esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Image Watermarking Algorithm Based on ASIFT against Geometric Attacks
###### Abstract  
Image processing technology has been developed rapidly in recent years, and altering the content of an image is easy for everyone, but may be illegal for an attacker. Thus, it is urgent and necessary to overcome this problem to protect the integrity and authenticity of images. Watermarking is a powerful technique proposed to solve this problem. This paper introduces a robust image watermarking algorithm working in the wavelet domain, embedding the watermark information into the third level low frequency coefficients after the three-level discrete wavelet transform (DWT) and singular value decomposition (SVD). Additionally, to improve the robustness to geometric attacks, the affine-scale-invariant feature transform (ASIFT) is applied to obtain feature points which are invariant to geometric attacks. Then, features of acquired points between the watermarked image and the received image are used to realize the resynchronization to improve the robustness. Experimental results show that the proposed algorithm achieves great balance between robustness and imperceptibility, and is robust against geometric attacks, JPEG compression, noise addition, cropping, median filters, and so on.  
digital image watermarking; copyright protection; discrete wavelet transform (DWT); affine-scale-invariance feature transform (ASIFT); geometric attacks +
Footnote †: journal: Applied Sciences
## 4 Preprocessing  
The proposed scheme has three main sections: (a) preprocessing, (b) resampled signal approximation and estimation, and (c) fault detection and diagnosis. To calculate signal conditions and the probable types of fault by using an intelligence-based indirect observer and a machine-learning approach, it may be necessary to calculate one or more statistical features extracted from the bearing signal. Therefore, in order to extract appropriate features, the bearing raw signal is first windowed at exactly the same size. Based on [38], the range of rotational speeds in the CWRU dataset is 1730 to 1797 revolutions per minute (RPM) or 28 to 30 revolutions per second (RPS). In addition, the data collection sampling rate frequency for the CWRU dataset is 48 kHz. So, to cover the bearing conditions, 1200 samples are needed every rotation. Moreover, because the signal length is 120,000 samples, we have 100 windows for each condition. In the next step, the RMS signal is selected as a feature based on the following definition [12]:  
\[Y_{rms}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}Y^{2}} \tag{1}\]  
Here, \(Y_{rms},~{}N\), and \(Y\) denote the resampled RMS value for each window, the number of windows calculated (based on the resampling technique), and the original bearing signal in each window resampled using the above technique, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Torque Load (hp)** & **Crack Sizes (Inches)** \\ \hline NORM & 0, 1, 2, 3 & - \\ ROLF & 0, 1, 2, 3 & 0.007, 0.014, 0.021 \\ INRF & 0, 1, 2, 3 & 0.007, 0.014, 0.021 \\ OURF & 0, 1, 2, 3 & 0.007, 0.014, 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Case Western Reserve University Vibration Dataset classes, torque loads, and crack sizes [38].
## 3 Proposed Methodology
### Stage 2: Data Curation  
The second stage of the proposed data workflow is the _\"Data curation\"_ stage. This is an essential step in the process of data handling, since it involves removing errors and inconsistencies in the data to ensure that they are accurate and reliable so the analysis can be continued. These errors may include typos, missing values and outliers, while inconsistencies can include duplicate data or an inconsistent data format. Many of these issues can be addressed in an automated way. The following functionalities can facilitate this stage:  
1. Missing data imputation: Several machine learning-based imputation strategies exist that attempt to estimate the missing values, such as Bayesian Ridge Regression [41], _k_-Nearest Neighbors [42], Random Forests [43] and Extra-Trees [44].
2. Outlier detection: These methods identify data anomalies, which significantly differ from other observations or data trends and can be attributed to measurement errors or data entry errors. In the literature, the Isolation Forest [45], Local Outlier Factor [46] and One-Class Support Vector Machine [47] algorithms constitute some of the most popular outlier detection methods.
3. Partial measurement estimation: In several cases, the overall behavior of an individual needs to be inferred from partial measurements. For instance, users may provide measurements through smartwatches for only part of the day, or may not use their smartwatch for some activities, thus requiring estimation of overall physical activity.
4. Measurement error and bias quantification: This component augments measurements with metadata related to the possible error and bias of the measurements. This information will assist in subsequent data analysis tasks, e.g., by providing confidence intervals through sensitivity analysis.  
The outcome of the \"Data curation\" stage is curated datasets, which can be used for data analysis and statistical and causal inference.
# Acoustic TDOA Measurement and Accurate Indoor Positioning for Smartphone
## 4 Experiments and Results  
### Experimental Parameters  
In this paper, we chose two experimental scenarios. Scenario 1 is in the lounge on the fourth floor of Luojia Laboratory, Wuhan University. Scenario 2 is in the lobby on the  
Figure 11: Positioning results of point A and point B (using ML method): (**a**) Positioning results of different elements of test point A; (**b**) Positioning results of different elements of test point B.  
Figure 12: Positioning simulation of scenario 1: (**a**) distribution of acoustic nodes and test points in scenario 1; (**b**) the CDF diagram of scenario 1.  
second floor of Luojia Laboratory, Wuhan University. The distribution of acoustic nodes and test points in scenario 1 is shown in Figure 12a. The distribution of acoustic nodes and test points in scenario 2 is shown in Figure 13.  
In scenario 2, we divide the test point into five areas. In the experiment, the parameters of the experimental signal are designed in advance, and the parameters are shown in Table 1:  
In two experimental scenarios, the acoustic node deployment shape is rectangular. In Scenario 1, the coordinates of the four acoustic nodes are (0.0 m, 0.0 m, 1.72 m), (0.0 m, 4.8 m, 1.72 m), (4.8 m, 0.0 m, 1.72 m) and (4.8 m, 4.8 m, 1.72 m). In scenario 1, the height of the smartphone is the same as the height of the acoustic nodes. The purpose is to verify the reliability of the algorithm in this paper. In scenario 2, the coordinates of the four acoustic nodes are (0.0 m, 0.0 m, 1.72 m), (0.0 m, 6.4 m, 1.72 m), (7.9 m, 0.0 m, 1.72 m) and (7.9 m, 6.4 m, 1.72 m). In Scenario 2, the height of the smartphone is 1.30 m. The process of deploying acoustic nodes is as follows: First, we adjust the height of the acoustic nodes. Then we use the plumb line to align the square acoustic nodes with the top of the square floor tile. Similarly, we also use the plumb line to align the smartphone with the top of the floor tile. The experimental diagram of scenario 2 is shown in the following Figure 14:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acoustic Node** & **Frequency Range** & **Signal Type** & **Duration** \\ \hline node 1 & 15–16 kHz & down & 10 ms \\ node 2 & 16.5–17.5 kHz & up & 10 ms \\ node 3 & 18–19 kHz & down & 10 ms \\ node 4 & 19.5–20.5 kHz & up & 10 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chirp signal parameters of different acoustic nodes.  
Figure 14: Experiment scenario 2 diagram.  
Figure 13: Distribution of acoustic nodes and test points in scenario 2.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
## 2 The System Model
### Connectivity Probability
In a harsh noise environment, since the DF mode can remove noise through decoding, compared with the AF mode, it performs better in ensuring the signal quality and transmission reliability and can effectively reduce the bit error rate of wireless communication. Based on this, the decode-and-forward mechanism is adopted in this work. For a two-hop communication system, the system's connection probability depends on the channel conditions of each hop. The signal-to-noise ratio of the entire system is determined by the minimum SNR of the two hops. Thus, the overall system SNR is expressed using the following formula:  
\[SNR_{\zeta 2r}=\min\left[\frac{P_{s}|h_{sr}|^{2}}{P_{n}},\frac{P_{r}|h_{rd}|^{2}}{P _{n}}\right] \tag{13}\]  
Therefore, the connection probability of the two-hop communication system can be expressed as  
\[\text{SNR}_{1}=\frac{P_{s}C_{1}d_{1}^{-\alpha}|h_{sr}|^{2}}{n_{0}} \tag{7}\]0  
Further derivations can be made:  
\[\text{SNR}_{1}=\frac{P_{s}C_{1}d_{1}^{-\alpha}|h_{sr}|^{2}}{n_{0}} \tag{7}\]1  
\[\text{SNR}_{1}=\frac{P_{s}C_{1}d_{1}^{-\alpha}|h_{sr}|^{2}}{n_{0}} \tag{7}\]2  
The connection probability of a two-hop system, as indicated in (15), is determined by various parameters. These include the distribution trend in the received channel gain, the path loss for each hop, distance, transmission power, forwarding power, noise power, and the signal-to-noise ratio threshold. However, in real-world maritime wireless communication networks, various dynamic environmental conditions can significantly affect the accuracy of the connection probability calculations. The impact of factors such as the ship's speed, surface wave fluctuations, and weather conditions on the system performance remains unclear. As a result, the validity of existing models requires further evaluation and refinement. To tackle this challenge, it is essential to develop a novel data-driven framework tailored to wireless networks operating in complex maritime environments.
# Improved Durability of Wood Treated with Nano Metal Fluorides against Brown-Rot and White-Rot Fungi
## 3 Results
### Decay Resistance  
The average mass loss of virulence specimens against _R. placenta_ were 18.4 \(\pm\) 2.3% and against _T. versicolor_ was 19.4 \(\pm\) 3.4%. The average mass losses of the treated specimens are shown in Figure 6. Specimens treated with only ethylene glycol had mass losses higher than 20%, which is expected as the solvent did not protect against fungal decay. NaF treated specimens that were leached were also tested to compare with NMF treated specimens. Against _R. placenta_, NaF treated specimens had the highest mass loss which was greater than the virulence specimens. Also, wood treated with NMFs exhibited less decay than those treated with NaF, evidenced even in visual assessment (Figure S1, Supplementary Section). Among the NMF treated specimens, the increase in concentration of MgF\({}_{2}\) from 0.5 M to 0.8 M resulted in reducing the mass losses caused by both fungi to below 10%. Although the average mass loss caused by _T. versicolor_ in MgF\({}_{2}\) (0.8 M) treated specimens was below 3%, it was higher for specimens exposed to _R. placenta_. Only the specimens treated with the combination of MgF\({}_{2}\) and CaF\({}_{2}\) had mass losses below 3% against both fungi. Thus, they can be considered durable against _R. placenta_ and _T. versicolor_.  
Figure 4: X-ray micro-CT images of the transverse section of Scots pine wood treated with NMFs: (**a**) before leaching; (**b**) after leaching.
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Jufra
#### 3.2.4 False Negatives  
The producer's accuracy (68%) shows that the algorithm failed to detect change at seven sites. The most significant changes which the algorithm missed were caused by the construction of new fields at a foggara (EAMENA-0001554) and at an area of walls/field systems which were probably contemporaneous with the nearby foggaras (EAMENA-0001553, see Figure 25). At both sites, the change consisted of rows of planting holes for trees which gave low values in the change layer (between 0.04-0.05). Lowering the threshold value would still not have detected this without greatly increasing the rate of false positives. The planting holes were not detectable in the Sentinel-2 imagery, but visible in the PlanetScope images. The available PlanetScope imagery of sufficient quality suggests that the planting holes at the field walls were dug at some time between 16-26 March 2020 (Figure 26).  
Figure 26: PlanetScope images. The remains of past fields and structures are delineated. The second image shows tree planting holes appearing at the south edge of EAMENA-0001553 between 16–26 March 2020 (changed area marked by arrows).  
Figure 25: New field with planting holes damaging the ancient field system of EAMENA-0001553. Google Earth images.  
Some minor changes were too small to be identified by Sentinel-2 imagery, including single lines of vehicle tracks at EAMENA-0001494, a colour change on the surface sand at EAMENA-0001529, and a small extension to a building (c.6 x 6 m) at EAMENA-0001507, which was added in July 2019 and not identified by the algorithm. Part of a modern boundary wall at EAMENA-0001527 which was bulldozed during the analysis period was also missed. Discolouration, which may result from runoff, was missed at EAMENA-0001556.
# An Experimental Study on Alkali Silica Reaction of Concrete Specim Using Steel Slag as Aggregate
## 3 Results and Discussion
### Evaluation Expansion Properties of Steel Slag
#### 3.2.2 Change in Length of Mortar Bar with Steel Slag  
The results of the ASR expansion test are shown in Figure 3. The results indicate that the expansion ratios noticeably increased with the increase in the test period. The expansion of the mortar bars with steel slag was higher than that of the mortar bars with natural aggregate. However, the expansion was within the permitted limit at 14 days, even in the specimens that only used OPC as the binder. Therefore, according to the ASTM C 1260 standard [23], both the steel slag and natural fine aggregate specimens had stable ASR expansion.  
Previously studies have reported that mineral admixtures such as fly ash, blast furnace slag, and other siliceous binders can effectively reduce ASR expansion [26; 27]. [PERSON] et al. insisted that fly ash reduces the alkalinity of pore solution by reducing the ion diffusion in mortar. So, the penetration of external NaOH and mass transport is reduced, resulting in a lower pore fluid alkalinity, and significantly slower ASR [28].  
Figure 2: Immersion expansion rates of aggregate.  
As for blast furnace slag, its hydration in cement-water systems reduces the rate of alkali diffusion to aggregate reaction sites, thereby reducing the ASR rate [29]. Blast furnace slag also has a higher specific surface area than cement and dilutes the alkali content in mortar [21; 27]. In this study, we also verified the effects of mineral admixtures on ASR expansion, and the test results are shown in Figure 3. As shown, the mortar bars with mineral admixture had lesser ASR expansion. The experimental results obtained herein corroborate the findings from previous studies and show that fly ash offers a greater reduction in ASR [30] when natural sand is used as the fine aggregate. Because the pozzolanic reaction is accelerating dramatically at the high temperature and it promoted the reduction of porosity and pore size. Besides, the resistance of expansion by ASR is greater FA20 than BFS30 even if the lower replacement of FA20. In contrast, for mortar with steel slag aggregate, the reduction in ASR with blast furnace slag was greater than that with fly ash. To meet the particle size specification prescribed by ASTM C 1260 [23], the steel slag must be crushed. The crushed steel slag contains many inner cracks and surface pores. The blast furnace slag, with its high specific surface area, contributes to filling the surface pores of steel slag and improves the ASR expansion resistance of the mortar. Consequently, the expansion is affected by the aggregate and mineral admixture types, to use mineral admixture for reducing the expansion of steel slag due to ASR, the evaluation of its properties should be performed.
# Bioengineered Skin Substitutes: Advances and Future Trends
## 3 Cell-Based Skin Substitutes
### Cell-Laden Commercial Skin Templates
#### 3.1.3 Dermal Templates  
The dermis is the main layer of human skin with approximately 1 mm thickness. The Young's modulus of this layer (88 to 300 kPa) is much lower than that of the human epidermis, but it is stiffer than the subcutaneous fat layer (around 34 kPa) [27]. For dermal reconstruction, a bioscaffold with appropriate physical features, cellular cues, and mechanical properties, such as its elastic modulus and strength, which should be in the range of human dermal tissue, is required [45].  
Dermagraft (Smith and Nephew, Largo, FL, USA) is a dermal substitute containing allogeneic human fibroblasts cultured in a polyglactin scaffold [46]. This scaffold is bioabsorbable, giving the product its structure and mechanical properties. Dermagraft is provided frozen in a clear bag containing one piece for a single-use application. It can be used for full-thickness diabetic foot ulcers for a long time and for deep necrotic cutaneous ulcers, which do not involve the tendon, muscle, and joint capsule or bone [46; 47]. To engineer this dermal substitute, the fibroblasts are harvested from neonatal human foreskins and are cultured in vitro on a bioabsorbable polyglactin scaffold, which degrades by the hydrolysis process in 20-30 days [48]. Meanwhile, the fibroblasts proliferate to fill the interstices of this scaffold and secrete human dermal collagen, matrix proteins, growth factors, and cytokines. Thus, this bioscaffold creates a 3D human dermal substitute containing metabolically active living cells, helping to reconstitute a dermal layer. Numerous clinical studies demonstrated the efficiency of Dermagraft to heal chronic or difficult-to-heal wounds [49; 50; 51; 52; 53]. In a particular study by [PERSON] et al., 18 patients with venous ulceration of the leg were recruited for the pilot study [52]. In this regard, 10 patients were treated with Dermagraft and compression therapy, and 8 patients of the other group were treated with compression therapy alone (control). The study demonstrated that patients treated with Dermagraft healed approximately four times faster than the control group by the end of the 12-week study period. Thus, Dermagraft was suggested as a promising dermal graft for the healing of hard wounds. However, Dermagraft does not contain macrophages, lymphocytes, blood vessels, or hair follicles [49]. Further disadvantages of Dermagraftare the necessity of multiple applications, safety issues owing to allogeneic cells, and high costs [1].
## 2 Methodology
### Emergency Observation Mission Scheduling Model
#### 2.3.1 Constraint Analysis of Satellite Scheduling
\begin{array}{l}\forall i=[1,m],\forall j=[1,n],\\ CS(OS_{ij})-IS(OS_{ij})\geq MinT_{j},\\ CS(OS_{ij})-IS(OS_{ij})\leq MaxT_{j},\\ IS(OS_{ij})-CS(OS_{(i-1)j})\geq MinPeriod_{j}\end{array} \] (5)
3. Solar elevation angle constraint. For a visible light payload, sensor \(s^{\prime}_{j}\) of satellite \(s_{j}\) must satisfy the constraint of the solar elevation angle \(Sun\theta_{i}\): \[\forall i,j,tm:SunAngle(t_{i},tm)\geq Sun\theta_{i},tm\in ST_{ij}\] (6)
4. Observation angle constraint. The satellite payload and observation mission must meet the minimum observation angle requirement \(MinObser\theta_{i}\) specified by the user: \[\forall i,j,tm:ObserAngle(t_{i},s_{j},tm)\geq MinObser\theta_{i},tm\in ST_{ij}\] (7)
5. Storage capacity constraint. For satellite \(s_{j}\), the storage capacity of the observation mission between any two adjacent data transmission windows \(w_{1}\), \(w_{2}\) cannot exceed the storage capacity \(M_{j}\) of satellite \(s_{j}\): \[ \begin{array}{l}\forall j\in[1,n],\forall w_{1},w_{2}\in GSW_{j},w_{1}\
eq w _{2},\\ \sum\left(StorCap(t_{1},v_{j})+StorCap(t_{2},v_{j})+\ldots+StorCap(t_{i},v_{j}) \right)<M_{j}\end{array} \] (8)
6. Energy constraint. The energy consumed by satellite activities can be expressed as a correlation function of observation time, sensor side swing and on and off processes. The consumption cannot exceed the maximum limit \(P_{j}\). \(P^{\prime}_{j}\) is defined as the energy consumed per unit time of observation, \(Q_{j}\) is the energy consumed per unit angle of side swing, and \(O_{j}\) is the energy consumption associated with turning on and off the machine: \[\sum_{i=1}^{m}\left(eos^{I}_{ij}-bos^{I}_{ij}\right)\cdot P^{\prime}_{j}+\sum_ {i=1}^{m}\left|SA^{I}_{(i+1)j}-SA^{I}_{ij}\right|\cdot Q_{j}+NUM(E)O_{j}\leq P _{j}\] (9)
7. Satellite payload action constraint. For sensor \(s^{\prime}_{j}\) of satellite \(s_{j}\), the time interval of continuous observation missions cannot be less than the time of side swing adjustment and the time of stability after a side swing. \(V_{j}\) is the speed of the side swing, which is simplified to a constant speed, and \(ts_{j}\) is the time of stability after a side swing: \[ \begin{array}{l}\forall i_{1},i_{2}\in[1,m],i_{1}\
eq i_{2},\forall j\in[1,n ],\forall l_{1},l_{2}\in N_{ij},\\ bos^{s_{j_{2}}}_{i_{2}j}-eos^{s_{1}}_{i_{1}j}>\left|SA^{I_{1}}_{i_{1}j}-SA^{I_ {2}}_{i_{2}j}\right|/V_{j}+ts_{j}\end{array} \] (10)Imaging mode constraint for radar satellites. For a radar satellite payload, the working wave cannot be changed during the imaging process. \[ \begin{array}{c}\forall t_{i}\in T,mt_{ia},mt_{ib}\in MT_{i},a\
eq b,\\ WP(mt_{ia})=WP(mt_{ib})\end{array}  \tag{11}\]