esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Developments in Instrumentation of Functional Near-Infrared Spectroscopy Systems
## 4 Frequency-Domain fNIRS Instrumentation
Therefore, the developed system combined with DNN enables a robust tissue oxygenation monitoring system that can be able to acquire, process, and display absolute concentrations of hemoglobin at an adequate rate to catch the cardiac cycle at the higher speed [86]. In an effort to maximize the penetration depth, [PERSON] et al. has shown that with combinations of sources and detectors using a dual-slope method, phase information can provide deeper sensitivity [87; 88]. In this theoretical work, the authors have presented a dual-slope (gradients versus source-detectors separations) method with a requirement of at least two sources and two detectors arranged symmetrically. In comparison to the conventional single-slope method, the dual-slope method has achieved maximal depth sensitivity for all three data types in FD-NIRS (DC, AC, and phase). In their recent work, [PERSON] et al. has systematically studied the reconstructed image quality when phase shift measurements incorporated from an FD high-density measurement system [89]. It has been shown that phase information provides not only deeper information sensitivity but also higher effective resolutions than the CW method [89]. This could be potentially very important for fNIRS, where gaining a distance as little as 1 mm or 2 mm means one can reach deeper in the cortex. Both works, by [PERSON] et al. [87] and [PERSON] et al. [89], showed experimentally that FD systems appear to be sensitive to deeper optical layers. There are different approaches for image reconstruction to recover the optical properties from the FD-NIRS measurements. For instance, a two-step image reconstruction approach was investigated in Refs. [90; 91; 92]. Moreover, different regularization techniques such as Tikhonov regularization and Levenberg Marquardt regularization were also studied and the reader is referred to Refs. [93; 94; 95] for more information about that.
## 2 Description of Investigation Subject  
The test stand presented in Figure 1c was used in the research. The StarMFC actuator [22] was used as an executive element, powered by a high-voltage amplifier. The feedback signal for the controlle was the vibration velocity in the central point of the circular plate. The wheel plate being tested was fixed with four pneumatic cylinders. During the experiments, the source of the disc vibration was a loudspeaker placed inside a closed body that was fastened to the disc.  
The parameters of the plate used in the tests are presented in the Table 1.  
The MFC element used as an actuator is made of eight rectangular elements connected in a star shape, each of which works in higher elongations (2000 ppm) mode with a supply voltage in the range of 500-1500 V. The actuator is made as a monolithic element with a diameter of 40 mm. The actuator used is shown in Figure 1a. Its parameters was shown on Table 2.  
A laser vibrometer was used to measure vibrations, enabling point and noncontact measurement of vibration velocity, ensuring high-precision measurements and the possibility to easily and quickly change the position of the measuring point on the surface of the tested plate. The vibrometer is equipped with a He-Ne laser with a wavelength of \(\lambda=633\) nm and enables speed measurement in the range of 0.01 \(\upmu\)m/s to 10 m/s from a distance of 0.4 m to over 100 m in the angular range of 20\({}^{\circ}\).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Property** & **Symbol** & **Value [Unit]** \\ \hline Radius of the plate & \(a\) & 0.237 [m] \\ Thickness of the plate & \(h\) & 0.0318 [m] \\ Density of the plate & \(\rho\) & 2700 [kg/m\({}^{3}\)] \\ Young’s Module & \(E\) & 71 [GPa] \\ Poison ratio & \(v\) & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of the plate model.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Property** & **Value [Unit]** \\ \hline type & 8-star \\ Tensile modulus, rod direction & 30.336 [GPa] \\ Tensile modulus, electrode direction & 15.857 [GPa] \\ Poisson’s ratio & 0.31 \\ Thickness & 0.3 [mm] \\ Diameter & 40 [mm] \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of MFC actuator.
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 2 Related Work
### Gated Channel Attention Mechanism  
Incorporating attention mechanisms into neural networks offers various methods, and taking convolutional neural networks (CNNs) as an example, attention can be introduced in the spatial dimension, as well as in the channel dimension. For instance, in the Inception [61] network, the multi-scale approach assigns different weights to parallel convolutional layers, offering an attention-like mechanism. Attention can also be introduced in the channel dimension [62], and it is possible to combine both spatial and channel attention mechanisms [63]. The channel attention mechanism focuses on the differences in importance among channels within a convolutional layer, adjusting the weights of channels to enhance feature extraction. On the other hand, the spatial attention mechanism builds upon the channel attention concept, suggesting that the importance of each pixel in different channels varies. By adjusting the weights of all pixels across different channels, the network's feature extraction capabilities are enhanced.  
When applied to small object detection in aerial images, the channel attention mechanism is considered more suitable and has seen substantial research in enhancing small object detection while also addressing model complexity. For example, [PERSON] et al. [64] addressed the trade-off between model detection performance and complexity by proposing an efficient channel attention module (ECA), which introduces a small number of parameters but yields significant performance improvements. [PERSON] et al. [65] introduced a channel attention-based DenseNet network that rapidly and accurately captures key features from images, thus improving the classification of remote sensing image scenes. To tackle the issue of low recognition rates and high miss rates in current object detection tasks, [PERSON] et al. [66] proposed an improved YOLOv3 algorithm incorporating a gated channel attention mechanism (GCAM) and an adaptive upsampling module. Results showed that the improved approach adapts to multi-scale object detection tasks in complex scenes and reduces the omission rate of small objects. Hence, in this study, we attempt to use a channel attention mechanism to enhance the performance of YOLO with drones.
## 3 Methods  
### Generation of the NDVI Time Series Using STARFM  
STARFM produces a synthetic image based upon a spatially weighted difference computed between the Landsat and the MODIS scenes acquired at t\({}_{1}\), and the Landsat t\({}_{1}\)-scene and one or more MODIS scenes of prediction day (t\({}_{2}\)), respectively [19]. The application of STARFM to ratio indices rather than reflectance usually provides more consistent indicators of vegetation, since normalization reduces the radiometric and atmospheric effects from different sensors [23; 35]. We applied STARFM to MODIS and Landsat NDVI data, generating the synthetic NDVI time series with an eight-day interval and at 30-m spatial resolution. The time series of the NDVI was smoothed using the Harmonic Analysis of Time Series (HANTS) method [36].  
To evaluate the accuracy of the synthetic NDVI generated by STARFM, we compared the synthetic NDVI against the NDVI derived from the original Landsat images in four test regions in the study area, with two regions in the homogeneous cropland, and two regions in the homogeneous evergreen broadleaf forest (EBF).
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
## 5 Challenges and Recent Advances
### Developments in Signal Analysis Techniques  
Contemporary analytics frameworks increasingly exploit expanding datasets from optimized acquisition platforms. Prominent developments include the following:  
1. Advanced statistical tools analyzing spatio-temporal attributes like b-value distributions, inter-event times, covariance, and clustering, identifying damage progression signatures;
2. Machine learning algorithms automating pattern recognition through supervised classifiers discerning stable/unstable slope conditions and source characteristics;
3. Deep neural networks trained on massive labeled datasets, achieving source localization and forecasting slope behavior through predictive modeling, often outcompeting conventional linear regression techniques;Numerical wavefield simulations incorporating virtual complex structures and sensor layouts, optimizing hardware designs through design validation prior to deployments under the guidance of full-waveform analyses.  
Current advancements are moving towards the integration of augmented datasets with physics-based comprehensive numerical-statistical frameworks, leveraging increasing computational capabilities for precise quantitative data-driven damage assessment and prognostics. When coupled with optimized data acquisition systems, these developments hold great promise for enhancing long-range subsurface monitoring with high accuracy.
## 4 Experiments and Discussion
### Selection of Image Patch Size  
The element size of the image patches plays an important role in detecting crack damages. In principle, if the patch size is very small, the small image region may occupy the inside of the crack object, thereby leading to less detection accuracy. If the patch size is very large, there may be other background disturbances (e.g., attachments) involved in the crack training samples. In this case, the discrimination between crack samples and background ones will be decreased, which will affect the classification performance of the presented method. For selecting a suitable image patch size, a series of patch size \(n\times n\) (\(n=30\), 45, 75, 100, 125) were chosen as inputs for training the ELM-based detecting network. The specific training process is as follows: First of all, the \(n\times n\) image patch was stretched into one \(1\times n^{2}\) data vector, which was used as the input layers after normalization. The following two-layer ELM-AE network, consisting of \(L_{f}\) and \(L_{f}\) hidden units, was trained with randomly generated hidden parameters. The presented Algorithm 1 was utilized for training the ELM-AE sparse network to obtain the optimal output weights. Then, for separating the cracks from backgrounds, one binary ELM classification network, with \(L_{c}\) hidden nodes, was employed as the output layer. Finally, the presented ELM-based detecting network architecture was \(n^{2}-L_{f}-L_{f}-L_{c}-2\), and each layer in the stack architecture was trained independently. Using the same amount of training samples and the same training parameters mentioned above, the training accuracy and cross-validation testing accuracy have been plotted in Figure 5. It can be seen that the training accuracy reached the maximum value (97.1%) when the \(n\) was 75, and the validation accuracy (96.7%) was also the largest for this element size. Therefore, for obtaining a good detecting ratio of crack damage, the size parameter of divided image patches was set to be \(75\times 75\) pixels.  
Figure 4: Illustration of the generation of the training database.  
Figure 5: Training accuracy and cross-validation testing accuracy under different image patch sizes (\(n=30\), 45, 75, 100, 125); **(a)** depicts the training accuracy, and **(b)** depicts the cross-validation accuracy.
## 3 Results and Discussion
### The Performance Evaluation Criterion, PEC  
The performance evaluation criterion, PEC, of four zigzag ratios and Pr = 7.0 is shown in Figure 11. For the same \(\mathrm{e_{2}/e_{1}}\) ratio value, the PEC values nonlinearly decrease with an increase in the Reynolds number. The lower the \(\mathrm{e_{2}/e_{1}}\) ratio value, the better the performance. The best performance is achieved at \(\mathrm{e_{2}/e_{1}}\) values of 0.25 and 0.5. Both ratios show almost identical PEC values. The value of \(\mathrm{e_{2}/e_{1}}=0.5\) has an advantage over the 0.25 value, since it has a higher Nusselt number, as mentioned above. Therefore, the best performance is achieved at an \(\mathrm{e_{2}/e_{1}}\) value of 0.5. This indicates that, compared with the traditional equal-height rib corrugated arrangement, the performance associated with a ribbed tube with a zigzag arrangement is enhanced by 8.2%.  
Figure 10: Streamlines and velocity magnitude flow fields between the fifth and seventh ribs of configuration \(\mathrm{e_{2}/e_{1}}=0.25\), \(\mathrm{e_{1}/d}=0.1\), \(\mathrm{p/d}=1.0\), and \(\mathrm{w/d}=0.05\) at Re values of (**a**) 10,000 and (**b**) 60,000.
## 2 Related Work
Especially under resource constraints, the redundancy mechanism of the above methods will greatly increase the computational burden and energy consumption of satellites. In the framework proposed in this article, detection and protection are carried out by ground stations, which greatly reduces the computational and storage pressure on satellites. A comparison of the existing detection and protection methods is shown in Table 1. As far as we know, there are few methods for detecting SH and GH nodes caused by SDC during routing updates. Therefore, this article analyzes and compares traffic- and behavior-based SH or GH detection methods (TBDMs) and SDC error detection methods (SEDMs). TBDMs can determine the presence of SH or GH nodes based on the behavior of nodes or traffic information in the network. However, these methods work in the forwarding stage, and, therefore, they cannot detect SH or GH nodes caused by SDC (during the routing update stage) in time. In addition, TBDMs require satellites to run additional detection programs, which increases the computational burden and energy consumption of satellites. SEDMs can detect possible SDC errors through redundancy during program execution. However, these methods increase program execution time, satellite computational burden, and satellite energy consumption, and do not meet the high-efficiency requirements of routing programs. Also, SEDMs are prone to overprotection, namely the introduction of false alarms by detecting benign faults (faults that are going to be masked). \begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} 1. Collect and analyze data exchanged \\ between nodes. \\ 2. Construct behavior classifiers to \\ distinguish the blackhole behaviors from \\ rational ones. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the existing detection and protection methods.
## 2 Results
### Expression Patterns of OFCYPs under Abiotic Stress  
To further explore the role of OFCYPs in response to cold and salt stresses, RNA-seq data under abiotic stress were employed to investigate the expression levels of OFCYPs in _O. fragrans_ under cold (4 \({}^{\circ}\)C) or salt treatment (Figure S3, Tables S5 and S6). Among the 276 OFCYPs, 170 and 175 genes showed expression (FPKM \(>\) 1 in at least one sample) under cold and salt treatments, respectively. After cold treatment (4 \({}^{\circ}\)C) for 5 d, some OFCYPs were upregulated, but the highest expression level was either at 12 h (OfCYP142), 24 h (OfCYP196), 72 h (OfCYP248), or 120 h (OfCYP262) in response to cold stress. OFCYP2/131/132/134/171/171 and some others were obviously suppressed by cold stress and their expression levels showed an increasing trend after recovery for 72 h (Figure 6A). Compared to controls, expression of OFCYP23/39/49/272/138 and some other genes was upregulated after salt treatment for 72 h; however, expression of OFCYP195/237 and some other genes was clearly downregulated (Figure 6B). These results suggested that some OFCYPs might be involved in cold and salt stress responses in _O. fragrans_. Remarkably, OFCYP142 was upregulated not only after cold treatment (4 \({}^{\circ}\)C) for 12 h but also after salt treatment for 6 and 72 h.  
Figure 5: (**A**) Transcription levels of OFCYPs in different stages of sweet osmanthus flowers: S1, bud-pedicel stage; S2, bud-eye stage; S3, primary blooming stage; S4, full blooming stage; and S5, flower fading stage. The levels of transcription are the mean values of three biological samples, each sample had three technical replicates. The standard error (SE) was used to draw error bars. The different letters marked above the error bars denote the results of the significant differences analysis at \(p\) < 0.05 obtained using SPSS (version 25). (**B**) Correlation analysis chart of qRT-PCR and transcriptome data FPKM values. (**C**) Correlation analysis of expression of OFCYPs and OFTPS2. Correlation coefficients from negative to positive correlation are indicated by a gradient from blue to red.
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 3 Relative Strengths and Limitations of Existing Techniques
### Approaches
#### 3.1.4 Change Detection  
Change detection approaches traditionally refer to techniques for comparing pre- and in-flood backscattering intensities to detect changes in pixels caused by flooding [48; 50; 57; 64; 65]. One or more prior- and post-flood SAR image or images are needed; we refer to this hereafter as \"dry reference.\" The principle behind change detection techniques is straightforward, and the techniques should, theoretically, be effective in overcoming the first source of error--over-detection on water-like surfaces--as was shown by [PERSON] et al. [49; 51]. In the past, however, limited by their availability, change detection traditionally used only two SAR images. As a result, the aforementioned second-geometric error can only be avoided by limiting the using SAR images obtained from the same orbit or by indirectly evaluating the change from binary water mask; and the third error sources--speckle-caused noise--could compromise the effectiveness [66]. [PERSON] et al. [59] have proposed an improved change detection (ICD) method that employs multiple (\(\sim\)5) pre-flood SAR images and a multi-criteria approach to reject false positives caused by water-like bodies and to reduce the effect of speckle.  
More generally, change detection can be extended to the use of multiple dry references [59; 67], the comparison of binary water masks [59], or InSAR coherence. The loss of InSAR coherence might be a more promising signature than the enhancement of intensity to detect buildings surrounded by inundation [33; 34; 35]. We refer to buildings surrounded by inundation as flooded buildings for simplicity, which should not be understood as flooded building floor or basement because satellite remote sensing images cannot detect the internal of buildings.  
Besides isolating inundated areas from permanent (or, more precisely, preexisting) water areas, change detection techniques have been used to initialize water pixel sets before thorough detection [53; 57], especially when full automation is an objective and no a priori water information is available. Change detection methods can also be used to detect flood beneath vegetation, as described in the next section.
### Deepening of Shaft II in the Borynia Mine  
Shaft II of the Borynia Mine is an intake shaft equipped with two hoists used for the transportation of personnel and materials. The shaft is currently connected to mining levels at depths of 713 m, 838 m, and 950 m. Its diameter is 7.2 m along its entire length. The shaft was designed to be deepened from a depth of 980 m to approximately 1150 m, thereby providing access to a new mining level at a depth of 1120 m.  
In the conceptual design developed in 2018, it was assumed that the shaft deepening would be carried out using the top-to-bottom method from a sublevel accessed via an auxiliary incline. The decision to select the top-to-bottom deepening method for Shaft II was primarily influenced by two critical factors. First, the contractor's limited experience with alternative deepening techniques, particularly the bottom-to-top approach, posed significant risks in terms of efficiency and safety. Second, the relatively low strength of the rock mass and the considerable excavation depth necessitated a deepening method that would ensure shaft stability and operational safety. At the conceptual design stage, two possible approaches to the top-to-bottom shaft sinking were considered, as follows:  
* Sinking with a large-diameter borehole used for the gravitational transport of mined rock to the mining level at a depth of 1120 m.
* Conventional sinking without a large-diameter borehole, whereby mined rock is transported to the sub-level (using a shaft hoist) and then to the existing mining level at a depth of 950 m (using conveyor belts).  
Since the new mining level at a depth of 1120 m could not be accessed before the commencement of shaft deepening, the sinking method without a large-diameter borehole was ultimately chosen (excluding only the short section of the shaft located between the hoisting machine chamber and the technological shaft inset). Therefore, the following auxiliary workings had to be excavated before the commencement of shaft sinking:  
* Connecting drift I, the technological incline, and the technological drift were used as access routes to Shaft II, as well as transport routes for materials and mined rock from excavated shaft.
* Technological shaft inset was used as a loading point for materials, a discharge point for mined rock from the excavated shaft, and a location for the concrete mixing plant.
* Hoisting machine chamber with the shaft inset was used as a location for hoisting machines and mining reels.
* Connecting drift II was used as an access route to the hoisting machine chamber.  
Arrangement of auxiliary workings for the deepening of Shaft II is illustrated in Figure 1.
# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 4 Text-Based Methods
### Graph Based Methods  
Graph-based methods structure data as a network of vertices and edges, where vertices represent entities and edges represent the relationship between these entities. The edges can be weighted to determine the strength of the relationship. In graph-based hashtag recommendation, the entities can be hashtags, terms of tweets, and users. The assumption of the research adopting graph-based methods in hashtag recommendation is that _terms in tweets and hashtags have implicit relationships, forming a semantic graph; using this graph can relate terms to hashtags even if they never co-occur_[9].  
As such, [PERSON] et al. [57] built the HE-graph, a heterogeneous graph of hashtags and Wikipedia terms drawn from tweets. HE-graphs draw directed hashtag-term edges and indirected term-term edges derived from Wikipedia graph structure. Each hashtag is linked to a set of terms that represents its semantic. Using TFIDF and various similarity measures, the relevance scores between hashtags were determined. [PERSON] et al. [14] added another dimension and built a heterogeneous graph of hashtags, tweets and users. This graph was further summarized into a homogeneous graph of hashtags. In their paper, two methods were used to calculate the relevance between hashtags: Random Walk with Restart method (RWR) and cosine similarity. The final ranking of candidate hashtags combined the RWR weight and the cosine similarity score. [PERSON] et al. [9] built a hashtag prediction that can find the most significant terms in the tweet, weigh their closeness to each hashtag, and use a temporal sliding window to reflect the terms and hashtags within a time frame. They used IDF and entropy methods to find significant words. They found that IDF and entropy methods contributed similarly to the final recommendation performance. They also found that a week's time frame generated the best results over an hour, four hours and a day for the temporal sliding window.