esa-sceva/satcom-chunk-collection huggingface dataset:
# Nondestructive Monitoring Techniques for Crack Detection and Localization in RC Elements
## 7 AE Measurements  
### Total AE Activity  
The applied load history along with the accumulated AE events are shown in Figure 5. The number of AE events started to increase rapidly during 4 th and 7 th load steps at approximately 2100 and 4500 s, meaning intensive cracking phenomena occurred at the time of increased central deflection. The major cluster of events comes at the unloading stage, from approximately 6900 s, where a possible AE source mechanism is the friction in aggregates and reinforcing bars due to cracks reclosing.  
The AE activity is first investigated considering the individual sensor recordings and the related statistical distributions of the signal parameters, i.e., peak amplitude, duration and average frequency. Figure 6a shows the distributions of the received signals by sensors AE0 and AE1, and Figure 6b shows those received by sensors AE4 and AE5. Considering the mean values of the distributions,  
Figure 4: Load versus mid-span deflection at the different load steps, from #0 to #8.  
recordings of AE0 and AE1 (located on the top surface of the beam) are characterized by lower frequency, lower amplitude and longer signal duration than AE4 and AE5 (located next to the bottom surface), namely \(<\)\(f\)\(>\) = 17 kHz vs. 31 kHz, \(<\)\(A\)\(>\) = 3.3 mV vs. 4.5 mV, and \(<\)\(\Delta t\)\(>\) = 0.8 ms vs. 0.4 ms. These findings can be explained in terms of amplitude loss (signal attenuation)--stronger for high frequencies--and, possibly, of spreading of AE wave packets traveling in a dispersive medium [24], such as the concrete to some extent. Hence, the measured effects appear to be descriptive of AE activity predominantly originating near AE4 and AE5 sensors, consistent with a crack pattern developing from the bottom surface of the beam.
## 2 Landsat 8 /OLI and Sentinel-2/MSI Characteristics  
The worldwide joint use of L8/OLI and S2/MSI data as input for LULC and LULCC applications occurs because they are: freely available [21], interoperable [29], and able to monitor large surfaces [30]. These sources are placed in sun-synchronous orbits [22] and make similar measurements in terms of spectral, spatial, and angular characteristics (Table 1).  
L8, launched by National Aeronautics and Space Administration (NASA) and United States Geological Survey (USGS), has two sensors: Operational Land Imager (OLI) (30 m of spatial resolution and nine bands) and Thermal InfraRed Sensor (TIRS) (100 m of spatial resolution and two bands) [22]. The temporal resolution is 16 days and the radiometric resolution is 16 bits. The area covered by each scene is 185 \(\times\) 180 km [30]. S2/MSI is a mission with two satellites (S2A/MSI and S2B/MSI launched by the European Union's Copernicus Earth Observation program of Europe Space Agency (ESA) in 2015 and 2017, respectively) [22]. Both S2 satellites carry the MultiSpectral Instrument (MSI), a sensor containing 13 bands and spatial resolution varying between 10 and 60 m at visible to shortwave infrared (SWIR) regions [33]. The S2/MSI mission provides data with a 5-day revisit frequency, 16 bits of radiometric resolution, and swath width of 290 km [30; 31]. Because of their spectral capabilities (including three bands in the Red-edge and two bands in the SWIR), S2/MSI mission provides new mapping possibilities [33; 34; 35]. These bands can be considered more linked to vegetation analysis than L8/OLI bands because this wavelength range is sensitive to chlorophyll content, and highly variable among different crops and different phenological states [36; 37], which can be useful to derive band ratios and to calculate a wide variety of indices, useful for vegetation discrimination and LULC classification [38; 39; 40]. For LULC and LULCC analysis, these benefits can provide more accurate results as opposed to Landsat data which have presented shortcomings from their spatial and spectral resolutions and cloud cover interference due to its 16-day revisit interval [36].  
The integration between L8/OLI and S2/MSI data provides a global average revisit interval of about 3 days [12], which allows surface monitoring with cloud-free observations in some regions [41], and the development of agricultural products at medium spatial resolution [28; 42]. With the Landsat 9, the virtual constellation will become even more frequent approaching a 2-day revisit cycle [43]. This enables the development of methods considering different temporal and spatial resolutions to derive biophysical vegetation parameters and the spectral unmixing of sub-pixel fractions, which permits mapping dynamic processes at sub-hectare resolution [29].
## 5 Results and Analysis
### Thermal Prediction Under Variable Speed Conditions
#### 5.2.2 Influence on Prediction Efficacy of Sample Size and Reference Node Numbers
This aligns with the conclusions drawn from Figure 11, demonstrating that the digital twin-driven hybrid framework utilizing 270 sample data sizes yields parameters with the best predictive validity during the temperature rise test. This is further corroborated by the N_270 scheme's globally minimal average Euclidean distance of 4.9 K across all nodes, as detailed in Table 6. Notably, the elevated Euclidean distances at nodes T3 and T6 may stem from the selected reference nodes (\(T_{ir}/T_{or}\)), as the framework prioritizes minimizing discrepancies at these reference points during thermal prediction. To investigate this hypothesis, the proposed framework was re-evaluated using 180 sample data sizes while varying the number of reference nodes. Comparative results of temperature similarity and average Euclidean distances for these schemes are systematically presented in Figure 15 and Table 7.  
Figure 14: Similarity of temperature nodes with different sample sizes. As illustrated in Figure 15, while the N_Ref_4 scheme, which utilizes four temperature reference nodes exhibits the smaller Euclidean distances between predicted and twin temperatures at specific nodes, the maximum Euclidean distance reaches 21.8 at node T2, and the average Euclidean distance of all nodes reaches 13.7, that indicates the lower prediction accuracy for this scheme. In contrast, the N_Ref_6, N_Ref_8, and N_Ref_10 schemes demonstrate localized improvements in similarity. For instance, the Euclidean distance at node T3 under N_Ref_8 scheme is 2.4, lower than 3.7 of N_Ref_10 scheme, yet this does not signify global superiority, as other nodes within N_Ref_8 exhibit higher deviations. A comprehensive analysis of average Euclidean distances reveals a monotonic reduction from 13.7 (N_Ref_4) to 0.7 (N_Ref_10) as the number of reference nodes increases. This trend empirically validates that augmenting the number of reference nodes enhances the congruence between predicted and twin temperature datasets. Specifically, when the proposed digital twin-driven virtual-real hybrid framework employs a low-speed sample dataset under low-risk behavior for thermal prediction, adopting more reference nodes can improve the accuracy of parameter identification within the LPTN model, thereby accurately predicting bearing temperature of high speed under high-risk behavior.
## 4 Training of MIMO-NARX Meta-Models
### Surrogating NARX Coefficients by Meta-Models  
The MIMO-NARX model trained by the simulation samples is aimed to determine the optimal NARX candidate terms from Figure 9. The corresponding NARX coefficients need to be calibrated based on the system responses in the specific dynamic simulation case. For the \(n_{s}=200\) simulation samples, the five parameters of ground motion and structural masses in Table 2 are considered uncertain factors affecting the dynamic responses. Therefore, the coefficients of the MIMO-NARX model are taken as output variables varying with the five input variables of system uncertainties. The meta-models are then built to surrogate the NARX coefficients using the five uncertain parameters under the condition that the responses of a large number of MCS are unknown as shown in Figure 10. This study explores three meta-models including Kriging, PCE, and APC. The order of 3 is used for PCE and APC as the recommended number of samples is twice the number of polynomial terms.
## 5 Results
### Parameter Analysis
#### 5.2.3 Feature Selection
The impeccable classification outcomes presented in the confusion matrices emphasize the efficacy of the chosen features and highlight the crucial significance of the MRMR FS strategy for attaining such elevated accuracy. These results illustrate the classifiers' discriminatory efficacy and their role in enhancing the accuracy of breast cancer diagnostics. Figure 4 depicts the ROC curves for the LSVM and MGSVM classification algorithms, highlighting the models' outstanding identification efficacy. Both curves attain an AUC of 1.000, indicating flawless sensitivity and specificity. The AUC score indicates that the classifiers exhibit outstanding accuracy and reliability throughout different threshold values, demonstrating their robust efficacy in differentiating between normal and abnormal cases. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **LSVM** & **QSVM** & **CSVM** & **MGSVM** & **CGSVM** \\ \hline
**Sensitivity** & 0.9980 & 0.9960 & 0.9960 & 0.9980 & 0.9960 \\
**Specificity** & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\
**Precision** & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\
**F1-score** & 0.9990 & 0.9980 & 0.9980 & 0.9990 & 0.9980 \\
**MCC** & 0.9980 & 0.9960 & 0.9960 & 0.9980 & 0.9960 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Assessment metrics computed for the SVM classifiers developed with the chosen attributes NNMF FS applied to the combined deep features of the three layers of the three deep neural networks. Figure 3: Confusion matrices for the LSVM and MGSVM classification algorithms after the NNMF approach. Figure 5 represents the feature importance scores determined using the MRMR algorithm. These MRMR scores were calculated by taking into account both the mutual  
Figure 4: ROC curves for the LSVM and MGSVM classification algorithms after the NNMF approach. information between each feature and the class labels (relevance) and the mutual information among features (redundancy). The algorithm selects features iteratively with maximum relevance and minimum redundancy such that the features added are always contributing new information not included in the already selected features. Figure 5 presents the sorted feature importance scores, which reflect how the MRMR algorithm orders features by their contribution to classification accuracy with minimal redundancy. The smooth curve illustrated demonstrates that MRMR is successfully prioritizing the most discriminative features with decreasing returns as less informative features are included. This method avoids overfitting by removing redundant or correlated features from various CNN layers.
# Machine Learning-Based Detection of Cognitive Impairment from Eye-Tracking in Smooth Pursuit Tasks
###### Abstract  
Mild cognitive impairment represents a transitional phase between healthy ageing and dementia, including Alzheimer's disease. Early detection is essential for timely clinical intervention. This study explores the viability of smooth pursuit eye movements (SPEM) as a non-invasive biomarker for cognitive impairment. A total of 115 participants--62 with cognitive impairment and 53 cognitively healthy controls--underwent comprehensive neuropsychological assessments followed by an eye-tracking task involving smooth pursuit of horizontally and vertically moving stimuli at three different speeds. Quantitative metrics such as tracking accuracy were extracted from the eye movement recordings. These features were used to train machine learning models to distinguish cognitively impaired individuals from controls. The best-performing model achieved an area under the ROC curve (AUC) of approximately 68 %, suggesting that SPEM-based assessment has potential as part of an ensemble of eye-tracking based screening methods for early cognitive decline. Of course, additional paradigms or task designs are required to enhance diagnostic performance.  
machine learning; eye-tracking; smooth pursuit; cognitive impairment Article
## 3 Results
### Internal Vertebral Venous Plexus  
The internal vertebral venous plexus (IVVP) was observed as two symmetric longitudinal hyperdense channels, oval in shape, located in the epidural space, dorsal to the vertebral bodies, and ventral to the dual sac (Figure 2). In dorsal reconstruction CT images, it was possible to appreciate their rhomboidal distribution within the vertebral canal (Figure 3). Additionally, CT transverse images obtained at the level of the middle portion of the vertebral body allowed visualization of the basivertebral veins and tributaries of the IVVP (Figure 4). The IVVP between the L1 and L7 vertebrae had an average area of 0.66 mm\({}^{2}\), with a minimum value recorded of 0.43 mm\({}^{2}\), and the maximum of 1.01 mm\({}^{2}\). In verteba L7, the IVVP presented the lowest average with a 0.61 mm\({}^{2}\) (\(\pm\)0.1) area, and L5 vertebrae presented the highest IVVP average area with a 0.74 mm\({}^{2}\) (\(\pm\)0.2) area (Table 1, Figure 5). The IVVP occupies an average of 7.67% of the L1-L7 vertebral canal. In the L1 verteba, there was the highest percentage of the IVVP in relation to the vertebral canal, with 8.9%, while in the L4 verteba, IVVP occupied the lower percentage, with 6.3% of the vertebral canal (Table 2).  
Figure 2: **(1)** Illustration and **(2)** transverse post-contrast CT image at **(3)** the level of L2 verteba in an adult dog illustrating the measured anatomical parameters. The broken line in the photograph indicates the level of the transverse section where the measurements were performed in the L1-L7 vertebral segment. **(a)** Epidural space, **(b)** dural sac, **(c)** internal vertebral venous plexus.  
Figure 4: Transverse post-contrast CT image of the mid-portion of the L3 vertebral body from and adult medium size dog. (**b**) basivertebral veins, (**L**) left, (**R**) right.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Morphometric Measures**} & \multicolumn{6}{c}{**Vertebral Segment**} \\ \cline{2-7}  & **L1** & **L2** & **L3** & **L4** & **L5** & **L6** & **L7** \\ \hline Vertebral canal & 7.87 \(\pm\) 1.3 & 8.24 \(\pm\) 1.8 & 9.21 \(\pm\) 1.7 & 10.27 \(\pm\) 1.9 & 9.77 \(\pm\) 1.7 & 8.68 \(\pm\) 1.2 & 8.28 \(\pm\) 2 \\ Dural sac & 4.67 \(\pm\) 1.1 & 5.04 \(\pm\) 1.2 & 6.34 \(\pm\) 1.3 & 7.42 \(\pm\) 1.6 & 5.04 \(\pm\) 0.9 & 1.78 \(\pm\) 0.4 & 1.22 \(\pm\) 0.1 \\ IVVP & 0.7 \(\pm\) 0.2 & 0.66 \(\pm\) 0.1 & 0.64 \(\pm\) 0.1 & 0.63 \(\pm\) 0.1 & 0.74 \(\pm\) 0.2 & 0.66 \(\pm\) 0.2 & 0.61 \(\pm\) 0.1 \\ Epidural space & 3.2 \(\pm\) 0.6 & 3.2 \(\pm\) 0.9 & 2.87 \(\pm\) 0.7 & 2.85 \(\pm\) 0.8 & 4.72 \(\pm\) 1.6 & 6.9 \(\pm\) 0.8 & 7.78 \(\pm\) 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average area (mm\({}^{2}\)) and standard deviation of the IVVP, vertebral canal, and dural sac and epidural space between the L1 and L7 vertebral segments in six adult medium size dogs.  
Figure 3: Dorsal reconstruction post-contrast CT images at the L3–L6 lumbar level in an adult medium size dog. (**a**) Intervertebral venous plexus, (**cr**) cranial, (**ca**) caudal.
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 2 Methods of On-Board Ship Detection
### Sea-Land Segmentation
#### 2.1.2 Sea-Land Segmentation Based on Region Growth  
Region growth is an image segmentation method that is based on image gray-scale similarity, neighborhood information, etc., with the basic goal of grouping pixels with similar properties to form regions [25]. Segmentation that is based on region growth has two key elements: one is to select the seed point of the image to be segmented and the other is to establish the range of pixel intensity contained in the region. Once there is a seed and a range, the area will grow from the seed point. When there are no more pixels that meet the conditions, the algorithm stops. T is the given threshold, so the seed growth criterion is expressed by Equation (1), as:  
\[|y(i,j)-\mu_{R}|<T. \tag{1}\]  
where, \(y(i,j)\) is the gray value of the pixel to be determined; and, \(\mu_{R}\) is the average intensity of the growth area R of the seed pixel.  
The selection of seed points is based on the results of the land and sea judgment. If a corner point is judged to be land, then that pixel point is used as the seed for regional growth. Compared with the ocean area, the land area in the SAR image exhibits strong scattering and it has a higher gray value. The gray value is used as the threshold value and the sea and land area can then be separated through regional growth. Figure 2 shows examples of sea-land segmentation.
## 4 Coverage Path Planning
### Coverage Path Planning for Simple Terrain  
The coverage method for simple terrain is similar to that for 2D planes, given the minimal terrain undulation. If changes in terrain height are taken into account during traversal, while this may reduce the elevation difference in the coverage path, it would also result in an increase in the number of steering maneuvers. This does not effectively decrease energy consumption; therefore, in this paper, we employ the standard coverage method for simple terrain.  
Currently, there are three prevalent mulching methods employed in agricultural machinery: reciprocating, spiral, and randomized [19]. The reciprocating mulching method entails traversing from one boundary of the working area to the opposite in a straight line, based on a specific angle, and then reversing direction until the mulching is completed, as illustrated in Figure 6a. The spiral mulching method involves initiating from either the boundary or the center of the working area and continuously progressing inward or outward in a spiral pattern until the entire area is multiched, as depicted in Figure 6b. The randomized mulching method consists of randomly selecting a starting point within a polygonal farmland and driving in a straight line until reaching a farmland boundary, as shown in Figure 6c. The randomized covering method, specifically, entails the robot departing from a randomly chosen starting point in a polygonal farmland and traveling in a straight line in a randomly selected direction until it reaches a farmland boundary. Upon reaching the boundary, the robot continues traveling in a straight line at a set angle, repeating this process until the entire operational area is covered [20]. However, the random covering method is susceptible to generating redundant paths, thereby prolonging the operating time, causing avoidable losses, and exhibiting low operational efficiency. Although the spiral covering method boasts a relatively low path repetition rate, it necessitates more steering maneuvers compared to the reciprocating method. Consequently, in this paper, the reciprocating method is chosen as the covering approach for simple terrain areas.  
Figure 5: Task flow chart.
## 4 The Proposed Distributed Image Codec
### The Encoder  
A block diagram of the proposed encoder is shown in Figure 2a. At the encoder, an image with a dimension of \(N\times N\) pixels is divided into \(B\) non-overlapping blocks of \(N_{B}\times N_{B}\) pixels. Let \(x_{i}\) represent the \(i\)-th block with CS measurements \(y_{i}=\Phi_{B}x_{i}\). Each image block is classified either as a key block or a non-key (WZ) block. They are encoded using CS measurement rates of \(M_{k}\) and \(M_{w}\) respectively, where \(M_{k}\gg M_{w}\). The CS measurements are then quantized using the quantization scheme proposed in our earlier work [39].  
Figure 2: Proposed distributed intra-image codec: (**a**) the encoder and (**b**) the decoder. CS, compressed sensing.  
Figure 1: _Cont._The way by which the image blocks are classified affects the compression ratio, as well as the quality of the reconstructed image. The number of CS measurements required to reconstruct a signal is dependent on the sparsity of the signal. If a block has high sparsity, then it can be reconstructed with fewer measurements compared with a block with low sparsity. If a low sparsity block is encoded with a low measurement rate, the reconstruction quality will be poor. Determining the sparsity of an image block is a challenging problem. Two different block classification schemes, one non-adaptive and the other adaptive, are proposed here.
###### Abstract  
Water inrush from separation layers is a special type of water inrush disaster that occurs during coal mining. It is characterized by the absence of precursors before the inrush, a sudden and massive influx of water, strong destructive power, and significant difficulty in prevention and control. This disaster causes substantial economic losses and casualties in mines. To study the dynamic evolution characteristics of the overlying rock strata in Guojiahe coal mine, field investigations, theoretical analysis, physical simulations, and numerical simulations were conducted. The development state of the overlying rock strata and the height of the water-conducting fractures in the 1304 working face during continuous mining were analyzed, and the spatial position of the strata where water hazards could form was determined. Through theoretical calculations, the height of the water-conducting fracture zone was found to be approximately 205 m, forming a water-storable separation space about 248 m above the coal seam roof. A physical similarity model was constructed to study the development state of overlying rock strata separation in the 1304 working face. Experimental results indicate that, as the working face advances, the rock strata gradually develop lateral separation spaces from bottom to top, and vertical fractures increase progressively in a similar manner. The spaces and fractures formed in the early stages of the working face advance gradually close and form a compaction zone. However, new fractures develop near the working face side. As mining continues, the asynchronous movement of the overlying rock promotes the development of vertical fractures, providing water-conducting channels for water inrush from the separation. Based on numerical simulation analysis of the development state of overlying rock strata separation and the height of the water-conducting fracture zone, it can be concluded that the water-storable separation space develops between the 8 th layer of the formation of sandy mudstone and the 9 th layer of the Yijun Formation conglomerate, which is 248 m from the coal seam roof. When excavated to 280 m, the separation space reaches its maximum, with a width of about 19 m and a height of about 1.2 m, and the water storage capacity increases to its maximum. The height of the water-conducting fracture zone eventually increases to 202 m.  
separation layer water hazard; separation layer space; height of the water-conducting fracture zone; dynamic evolution of overlying rock strata +
Footnote †: journal: Applied Sciences