esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 2 Related Works
The drawback of the reconstruction method is that when the training set contains anomaly samples, it leads to low reconstruction error for the anomaly samples. There are few studies on industrial anomaly detection and segmentation in the case of dirty datasets. TrustMAE [32] filters anomaly samples in the autoencoder latent space based on the distance of the sample from the center of the batch samples. Samples with a distance greater than a threshold from the centroid are considered anomaly samples. The disadvantage of TrustMAE is that at the early stage of model training, there are no labels to guide the encoder to extract distinguishing features, which can lead to the possibility that samples close to the center of the batch sample also be anomaly samples. [PERSON] et al. [33] use the feature vector of the latent space of autoencoder as a proxy to perform clustering and select clusters with low variance as normal samples. This process is cycled in the training process of the autoencoder. These two methods have a drawback: early in the autoencoder training process, the extracted feature vectors may not satisfy their assumptions. DFR [34] extracts the ImageNet pretrained network features, determines the optimal latent space dimension of AE by PCA, and then uses AE to reconstruct the extracted features. DFR is robust to noise because of the noise-reduction effect of PCA.
## 3 Results
### Characterization of Fucoidan from S. _licifolium_  
The FT-IR analysis of fucoidan extracted from _S. _licifolium_ was shown in Figure 4. The carbohydrate O-H stretching tremor was assigned to the bands at 3292.42 and 3258.45 cm\({}^{-1}\) (Table 3, Figure 4). The stretching and bending vibrations of the alkyl groups (-CH\({}_{2}-\), CH\({}_{3}\)) were assigned to the absorption band at 2930.59 cm\({}^{-1}\), indicating the existence of C=O stretching vibrations of O-acetyl groups. CH\({}_{2}\) was assigned to the band at 1412.35 cm\({}^{-1}\) (galactose, xylose). The presence of sulphate groups is indicated by the fucoidan characteristic absorption band at 1246.34 cm\({}^{-1}\) (S=O Stretching, Fucose, O-acetyl group). The absorbance at the wavelength of 1029.0 cm\({}^{-1}\) indicates C-O-SO3 vibration. The C-O-S bending vibration of the sulphate substitute at the axial C\({}_{2}\) and C4 positions were assigned to the bands at 882.96 and 816.06 cm\({}^{-1}\), respectively. Figure 5 depicts that the \({}^{1}\)H NMR range of fucoidan isolated from _S. ilicifolium_. The signal at 5.00 ppm represents the components of fucoidan L fucopyranosyl units, the 4.74 ppm signal represents the 3-linked \(\alpha\)-L-fucose, and the 4.56 ppm signal at \(\beta\)-D-Galactose (G) represents the 3,6-\(\alpha\)-L-anhydrogalactose residues. The 3.98 ppm represents the 3-linked 8-D-Galactose. The shape, diameter, and length of numerous polysaccharides have been characterized using AFM. The topography images of the fucoidan surface revealed the sample, soft template heights, and the bright spot. The AFM microscopy results exposed the peaks and troughs all over the surface. Figure 6 depicts various distorted forms with huge diameters and lengths. The size of the AFM was 390 nm. The shape and size of fucoidan were characterized by SEM examination, revealing the thread-like exterior morphology (Figure 7).  
Figure 3: Thermogravimetric and different thermogravimetric analysis of fucoidan from the brown seaweed _Sargassum licifolium_.  
Figure 4: FT-IR spectra of fucoidan fraction isolated from the brown seaweed _Sargassum_ licifollium.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**S. No.** & **Peak Area** & **Functional Groups** \\ \hline
1. & 2930.59, 2361.89\({}^{-1}\) cm & Stretching vibrations and bending vibrations of alkyl groups (-CH\({}_{2}-\) and -CH\({}_{3}\)) \\
2. & 1245.34\({}^{-1}\) cm & S=O Stretching vibration \\
3. & 1029.00\({}^{-1}\) cm & C-O-SO\({}_{3}\) vibration \\
4. & 882.96, & C-O-S bending vibration of the sulphate \\  & 816.06\({}^{-1}\) cm & substitute at the axial C\({}_{2}\), C\({}_{4}\) position.  
\end{table}
Table 3: The FT-IR analysis of _S. licifollium._  
Figure 5: H \({}^{1}\)NMR of fucoidan from the brown seaweed _Sargassum_ licifollium.
## 3 Coherent Pointing Estimation using Notch and Boresight Pattern
### Difference Pattern Approach
Hence, also the unique 180\({}^{\circ}\) phase shift at the notch position of the mono-pulse beam can be exploited in addition to the amplitude information for a more reliable pointing estimation.
## 4 Methodology And Implementation
### Metrics
#### 4.4.1 Levenshtein Similarity  
The Levenshtein distance, also known as the edit distance, is a string metric for measuring the difference between two sequences. Introduced by [PERSON] in 1965, it quantifies the minimum number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. This metric has found extensive application in various domains, including computational linguistics, DNA sequence analysis, and information retrieval. Formally, the Levenshtein distance between two strings \(a\) and \(b\) (of length \(|a|\) and \(|b|\) respectively) is given by \(lev_{a,b}(|a|,|b|)\), where  
\[lev_{a,b}(i,j)=\begin{cases}\max(i,j)&\text{if }min(i,j)=0,\\ \min\begin{cases}lev_{a,b}(i-1,j)+1\\ lev_{a,b}(i,j-1)+1\\ lev_{a,b}(i-1,j-1)+1_{(a_{i}\
eq b_{j})}\end{cases}&\text{otherwise.}\end{cases} \tag{1}\]  
Here, \(i\) and \(j\) are character positions of string \(a\) and \(b\), respectively, and \((a_{i}\
eq b_{j})\) is the indicator function equal to 0 when \(a_{i}=b_{j}\) and equal to 1 otherwise. The Levenshtein similarity providing a normalized score between 0 and 1 can be derived from the distance as  
\[sim_{lev}(a,b)=1-\frac{lev_{a,b}}{\max(|a|,|b|)} \tag{2}\]  
In the context of UDC codes, the Levenshtein similarity offers a robust method for comparing classification strings. Unlike the Jaro-Winkler similarity, which emphasizes prefix matching, the Levenshtein similarity treats all positions in the string equally. This property can be advantageous when dealing with UDC's complex notation system, where significant semantic differences may be encoded by changes in characters at any position within the code. For instance, consider the UDC codes \"621.39:004\" (telecommunications in relation to computer science) and \"621.38:004\" (electronics in relation to computer science). Despite their high visual similarity, these codes represent distinct technological domains.
## 4. Scope and Applicability
### Critical Infrastructures for Safety and Security  
The advent of smart cities is fostering the deployment of different types of infrastructures to complement and provide public security services with data to detect threatening situations. Systems such as smart street lighting infrastructures supporting the security and safety of citizens and goods in public spaces are gaining momentum. However, these systems need to manage the large quantity of data generated, e.g., by surveillance cameras, therefore, AI-based systems are required to automatically analyze and filter such data to report only on potentially threatening situations. The large variety of possible situations and the large quantity of data generated implies an obligation for the systems to be able to adapt to these continuously changing situations, which may also be completely different depending on the city neighborhood, seasons of the year or specific events., Machine learning technologies will be required to take advantage of algorithms that may need to be adapted and redeployed depending on the detected situations. In the case of smart lighting infrastructures, the CloudOps reference framework can provide support to the optimization of the deployment configuration in an environment with continuously changing requirements and a limited processing capacity. Additionally, the algorithms that analyze the data may suddenly need high computational capacity in specific locations and times, and permanently providing such computational capacity up to the edge would be neither affordable nor sustainable, so there is also the need of flexibility to take advantage of the computational power of other edge nodes or the cloud. To achieve this, runtime monitoring and self-healing mechanisms can support the operators in the operation of such complex environments, providing them with the flexibility to execute code at any time in the most convenient computing resource, the capacity to update and to re-deploy algorithms whenever required and keeping investment and operational costs affordable.
## 3 Results  
### Measurement of Pore Structure Parameters  
In this case, 18 sandstone slices with various physical properties were selected for analysis. The pore size and connectivity of the samples were determined by measuring the PP, APD, ASS, PSC and THC of the samples. The PP refers to the percentage of pore and throat area in the total area of the slice. The equivalent area circle diameter represents the APD. The ASS relates to the ratio of pore surface area to volume or the ratio of equal circle surface area to volume. The THC is the average pore diameter divided by the maximum pore diameter.  
Figure 4 displays the microscopic structures of the samples. Figure 5 shows an SEM image of the samples.  
Figure 4: Microscopic observation of sandstone samples. (**a**) Sandstone in the Luo’he formation; (**b**) Sandstone in the Zhi’luo formation; (**c**) Sandstone in the Yan’an formation; (**d**) Microfracture.  
Figure 5: SEM observation of samples. (**a**) Sandstone pores of the Luo’he formation; (**b**) Kaolinite; (**c**) Sandstone in the Zhi’luo formation; (**d**) Sandstone in the Yan’an formation; (**e**) Calcareous cementation; (**f**) Microfracture.  
The minerals of the sandstone in the Luo'he formation are dominated by quartz and feldspar, with a particle size of 500-1000 \(\upmu\)m. These are subangular, with poor roundness, poor sorting and pore cementation (Figure 4a). Intergranular pores are developed, and pore throat is large (Figure 5a). Feldspar dissolution occurs, and a large amount of kaolinite is formed (Figure 5b).  
The porosity of the sandstone in Zhi'luo and Yan'an formations is less than that in the Luo'he formation, indicating their poor packing pattern and intergranular space (Figure 4b,c). Mineral composition mainly constitutes quartz and feldspar. The particle size is mostly between 250 and 500 m, with pore type cementation and particle support, and some pores are filled with organic matter (Figure 5c,d). The quartz grains are granular aggregate, with calcite cementation (Figure 5e).  
The development of micro-cracks in the rock enhances pore connectivity and improves the rocks' water-bearing capacity under certain conditions (Figures 4d and 5f).  
As listed in Table 1, the PP of sandstone is 4.57%-20.15%. The APD ranges from 20.3 to 51.9 \(\upmu\)m. The ASS ranges from 0.51 \(\upmu\)m\({}^{-1}\) to 1.18 \(\upmu\)m\({}^{-1}\). The PSC is between 0.44 and 0.63, and the THC is between 13.59 and 28.74. Obvious differences occurred in pore structure parameters among various samples. The average PP, APD, ASS, PSC and THC of Luo'he sandstone were 19.17%, 47.94 \(\upmu\)m, 1.125 \(\upmu\)m\({}^{-1}\), 0.6116 and 27.515, respectively; those of Zhi'luo sandstone were 14.015%, 28.2 \(\upmu\)m, 1.02 \(\upmu\)m\({}^{-1}\), 0.5517 and 20.133, respectively; and those of Yan'an sandstone were 5.89%, 11.83 \(\upmu\)m, 0.58 m\({}^{-1}\), 0.473 and 14.81, respectively.
# Tracking of Stiffness Variation in Structural Members Using Input Error Function Observers
###### Abstract  
This study evaluates input error function observers for tracking of stiffness variation in real-time. The input error function is an Analytical Redundancy (AR)-based diagnosis method and necessitates a mathematical model of the system and system identification techniques. In practice, mathematical models used during numerical simulations differ from the actual status of the structure, and thus, accurate mathematical models are rarely available for reference. Noise is an unwanted signal in the input-output measurements but unavoidable in real-world applications (as in long span bridge trusses) and hard to imitate during numerical simulations. Simulation data from the truss system clearly indicates the effectiveness of the proposed structural damage detection method for estimating the severity of the damage. Optimization of the input error function can further automate the stiffness estimation in structural members and address critical aspects such as system uncertainties and the presence of noise in input-output measurements. Stiffness tracking in one of the planar truss members indicates the potential of optimization of the input error function for online structural health monitoring and implementing condition-based maintenance.  
real-time structural damage detection; input error function; optimization; analytical redundancy; system uncertainty; interaction matrix
## 2 Materials and Methods
### Data and Processing
#### 2.2.1 Pleiades Data and Processing
With those transformation functions, the individual geometries of each image in the image are determined by the same procedure as the original image. The RPC file is then used to define the initial functions for transforming the sensor geometry to image geometry. the triplet are then orientated relative to each other. To obtain the most accurate exterior orientation possible, the initial RPC functions were refined using tie points (TP). We used automatically identified and manually controlled TPs over the PA triplets, obtaining an overall root mean square error of less than 0.1 pixels (<0.05 m) shift between images at each TP within all of the acquisitions. For the data in the Hochjochferner area, 12 ground control points (GCPs) collected from a high-resolution orthoimage and an ALS DEM, which was available for the state of Tyrol from 2013 [36], were used in addition to the automatically collected TPs. GCPs for the Khumbu Himal study area were not available in a suitable spatial distribution, so the processing was solely based on the RPC files and TPs. The SGM algorithm was used to calculate point clouds (x, y, z) from each possible image pair (F-B, F-N, and N-B), which resulted in three point clouds for each Pleiades triplet. For a detailed description of the generation of the photogrammetric point clouds, see [PERSON] et al. [37]. The calculated point clouds for the N-B, F-N, and F-B combinations were then filtered for outliers, which are mainly found in very steep and shaded areas, using a local topographic three-dimensional (3D) filter. By merging the three point clouds, we obtained a dataset with a higher point density and less gaps, since the three image pairs offer higher chances of successful cross-correlation, especially in steep or shaded areas. The merged point cloud was then gridded to a spatial resolution of 1 \(\times\) 1 m pixel size using the average elevation of all of the points within one raster cell as the elevation value for the cell. Gaps were still present in very steep areas as well as in areas with low contrast because of clouds, fresh snow, or liquid water. Gaps only occurred in about 2% of the study area, but are irregularly distributed. The main problem with the calculation of the photogrammetric point clouds, and respectively the DEM, occurred in areas with low contrast due to relatively fresh snow. Those are mainly located in the highest parts of the Ngozumpa glacier (see Figures 3-5). The DEM shows a lot of noise in those areas, which results in an unrealistic representation of the terrain surface (see Figure 6). To account for the flat-water surfaces of proglacial lakes, we mapped unfrozen water bodies from the optical images and set those areas as flat surfaces in the DEM by using the mean value of the surrounding cells. Figure 6: Hillshade of the spring 2016 DEM showing part of the highest area of the Ngozumpa glacier. A problematic area due to snow cover is clearly visible in the area east of the ridge.
## 4 Conclusions  
We constructed DNA aptamer-protein hybrid molecules for use as BRET-based biosensors. Fusion proteins were enzymatically conjugated to DNA aptamers via the catalytic domain of porcine circumovirus type 2 replication initiation protein, which was positioned between NanoLuc luciferase and Venus. The catalytic domain of PCV2 Rep was shown to retain its DNA binding activity, even after elimination of the flexible regions at the N- and C-terminals. The resultant DNA aptamer-protein hybrid molecule exhibited a weak BRET signal, even in the absence of the target molecule of the DNA aptamer. However, the BRET signal was found to depend on the concentration of the target molecule. These results demonstrate the potential for use of the designed fusion protein as a DNA aptamer-based platform for construction of BRET-based biosensors.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/21/7646/s1](http://www.mdpi.com/2076-3417/10/21/7646/s1)]([http://www.mdpi.com/2076-3417/10/21/7646/s1](http://www.mdpi.com/2076-3417/10/21/7646/s1)), Figure S1. Evaluation of lysozyme-binding DNA aptamer-VpRNH conjugates with and without target molecules. Normalized emission spectra of lysozyme-binding DNA aptamer-VpRNH conjugate in the presence of BSA (A) and lysozyme (B). Change in BRET ratio (\(\Delta\)BRET) as a function of lysozyme concentration (C), Figure S2. \(\Delta\)BRET of thrombin of thrombin-binding DNA aptamer-VpRNH conjugate as a function of thrombin concentration (A).  
Figure 6: Change in BRET ratio (\(\Delta\)BRET) of thrombin-binding DNA aptamer-VtpRNH conjugate as a function of thrombin concentration in the presence of thrombin (orange circles) and BSA (black triangles) (**A**). Correlation of \(\Delta\)BRET and thrombin concentration between 200 and 1500 nM (**B**); each value represents the mean of 4 replicates (\(n\) = 4).  
Correlation of ABRET and thrombin concentration between 200 and 1500 nM (B); each value represents the mean of 4 replicates (\(n=4\)).  
Conceptualization, M.M.; methodology, M.M., R.H. and Y.M.; investigation, R.H., writing--original draft preparation, M.M.; writing--review and editing, Y.M. and E.K.; supervision, M.M. and E.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by JSPS KAKENHI Grant Numbers 16K01388 (M.M.) and 26289310 (E.K.).  
The authors declare no conflict of interest.
## 6 Lessons Learned
### Information Asymmetry  
A major reason for targeted attacks to be so successful is that the information available to the attackers and the defenders of systems is highly asymmetric: the attackers can obtain the commercially available security products, and fine tune their attack until these products do not detect it, while defenders have much less information about the methods and tools used by the attackers. One challenge is, thus, to break, or at least to decrease this asymmetry either by individualizing security products and keeping their custom configuration secret, or by gathering intelligence on the attackers and trying to predict their methods. The second approach seems to be more difficult and, due to its slightly offensive nature, morally questionable. Therefore, we argue that the (white hat) defensive security community should focus more on the first approach.  
One specific example in this vein would be the more widespread usage of honeypots and traps [28]. A honeypot is a decoy system component whose sole purpose is to attract attacks, and hence, to detect them as early as possible. The defender knows that certain events should never happen on a honeypot, and when they do indeed happen, it is a clear indication of an attack. For example, when a malware infected honeypot starts communicating with the attackers' remote C&C server, the very fact of this remote communication can raise an alarm. In this case, the asymmetry of information is decreased by the fact that the attackers do not immediately know which system components are real and which arethe decoys. Note, however, that the efficiency of this defense method is decreased by different honeypot detection techniques [29].
# The Framework of Cross-Domain and Model Adversarial Attack against Deepfake
## 3 CDMAA
### CDMAA Framework  
In summary, this paper proposes a framework of adversarial attacks on multiple domains of multiple models simultaneously. Using the I-FGSM adversarial attack algorithm as an example, the procedure of CDMAA is as follows (Algorithm 1):  
```
Input: original image \(\mathbf{x}\), iterative steps \(T\), perturbation magnitude \(\varepsilon\), step size \(a\), deepfake model group \(G^{(j)},j=1,\ldots,J\) Output: adversarial example \(\widetilde{\mathbf{x}}\) Initialization: \(\mathbf{x}_{0}=\mathbf{x}\)
1. For\(t=0\)to\(T-1\)do
2. For\(j=1\)to\(J\)do
3. \(L^{(j)}=\frac{1}{K^{(j)}}\sum\limits_{i=1}^{K^{(j)}}L\Big{(}G^{(j)}(\mathbf{x}_{t},c_{i}),G^{(j)}(\mathbf{x},c_{i})\Big{)}\)
4. \(\mathbf{grad}^{(j)}=\
abla_{\mathbf{x}_{t}}L^{(j)}\)
5. \(\mathbf{grad}^{(j)}_{not}=\mathbf{grad}^{(j)}/\frac{1}{\max\{L^{(j)},\varepsilon\}}\)
6. Endfor
7. \(\mathbf{w}=\underset{\mathbf{a}\in\mathcal{T}}{argmin}\left|\left|\sum\limits_{j=1}^{J }a^{(j)}\cdot\mathbf{grad}^{(j)}_{not}\right|\right|_{2}^{2}\)
8. \(\mathbf{x}_{t+1}=clip(\mathbf{x}_{t}+a\cdot sign(\mathbf{w}))\)
9. Endfor
10. \(\widetilde{\mathbf{x}}=\mathbf{x}_{T}\)
```  
**Algorithm 1** CDMAA  
Step 3 is the use of the uniformly weighting method to obtain the cross-domain loss function, which is sufficiently effective due to the similarity of the gradients between domains (Section 3.2). It needs only one backpropagation step to calculate gradient in the following step, while the MGDA needs \(K^{(j)}\) backpropagation steps to calculate the gradients in each domain, thus ensuring good efficiency.  
Step 7 is the use of the MGDA to obtain the cross-model perturbation vector, where a simple uniformly weighting method is less effective due to the low similarity of gradients between models (Section 3.3). Therefore, the MGDA is used to achieve better attacks at the expense of time. We use the Frank-Wolfe method [36] to approximately calculate the minimal norm vector in the convex hull of \(\mathbf{grad}_{nor}\), which has a well convergence in such cases that the number of dimensions is much larger than the number of vectors [35; 37].  
Figure 3 shows the overview of CDMAA. It is noted that CDMAA is not necessarily applied on the I-FGSM, although this paper uses the I-FGSM to introduce CDMAA. The main idea of CDMAA is to obtain a perturbation vector from gradients in multiple domains and models and then update the adversarial examples to ensure their ability of attacking multiple models and domains. Therefore, CDMAA can be applied to any gradient-based adversarial attack algorithms, such as the MI-FGSM and APGD.
# Prediction of Almond Nut Yield and Its Greenhouse Gases Emission Using Different Methodologies
## 3 Results and Discussion
### Comparison among ANN and ANFIS Approaches  
In general, ANFIS models were able to work with uncertain, noisy, and imprecise data, particularly the data related to agricultural production processes, thus these models are composed of ANN and fuzzy system models. As presented, the ANFIS and ANN models had good accuracy in predicting the nut yield. However, comparing the results revealed that the ANN estimated the almond nut yield more accurately than the ANFIS models. The results are contrary to the findings reported by [PERSON] et al. [45] to predict wheat yield and [PERSON] et al. [23] for tomato production in the greenhouse.