esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 2 Energy Trade-Offs for Mobile Systems
### Keeping the Charge  
As the node boots, the most energy-efficient way to utilise the energy is to consume it straight away to avoid energy loss due to capacitor leak current \(E_{leak}\) and the minimum energy needed to operate the sensor node in the sleep mode \(E_{sleep}\). The supercapacitor energy level at time \(t\) is modelled as:  
\[E(t+1)=E(t)+E_{harvested}(t)-E_{leak}(t)-E_{load}(t) \tag{3}\]  
where \(E_{load}\) denotes energy consumed by the sensor node and depends on the sensor operating mode, e.g., in case a sensor node is sleeping \(E_{load}=E_{sleep}\). However, using energy immediately is not necessarily useful, as many sensor tasks require spreading the energy in time to maximise the utility, e.g., sensor coverage or operating time. Reducing the duty cycle in an attempt to spread the energy will lead to higher energy loss, \(E_{leak}\), and consequently lower energy utilisation. Thus, the tasks that require long operating time or sensor coverage would benefit from a larger capacitor, which conflicts with the fast boot time requirement.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 4 Experimental Results and Analysis
### Capacity and Imperceptibility  
The average result for SNR of the audio signals, BER and normalized correlation (NC) of the extracted watermarks and the capacity are shown in Table 1.  
Each audio segment conceals 1 bit information according to the watermark embedding principle of this algorithm in Section 2.2, so the capacity is 172.27 bps. It can be seen from the average results in Table 1 that the SNR of this algorithm is 24.58 dB, higher than that in literature [4; 18] with the same capacity, and both the capacity and SNR of this algorithm are better than those in literature [1; 10; 25], so this algorithm has better imperceptibility and larger capacity compared with the five related works.  
Figure 4: Three binary pictures and their encrypted pictures: (**a**) the first picture (100 \(\times\) 100); (**b**) the first encrypted picture; (**c**) the second picture (200 \(\times\) 50); (**d**) the second encrypted picture; (**e**) the third picture (200 \(\times\) 50); (**f**) the third encrypted picture.  
In the case of no attack, the waveform comparison charts of an audio clip (lasting about 3 s) before and after embedded watermarks are shown in Figure 5, and the corresponding spectrogram comparison charts are shown in Figure 6. Those two figures indicate the excellent imperceptibility.
## 3 Materials and Methods
### EEG Data Acquisition  
The BCI Competition IV 2a dataset [62] is utilized during this study. It originates from recordings distributed in GDF format, a widely recognized standard for biomedical signals. However, for practical purposes, many studies (including this one) use the equivalent.mat (MATLAB R2022a files) format. The latter is widely used as a benchmark for MI-based brain-computer interfaces, largely due to its standardized protocol, balanced class distribution, and thorough documentation.  
This set comprises contributions from nine participants, each performing four distinct motor imagery (MI) tasks--left hand, right hand, both feet, and tongue--during two separate sessions on different days. In each session, the participant completed six runs. Each run comprised 48 trials, yielding 288 trials per session and a total of 576 trials across both sessions for each subject. The trials typically lasted around 7 to 8 s, and annotations were present to mark trial onsets and the specific MI cue (one of the four classes). The sequence of prompts and rest periods conforms to a predefined structure, which typically includes an initial interval of eyes open, followed by eyes closed, and a brief phase of eye movements. As a result, the entire sessions varied in duration from approximately 2016 to 2304 s (see Figure 1 for the overall timing diagram).  
At a sampling rate of 250 Hz, for every participant, 25 channels were recorded. Of these, 22 channels are dedicated to electroencephalography (EEG), placed according to  
Figure 1: Representation of the temporal progression related to executing motor imagery tasks.  
the international 10-20 system, and centered around motor-relevant scalp sites (C3, Cz, C4, etc.). The remaining three channels captured electrooculography (EOG) signals. In applications focused on MI classification, EOG channels are often excluded to minimize artifacts unrelated to motor cortex activity (see Figure 2 for the montage representation).  
To maintain the data quality, the EEG signals were optimized through the application of a bandpass filter ranging from 0.5 Hz to 100 Hz, in addition to employing a 50 Hz notch filter specifically designed to remove interference caused by power line noise.  
While the original dataset is sampled at 250 Hz--a rate sufficient for capturing critical frequency bands (e.g., alpha and beta) involved in motor imagery--it is worth noting that using a higher sampling rate, such as 512 Hz, could allow for finer temporal resolution and the retention of higher-frequency components (e.g., gamma activity). However, this would also result in significantly larger data volumes and increased computational load, since motor imagery primarily balance between data resolution and processing efficiency.
# Stereo Digital Image Correlation in MATLAB
## 4 Validation
### Samples 1 and 2  
Sample 1 captures images of a complex specimen geometry, consisting of a flat base plate with protruding 3D features, as it is displaced in increments of 10 mm in the x- and z-directions according to Table 1 in Appendix D. The specimen is displaced by an Aerotech nano-positioning stage (model ANT130-160-XY) with a high-precision linear encoder and position feedback control such that the true displacements are known with high certainty. Sample 2 simulates the experiment of Sample 1 using the synthetic image generator documented by [PERSON] et al. [62] to minimize error sources in the image set.  
The world CS of each DIC code differs from that within which the displacements were imposed. Thus, for each code [PERSON]'s theorem is used to compute the true, \(\hat{\mu}_{w_{q}}^{true}\), and calculated, \(\hat{\mu}_{w_{q}}^{calc}\), displacement magnitudes of each subset \(q\). The resulting errors in displacement magnitude are quantified as bias, computed as the mean of the absolute error (\(MAE\)), and variance, computed as the standard deviation of the absolute error (\(\sigma\)).  
\[MAE=\frac{\sum_{q=1}^{Q}\left|\hat{\mu}_{w_{q}}^{calc}-\hat{\mu}_{w_{q}}^{true }\right|}{Q}\,\sigma=\sqrt{\frac{\sum_{q=1}^{Q}\left(\left|\hat{\mu}_{w_{q}}^{ calc}-\hat{\mu}_{w_{q}}^{true}\right|-MAE\right)^{2}}{Q-1}} \tag{52}\]  
Here \(Q\) is the number of valid subset pairs in the image pair analyzed. For Samples 1 and 2, ADIC3D and DICE had 71,715 and 66,850 valid subsets, respectively, while StrainMaster had 112,021 and 108,085 valid subsets respectively. Tables 15 and 16 report errors metrics for Samples 1 and 2, respectively, for steps 3, 7, and 15 which correspond to displacement extremes.  
The improved error metrics of Sample 2 relative to Sample 1 for each code, with the exception of StrainMaster's variance for step 15, reflects the impact of the reduced error sources of the synthetic image set. For Sample 1, ADIC3D performs on par with DICe (up to 27% higher bias) and StrainMaster (at least 12% lower bias). In contrast, for Sample 2 ADIC3D shows improved error metrics relative to DICe and StrainMaster (at least 69% and 54% lower bias respectively). StrainMaster's higher bias and variance are a result of it analyzing more subsets in the vicinity of protruding features, which are challenging to track, which are removed for ADIC3D and DICe.
## 3 Antimicrobial Resistance--Mechanisms and Mitigation Strategies
### Active Drug Efflux  
Once the antimicrobial has entered the bacterial cell and reached the periplasm or cytoplasm, the drug can be actively pumped outside of the bacterial cell through efflux pumps, which are embedded in the bacterial plasma membrane [15]. These efflux pumps are responsible for much of the intrinsic or constitutive resistance of Gram-negative bacilli to certain antimicrobials but can also be seen in Gram-positive germs and can be deter  
Figure 1: **Main mechanisms of antimicrobial resistance** (created with BioRender.com): 1. limiting drug uptake (e.g., thickened cell wall, porin mutation, porin downregulation.); 2. target modification (e.g., penicillin-binding protein modification in the bacterial membrane, target modification during nucleic acid synthesis or ribosomal methylation); 3. drug inactivation (e.g., by betalactamases); 4. drug efflux through active pumps.  
minants of acquired drug resistance. Specifically, efflux pumps can induce high-level resistance to tetracycline [15], resistance to fluoroquinolones, macrolides, streptogramin and lincosamides, glycylcyclines, aminoglycososides, oxazolidinones, metronidazoleole and betalactams [16].  
Efflux pumps can be drug-specific or multidrug pumps. The former are encoded on mobile genetic elements, while the latter, which can transport several different molecules out of the bacterial cell, are mainly encoded on chromosomes.
# Deep Watermarking Based on Swin Transformer for Deep Model Protection
## 5 Conclusions  
The main contribution of this research lies in successfully introducing Swin-UNet to watermarking tasks and exploring its potential for watermark hiding. Compared to the traditional CNN networks used in previous studies, the attention mechanism of Swin-UNet significantly enhances the quality of watermarked images. Specifically, the peak signal-to-noise ratio of the watermarked images has improved by approximately 4 dB compared to [PERSON]'s method [25], demonstrating a notable enhancement in visual quality. This improved performance makes the watermark embedding process more effective and contributes to the increased invisibility of the watermark.  
In the context of utilizing watermarks for image protection, Swin-UNet exhibits notable advantages. However, experiments indicate that directly substituting the embedding network with Swin-UNet reduces robustness against surrogate attack models. Nevertheless, this limitation can be mitigated through alternative techniques, such as data augmentation, to enhance robustness. By incorporating data augmentation during adversarial training, the model becomes more resilient to various attack scenarios, thereby improving the overall extraction performance. These findings suggest that Swin-UNet holds significant potential for further advancements in watermark embedding.  
Future research can focus on enhancing the model's robustness. The proposed methods primarily addresses robustness against surrogate attacks, but in real-world scenarios, adversaries may employ various techniques to compromise watermarks in images. Common attacks include cropping, scaling, compression, and more recent AI-based transformations that alter image elements, such as changing faces. Future studies could explore a reduction in the size of embedded watermark information to achieve increased robustness, employing strategies like repeated embedding or multi-dimensional embedding. Only when the watermark extractor can withstand a broader range of attacks will the technology's broader applicability and success be ensured. Additionally, integrating Swin-UNet with other advanced deep learning architectures could significantly advance watermarking technology. In conclusion, the results of this study suggest that Swin-UNet is a promising solution for watermark embedding tasks, providing a strong foundation for future research and practical applications.  
Conceptualization, C.-H.U.; methodology, C.-H.U.; writing--original draft preparation, C.-H.U.; writing--review and editing, K.-C.C.; supervision, K.-C.C. 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.  
The authors sincerely appreciate all technical support.
## 1 Introduction
This study focuses primarily on the F\({}_{900}\) absorption feature and more specifically on how calculation of the full width half maximum (FWHM) of the feature, when combined with the wavelength of the point of maximum absorption of the F\({}_{900}\) absorption wavelength, adds a strong qualitative aspect to spectral datasets collected in BID and CID formations. Figure 1: A surface reflectance goethite sample (blue spectrum) showing the \({}^{6}\)A\({}_{1}\)\(\rightarrow\)\({}^{4}\)T\({}_{1}\) (F\({}_{900}\)) crystal absorption feature at approximately 900 nm. The upper spectrum in orange is the hull-corrected spectrum. The hull-corrected F\({}_{900}\) feature is used to define the point at which the deepest absorption value occurs and defines the Fe oxide wavelength, Fe oxide FWHM and Fe oxide depth spectral metrics. The wavelength region between 1550 and 1850 nm is the region where a local hull correction is performed, and the maximum depth recorded as the SWIR dropoff metric. The region between 1850 and 2100 nm also uses a local correction to calculate the maximum depth and is recorded as the water depth. See Figure 2 for an example of the iron boomerang scatterplot. The manifold is formed by plotting the F\({}_{900}\) wavelength versus the FWHM of the F\({}_{900}\) and is herein referred to as the iron boomerang due to its distinct shape. This shape is not necessarily evident in sparse spectral datasets, but the findings are still applicable. Visualising the calculated F\({}_{900}\) wavelength versus F\({}_{900}\) FWHM data in the iron boomerang allows the user to immediately see the general composition of the dataset in terms of hematite/magnetite and goethite and, as will be shown, the magnitude of the F\({}_{900}\) FWHM  
Figure 2: **(A)** shows the plot of the Fe oxide wavelength versus the Fe oxide FWHM for both the complete dataset (labelled as no SWIR masking) and the dataset after masking for SWIR alteration minerals (labelled as SWIR masking) which leads to a suppression of the Fe oxide FWHM values. **(B)** shows a density plot comprising 120 \(\times\) 400 cells giving a spectral resolution of approximately 1.4 nm \(\times\) 0.7 nm and demonstrates a typical density distribution of the iron boomerang for dense datasets. **(B)** is applied to the ”no SWIR masking” data in **(A)**. feature in the goethite-dominated region, which can provide a level of vitreous and ochreous goethite discrimination.
## 2 Materials and Methods
### Study Selection and Data Extraction  
The evaluation of the retrieved articles' titles and abstracts for eligibility criteria was conducted blindly by two reviewers ([PERSON] and [PERSON]). Any discrepancies were settled through discussions with a third author ([PERSON]). The full text of the remaining articles was then assessed independently and in duplicate by [PERSON] and [PERSON] before final selection. Data were obtained from the selected studies and organized according to the PICOS question, including general information (author name and publication year), participant details (number of samples and landmarks measured, anatomic landmarks), intervention/comparison(deep learning vs. manual landmarking), and outcomes (mean radial error, successful detection rate).
## 3 Results
### Analysis Stage
The set was split into five subsets of the same size, as shown in Figure 10. The validation subset in each training session was used to measure the generalization error, in other words, the misclassification rate of the model with data dissimilar from those previously applied during the training procedure. The cross-validation procedure was implemented on the training and testing datasets, and the average value of MSE and the standard deviation obtained were very close to those obtained in the confirmation stage [48]. Table 9 shows the results obtained in the cross-validation process, where it is observed that the average training value was equal to 17.5099, the average percentage of hits considering an error of less than 5% was equal to 87.86%, and the average value of MSE was equal to 17.5099, with standard deviations of 1.5848, 1.8974, and 0.0059, respectively. Figure 9: Trajectory of the manipulator with the worst prediction and correlation test: (**a**) worst predicted values; (**b**) correlation test. In relation to the three profiles analyzed, the choice of the appropriate levels for the structural parameters of the best network topology were those corresponding to the maximized desirable profile with 100 and 30 neurons, respectively, a momentum of 0.2, and a learning rate of 0.2. Figure 11 shows the layered surface diagram of the neural network used in this work. The training was performed using MATLAB software. The ANN was composed of an input layer with 100 neurons, a hidden layer with 30 neurons, and an output layer with 6 neurons. All three layers used the activation function. The training algorithm used to adjust the weighting of the synaptic weights was _resilient backpropagation_.
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Internal/Subsurface Defects
#### 3.2.4 Radiographic Testing (RT)  
This testing method uses X-rays or gamma rays to produce a radiograph of a specimen to register any changes in thickness, assembly details, and defects. Radiography has significant use in structural engineering, but for steel bridge inspection, more commonly, it is used for testing the welded joints. Cracks in welded joints under dynamic loading can propagate further and reduce the cross-section until the fracture of the welded part occurs. Although this type of NDT can be very slow and expensive, it has the capabilities and accuracy to detect porosity, cracks, inclusions, and defects in weld interiors. Figure 20 shows an inspection of a steel pipe and steel beam using the radiographic method [52]. Figure 21 shows an example of Radiographic Testing, where the defective specimen is subjected to a radiation source (either X-rays or gamma rays) [53].  
Figure 19: Schematic representation of fatigue cracks and IT inspection at rib-to-deck joint [46].  
Figure 20: Inspection of a steel pipe using RT Method [10; 52].  
The radiographic testing equipment consists of a radiation source, film cassette or digital flat panel detector, penetrometer, and filmmakers. Little or no surface preparation is needed for the application of RT. The most effective application for RT is for the detection of subsurface defects hidden from the naked eye. In this method, the test piece is subjected to the radiation source. Based on the material density, the radiation is transmitted at different rates and variations are captured on photographic film or fluorescent screen. Radiographic testing functions with an X-ray sensitivity of 2%, meaning that if the smallest dimension of a component is 20 mm, the smallest void that can be detected is 0.4 mm [15; 54]. RT has also been used at an experimental level for detection of wire breaks in stay cables [55]. The disadvantages of the RT method for steel bridges include its slow operation and high cost, its serious safety issues and health hazards, its improper use for thick sections, and its need for expensive equipment and special training for the inspectors [56].
# Stability Control of Multilayer Roof Strata in the Large Mining Height Gob-Side Entry: A Case Study
## 3 Roof Separation Analysis of Bolted Joint Rock Mass  
### Roof Strata Movement Analysis  
The roof of the 22301 working face in Tunlan Mine is characterized by a compound structure with a soft lower layer and hard upper layer. The thickness of the soft rock layer at the bottom does not exceed 3 m. After mining activities, the immediate roof above the backfilled area changes from a three-directional stress state to a two-directional or one-directional stress state. When the main roof ruptures, and subsequently bends downward above the solid coal, the immediate roof supported by the solid coal tends to produce flexural subsidence. Due to the lower strength and thin layers of soft lower rock strata, their deflection is greater than that of the upper rock strata. The weak bonding between the upper and lower layers, subjected to the compressive forces during the rotation of the main roof, makes it easy for delamination. As a result, the lower layers are more likely to collapse towards the gob-side, leading to partial roof collapse in the filled areas ahead of the entry (as shown in Figure 6). As the main roof continues to rotate and the gob-side backfill can no longer effectively support the rock strata, the compound roof layers separate resulting in instability of surrounding rock structure in the gob-side entry. Furthermore, when the compound roof is unable to bear the increased loading pressure, collapse accidents may occur, where the roof shears and subsides along the head entry.  
Figure 5: Fracture distribution characteristics in the roof rock layer along the strike of 22301 head entry.
###### Abstract  
Vermiculite has been used for the removal of Cu\({}^{2+}\) and Ag\({}^{+}\) from aqueous solutions in a fixed-bed column system. The effects of initial silver and copper ion concentrations, flow rate, and bed height of the adsorbent in a fixed-bed column system were investigated. Statistical analysis confirmed that breakthrough curves depended on all three factors. The highest inlet metal cation concentration (5000 mg/dm\({}^{3}\)), the lowest bed height (3 cm) and the lowest flow rate (2 and 3 cm\({}^{3}\)/min for Ag\({}^{+}\) and Cu\({}^{2+}\), respectively) were optimal for the adsorption process. The maximum total percentage of metal ions removed was 60.4% and 68.7% for Ag\({}^{+}\) and Cu\({}^{2+}\), respectively. Adsorption data were fitted with four fixed-bed adsorption models, namely Clark, Bohart-Adams, Yoon-Nelson and Thomas models, to predict breakthrough curves and to determine the characteristic column parameters. The adsorbent was characterized by SEM, FTIR, EDS and BET techniques. The results showed that vermiculite could be applied as a cost-effective sorbent for the removal of Cu\({}^{2+}\) and Ag\({}^{+}\) from wastewater in a continuous process.  
Keywords:fixed-bed column; sorption dynamics; silver; copper; vermiculite +
Footnote †: journal: Applied Physics Letters