart homes and cities, FL-DSEA can protect interrelated devices from DDoS assaults by detecting abnormal traffic patterns without risking user data secrecy. In healthcare, this system can secure critical medical devices and patient data from malicious and forbidden access, ensuring the integrity and confidentiality of crucial health data. Furthermore, FL-DSEA can strengthen industrial systems against APTs in industrial IoT settings by unceasingly appraising and refining security models founded on real-time datasets from varied edge/endpoint devices. By exploiting distributed learning, FL-DSEA can offer compliant and robust security procedures personalized to the explicit requirements of several IoT settings, thus improving IoT networks' inclusive robustness and dependability.
# Landslide Susceptibility Assessment by Using Convolutional Neural Network
## 4 Results and Discussion
### Discussion  
As mentioned, the main uncertainties in the susceptibility assessment are related to the inventory dataset that belongs to the data gathering, historical landslides, site information, etc., which requires high definition and experience to minimize the error. An approach has been re-evaluated to overcome such uncertainties. After providing the detailed dataset, the CNN-based model was implemented to estimate the susceptibility of landslides in the Gorzineh-khi region. The algorithm used the DEM and Landsat TM8 satellite data to evaluate the hazard susceptibility zonation in the studied area. The main landslide inventory dataset was divided into the training (80%) and testing (20%) sets randomly and used in the analysis. The CNN predictive model was utilized to produce a susceptibility map for the Gorzineh-khi region with appropriate accuracy. The results show a considerable increase in landslide susceptibility prediction accuracy compared to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Justification** & \multicolumn{3}{c}{**Assessment Score**} & \multicolumn{1}{c}{**Accuracy**} \\ \cline{2-5}
**Method** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline SVM & 0.70 & 0.71 & 0.70 & 0.71 \\ k-NN & 0.65 & 0.61 & 0.61 & 0.65 \\ DT & 0.60 & 0.63 & 0.65 & 0.63 \\ CNN & 0.73 & 0.75 & 0.77 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Justification results for the proposed landslide susceptibility model.  
Figure 15: ROC results for all machine-learning models in susceptibility assessment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier** & **MSE** & **MAPE** & **RMSE** \\ \hline SVM & 0.2237456 & 0.3449610 & 0.2365904 \\ k-NN & 0.1307752 & 0.1975660 & 0.1469843 \\ DT & 0.2359663 & 0.2275315 & 0.2085891 \\ CNN & 0.0135980 & 0.0102987 & 0.0175438 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The error table for different machine-learning modeling.  
the benchmark models. Regardless of the higher accuracy achieved by the model than other classifiers, this study is associated with some limitations that could be considered in future research. These limitations can be categorized as:  
* The primary database was provided based on reports, historical landslide locations, field surveys, and remote sensing data, which is challenged by a limited personal budget;
* The triggering factors' data is highly dependent on the satellite imagery resolution and DEM data quality, which affect directly the primary dataset; and
* Deep learning requires strong processing hardware to conduct accurate mapping.  
Nevertheless, by using the CNN model, the susceptibility map of the studied area was obtained. The model was verified by using the SVM, k-NN, and DT classifiers. To justify the model, loss function, error table (MSE, MAPE, and RMSE), and ROC were used. According to the results, the proposed model has the highest accuracy value.
###### Abstract
Typically, DOR\({}_{1}\) signals are employed to resolve time delay ambiguity while DOR\({}_{2}\) signals facilitate high-precision interferometric time delay acquisition. The clock tone frequency of the regenerative pseudo-noise is roughly \(\pm\)0.5 MHz (1 Mchips/s) or \(\pm\)1 MHz (2 Mchips/s) from the downlink carrier, resulting in an effective bandwidth of 1 MHz or 2 MHz, which closely matches that of the DOR\({}_{1}\) beacon signal. Therefore, it becomes feasible to simultaneously achieve regenerative pseudo-noise ranging and \(\Delta\)DOR [26]. To this end, a novel interferometry processing method that combines pseudo-noise ranging and DOR signals is presented. This approach employs the pseudo-noise ranging clock component to solve the interferometry delay ambiguity, subsequently deriving the final interferometry delay through the joint processing of the downlink carrier and DOR\({}_{2}\) beacon signals. Notably, this innovative method repurposes the regenerated pseudo-code ranging signal, facilitating the integrated application of both ranging and DOR beacon signals, thereby enhancing the comprehensive utilization rate of the downlink signal power. In the case of the limited downlink signal power of spacecraft, the proposed method may improve the measurement accuracy or enlarge the detection distance, providing direct technical support for the efficient use of downlink signal power and for the design of a TT&C downlink signal structure in a weak-signal scenario such as interplanetary deep space exploration. This paper is structured as follows. Section 2 delineates the fundamental principle of \(\Delta\)DOR, a prevalent VLBI technology system, and provides an analysis of its error budget. Section 3 offers a comprehensive presentation of the downlink signal spectrum structure of spacecraft and the proposed method. And Section 4 analyses the performance of the proposed method, contrasting it with traditional methods for comparison. Subsequently, Section 5 further validates the proposed method on the basis of measured interferometry data from the China Deep Space Network (CDSN). Finally, the conclusions are presented in Section 6.