esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Analysis of Image Communication System
#### 4.2.1 Performance Matrices  
BER: The Bit Error Rate (BER) is a standard performance metric in digital wireless communication systems to measure the quality of the reconstructed signal at the receiver. It calculates the ratio of incorrect bits to the total number of bits transmitted, typically expressed as a ratio. The BER is a crucial measure in determining the maximum achievable data rate for a given system design, considering the presence of noise and other sources of interference in the channel.  
PSNR: Peak Signal-to-Noise Ratio (PSNR) is an important metric to evaluate the quality of recovered images in image communication systems. Unlike BER, which indicates the performance of the communication system in bit level, PSNR provides a measure of the perceptual quality of the images by comparing the received image with the original transmitted image. PSNR is a full-reference image quality measure that uses an objective approach based on explicit numerical criteria, such as comparisons with ground truth or prior knowledge expressed in terms of statistical parameters and tests. The PSNR value is measured in Decibels (dB) and approaches infinity as the mean square error (MSE) approaches zero, implying that a higher PSNR value indicates a higher-quality image. A low PSNR value, on the other hand, signifies significant numerical differences between the original and recovered images [74].  
MS-SSIM: The MS-SSIM (multi-scale structural similarity index) is another full-reference image metric developed to compare the quality of an input image to that of a reference image with no distortion. Developed by [PERSON] et al. [74], the MS-SSIM is correlated with the quality perception of the human visual system (HVS). It works by aggregating the inner similarity indexes obtained from multiple spatial scales (resolutions) to estimate the overall similarity between the input and reference images. MS-SSIM is an improved metric of single-scale SSIM (SS-SSIM), resolving the limitations of being only suitable for limited visual contexts and unable to account for the vast visual diversity [75]. MS-SSIM is also measured in dB and the values are usually represented in log scale to observe high quality results, \(-10 log_{10}(1-M)\), where \(M\) is the MS-SSIM score.
## 4 Nonsingular Predefined-Time Attitude Controller Design
If the system states are in \[\overline{t}_{f}<t<T_{P}\]1, we have \[\overline{t}_{f}<t<T_{P}\]2 \[\overline{t}_{f}<t<T_{P}\]3  
According to Lemma 1, the attitude tracking errors will reach \(\mathbf{S}=\mathbf{0}\) or \(\Omega_{2}\) within a predefined time. If the system states are in \(\Omega_{2}\) when \(\psi\
eq 0\), the SMS \(\mathbf{S}=\mathbf{0}\) is still an attractor. The remainder of the study demonstrates that the curve \(\psi=0\) is unattractive except for \(\mathbf{S}=\mathbf{0}\). There is \(\mathbf{\Gamma}=\mathbf{0}\) when \(\psi=0\), and the control command \(\mathbf{M}_{c}\) is described as \[M_{c}=-\Big{(}\mathbf{A}\mathbf{R}\mathbf{J}^{-1}\Big{)}^{-1}\left( \begin{array}{c}\mathbf{A} \Big{(}\Big{(}\dot{\mathbf{R}}\mathbf{R}^{-1}+\mathbf{R}\mathbf{J}^{-1}\mathbf{\Omega}\mathbf{I}\mathbf{R}^{- 1}\Big{)}\mathbf{z}_{2}+\Big{(}\dot{\mathbf{R}}\mathbf{R}^{-1}+\mathbf{R}\mathbf{J}^{-1}\mathbf{\Omega} \mathbf{I}\mathbf{R}^{-1}\Big{)}\hat{\mathbf{\Lambda}}_{\text{C}}-\tilde{\mathbf{\Lambda}}_{ \text{C}}-\mathbf{\Phi}\Big{)}\\ \quad+\Theta\big{(}2+\frac{2a}{3}\big{)}\text{diag}\Big{(}|e_{1i}|^{1+2a/3} \Big{)}\mathbf{e}_{2}\\ \quad+\frac{\pi}{2\eta\mathbf{I}\mathbf{r}}\text{diag}\Big{(}|S_{i}|^{1-\eta/2}+3^{\eta /2}|S_{i}|^{1+\eta/2}\Big{)}\text{sign}(\mathbf{S})+\mathbf{P}\hat{\mathbf{e}}_{1}+\hat{ \mathbf{\Delta}}\end{array} \right)-\mathbf{M}_{air}. \tag{37}\]  
Differentiating \(\psi\) yields  
\[\dot{\psi}=-\frac{\pi}{2\eta T_{p}}\Big{(}|S_{j}|^{1-\eta/2}+3^{\eta/2}|S_{j}| ^{1+\eta/2}\Big{)}\text{sign}\big{(}S_{j}\big{)}. \tag{38}\]  
It is obvious that \(\dot{\psi}<0\) for \(S_{j}>0\) and \(\dot{\psi}>0\) for \(S_{j}<0\). Therefore, the curve \(\psi=0\) is not an attractor except for \(\mathbf{S}=\mathbf{0}\); the attitude tracking errors will transgress the area \(\Omega_{2}\) into \(\Omega_{1}\) monotonically. \(\square\)  
**Remark 3**. : _The transient and steady-state performance of the attitude tracking errors can be sufficiently guaranteed under the proposed PTPPF. Compared with the traditional nonsingular predefined-time controller [34; 35], the singularity can be avoided without using piecewise continuous functions._  
**Remark 4**. : _It is theoretically possible to design a very short convergence time for RLV attitude control, but this will lead to a very large control moment. Therefore, in practical application, the convergence time needs to be reasonably designed according to the mission requirement and the ability of RLV control system._
# A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions
## 2 SRDCNN-Based Intelligent Fault Diagnosis Method
### Framework of SRDCNN Model  
The overall framework of proposed SRDCNN model is shown in Figure 1. The structure of the SRDCNN model is composed of several residual dilated convolutional layers, the flatten layer and the Softmax layer. Residual dilated convolutional layer is a combination of the dilated convolution, the input gate structure of LSTM and a residual network, which is shown in the partial enlarged detail. For one sample, the length is reduced by half after getting through a residual dilated convolutional layer, which is the most important part of the network structure and will be explained in the next section. In order to adequately mine sample features, multiple filters are used at each residual dilated convolution layer. The flatten layer flattens a plurality of vectors and then flattens them into a one-dimensional vector. The last layer is a dense layer that uses the Softmax function to implement a multi-classification task. The value of each neuron in the output layer indicates the probability that the input sample belongs to a fault category. And the category of the input signal is the one with the highest probability in the output layer of the model.  
The input of the SRDCNN model is a sample taken from the bearing vibration signals using a sliding window. To ensure real-time performance, when a new sampling point is collected, the previous sample will discard its first sampling point and add a new sampling point at the end to form a new next sample. After the sample is input into the network, the neurons in each layer are all represented by solid spots. When a new sampling point is collected and used as the end point of the next sample, the outputs of every layer are represented by virtual spots and the dashed line represents the calculation process of the next sample. The points of the same color in Figure 1 are neurons of the same layer.  
Figure 1: The overall framework of proposed SRDCNN model.
## 6 X Security: ML/DL Solutions
### Detection of Vulnerabilities and Exploits on X
The performance of the model was evaluated using a manually labeled dataset, yielding an F1-score of 64%, surpassing the results of SVM, MLP, and CNN baseline models. Ref. [140] developed machine learning models to categorize cybersecurity-related X accounts. They collected cybersecurity-related tweetsusing X's Sampling API and manually labeled them. A baseline model was trained to identify general cybersecurity accounts, followed by sub-models for classifying accounts into individuals, hackers, or academia. Four machine learning models (Decision Tree, Random Forest, Support Vector Machine, and Logistic Regression) were compared using various account features. Random Forest achieved the highest performance, with 93% accuracy for the baseline model and 88-91% accuracy for the sub-models. Ref. [141] introduced Darkintellect, a machine learning-based approach for identifying cyber attacks through X data, The researchers collected approximately 21,000 cyber attack-related tweets using the Tweepy3 Python library. To prepare the data for analysis, they employed NLP techniques to preprocess the tweets by removing irrelevant information and special characters. Feature extraction was performed using TF-IDF to represent the text data numerically. Five machine learning algorithms--SVM, RF, DT, XGBoost, and AdaBoost--were evaluated for their ability to classify tweets as cyber attacks or not. The results demonstrated that Decision Trees (DT) outperformed the other methods, achieving a classification accuracy of 87.54%. The authors of [142] developed a hybrid NLP and CNN model to identify and categorize four cyber attack types (malware, phishing, spam, and bot attacks) within social network messages. This method uniquely focuses on textual analysis, making it adaptable across different platforms. Evaluated on real-world data, the model underwent a two-phase process. First, it detected the presence of any cyber attack, followed by classification into a specific category. The model achieved an overall accuracy of 82%. The efficiency of utilizing information disclosed on X to detect zero-day vulnerabilities and exploits has been established through several studies. However, it is noted that these solutions have limitations in terms of vulnerabilities, where a more comprehensive approach to detecting security content is necessary. This is since the retrieved tweets may not include specific vulnerability numbers, unlike the advanced counting-based secret-sharing security technique.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 4 Discussion
### The Role of Multi-Temporal Data in Parcel Extraction  
Agricultural production activities, due to significant human intervention, differ greatly from natural conditions. Consequently, time series information plays a crucial role. One notable example is the commonly used NDVI curve for crop identification [43; 44; 45]. In the context of parcel extraction, data collected at different time points provide varying information, which can be beneficial for accurately delineating parcel boundaries.  
Figure 11: **(a)** High-voltage towers, **(b)** areas of uneven gray scale, **(c)** cluttered areas, and **(d)** internally complex parcels will cause SAM to produce false segmentations.  
In this study, we conducted parcel extraction using Sentinel data captured at four different time points in the A2 study area. In Figure 12, we can observe that during the initial stages of crop growth in June, the vegetation characteristics within cultivated land were not distinct, whereas non-cultivated land exhibited obvious spectral characteristics. After the crops were harvested in late September, certain boundary information became more evident compared to the period when the vegetation was dense. It can also be seen from the parcel extraction results that there are certain differences in the extraction results of different temporal phases (the red and white circles in Figure 12). Furthermore, as shown in Table 5, the accuracy of extraction using single-temporal imagery was generally mediocre, with little variation in extraction accuracy between different temporal phases. However, merging prediction data from multiple time phases enhanced the accuracy of the extraction results, but incorporating additional input images inevitably increases computational costs. Therefore, how to effectively use multi-temporal data as a supplement in the process of parcel extraction is still a problem worth exploring. In future research, we plan to propose an approach that initially employs single-temporal images for the extraction process and subsequently incorporates suitable temporal data to supplement the extraction in areas that exhibit poorer results. This strategy aims to reduce computational costs while preserving accuracy and warrants further exploration.
## 3 Results
### Characterization of SDHI Change
#### 3.1.2 Spatial Variation in SDHI  
As can be seen from Figure 4a, the overall SDHI values in Inner Mongolia have shown a decreasing trend over the past 39 years. Specifically, 63.61% of the regions showed a significant decreasing trend in SDHI, mainly concentrated in northwestern Hulunbeier, Xilingol league, Ulanqab, and the border areas of Bayannur, Baotou, and Erdos. In terms of vegetation types, except for the desert area, the area with significant decreased SDHI was larger than the area with no-significant decrease in all other four vegetation types, among which the meadow steppe and typical steppe located in central Inner Mongolia showed a more obvious decreasing trend in SDHI, and the area with no-significant decreasing trend in SDHI was larger in the desert area (Figure 4b).
# Evaluation of Methods Based on CPTu Testing for Prediction of the Bearing Capacity of CFA Piles
## 2 Materials and Methods
### Cone Penetration Testing and Pile Bearing Capacity Prediction
#### 2.3.3 Eurocode 7--2 Method  
The method involved in Eurocode 7--2 is based on the Dutch standard NEN 6743-1 Geotechniek-Berekeningsmethode voor funderingen op palen-Drukpalen (Koppejan method) [57]. The maximal unit resistance at pile tip \(q_{b}\), which is limited to 15 MPa, is determined as follows (Equation (9)):  
\[q_{b} =0.5\ \times\ \alpha_{p}\ \times\ \beta\ \times\ s\left(\frac{q_{c,l \
u}+q_{c,l\
u z}}{2}+q_{c,l\Pi lz}\right)\ \leq\ 15\ \text{MPa}, \tag{9}\]  
where \(\alpha_{p}\) is the pile type coefficient (0.8 for CFA piles); \(\beta\) is the pile tip shape coefficient (\(\beta=1.0\) for circular piles); \(s\) is a coefficient of pile foundation base shape (\(s=1.0\) for circular piles); and \(q_{c,l\
u z}\) is an average of values \(q_{c,l}\) from depths below the pile tip limited by 0.7\(D_{eq}\) to 4\(D_{eq}\): where \(D_{eq}\) is an equivalent diameter of a pile at the tip; \(q_{c,l\
u z}\) is an average of smallest values of \(q_{c,l\Pi}\) above the depth going up from critical depth up to the pile tip; and \(q_{c,l\Pi lz}\) is an average of values \(q_{c,l\Pi l}\) from interval of depths within the interval of 8\(D_{eq}\) above the pile tip.  
Maximal unit shaft friction \(f_{p}\) is calculated as follows (Equation (10)):  
\[p_{max,shift,z} =\ \alpha_{s}\ \times\ q_{c,zz,q}, \tag{10}\]  
where \(\alpha_{s}\) is a pile type coefficient (\(\alpha_{s}=0.006\) for sand and sandy gravels, \(\alpha_{s}=0.02\)--0.03 for clays) and \(q_{c,zz,q}\) is the reduced value of \(q_{c}\) at the depth \(z\). If \(q_{c,zz,q}>12\) MPa continually with depth interval of 1 m or more, \(q_{c,zz,q}\leq 15\) MPa in this interval.
## 3 Test Results
### Load Carrying Capacity  
The yield loads (\(F_{y}\)) which initiate the yielding of truss members and the peak load (\(F_{p}\)) of all specimens are summarized in Table 3. It is shown from the comparison that the peak loads (\(F_{p}\)) of all specimens were increased roughly by 10% from the yield loads (\(F_{y}\)), except for the specimen TS-FC, whose peak load (\(F_{p}\)) and yield load (\(F_{y}\)) were not obtained because of the limit of test setup, which shows that yield load (\(F_{y}\)) and peak load (\(F_{p}\)) of specimen TS-FC are the largest among these four specimens. On the other hand, it can be seen from Table 3 that the yield loads (\(F_{y}\)) and peak loads (\(F_{p}\)) of specimens T-HN, T-HW, TS-AS, and TS-FC are in ascending order. Compared to the load carrying capacity of specimen T-HW, the load carrying capacity of specimen T-HN was decreased by 18%, and the load carrying capacity of specimen TS-AS increased by 36%, whereas the load carrying capacity of specimen TS-FC increased by more than 60%. It is illustrated from the comparison that being hidden weld welded or not influences the load carrying capacity, and the reinforcing method of filling concrete into the top and bottom chord members can improve the load carrying capacity of Warren CHS tubular trusses better than adding a half outer sleeve on each joint, which means the reinforcing method of filling concrete into the top and bottom chord members is much more efficient in practice.
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 3 Results and Discussion
### Biology
#### 3.2.2 Evaluation of the Antibacterial Activity by the Determination of IZDs  
The results of the in vitro antibacterial screening of the test compounds are listed in Tables 2 and 3. The inhibitory effects of all of the selected compounds **4**, **7**, **12**, **22**, and **23** were not detected against Gram-negative and Gram-positive bacteria by both methods. Hydrazone **23**, with a 5-nitro-2-furyl substitution, showed the inhibitory effect only against Gram-positive bacteria (_S. aureus_ and _B. subtilis_) strains. Figure 2: MBC and MIC ratios for compounds **1–26**. The antibacterial activity test results for the Gram-positive bacteria _S. aureus_ and _B. subtilis_ indicated that the size of the IZDs for both methods correlated with the amount of hydrazone **23**. It was determined by the agar well diffusion method that the IZDs for _S. aureus_ were 13.11 \(\pm\) 0.01 mm after using 25 \(\upmu\)g of a compound and increased to 18.15 \(\pm\) 0.21 mm when 50 \(\upmu\)g was used. The increase in IZDs was demonstrated after the treatment of _B. subtilis_ with different quantities of the compound as well. However, the size of the IZDs was greater for bacilli and formed 18.16 \(\pm\) 0.03 mm and 21.67 \(\pm\) 0.08 mm after using volumes containing 25 \(\upmu\)g and 50 \(\upmu\)g of compounds, respectively. The same was observed using the disk agar diffusion method with volumes containing 3 \(\upmu\)g and 5 \(\upmu\)g of the compounds. The IZDs were 6.73 \(\pm\) 0.01 mm and 14.03 \(\pm\) 0.01 mm for _S. aureus_ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multirow{2}{*}{**Compounds**} & \multicolumn{4}{c}{**Diameter of the Zone of Inhibition (mm)**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Gram-Positive Bacteria**} & \multicolumn{2}{c}{**Gram-Negative Bacteria**} \\ \cline{3-6}  & & _S. aureus_ & _B. subtilis_ & _E. coli_ & _S. enteritidis_ \\ \hline \multirow{2}{*}{**4**} & 25 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 50 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{7} & 25 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 50 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{**12**} & 25 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 50 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{**22**} & 25 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 50 \(\upmu\)g & NI & NI & NI & NI \\ \hline \multirow{2}{*}{**23**} & 25 \(\upmu\)g & NI & NI & NI & NI \\ \cline{2-6}  & 50 \(\upmu\)g & 18.15 \(\pm\) 0.21 & 21.67 \(\pm\) 0.08 & NI & NI \\ \hline \multicolumn{6}{c}{Ciprofloxacinacin, 5 \(\upmu\)g} & 26.00 \(\pm\) 1.41 & 26.50 \(\pm\) 0.71 & 31.50 \(\pm\) 0.71 & 29.50 \(\pm\) 2.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Zone of inhibition of the synthesized compounds **4**, **7**, **12**, **22**, and **23** against Gram-positive and Gram-negative bacteria by the agar well diffusion method.
## 2 Theory
### Scattering Model for Subsurface Bubble Defects  
The bubbles in the optical components can be regarded as spherical particles with a nanometer radius, while the surrounding medium can be considered a uniform background. Due to the distribution of bubbles within the components being random, the light scattered by the bubbles can be regarded as a random distribution of spherical particles interacting with light. If the size of the bubble particles is much smaller than the wavelength of the  
Figure 3: Schematic diagram of the dual-interaction model.  
visible light, the Rayleigh scattering theory is still applicable for studying the characteristics of bubble light scattering, even when dealing with random particle distributions. Figure 4 gives a schematic diagram of the composite scattering of bubble particles below the surface of the optical element.  
When the incident light irradiates the target surface at wavelength \(\lambda\) and angle of incidence \(\theta_{i}\), the pBRDF expression for a particle of radius \(a\) at a distance \(d\) below the smooth surface in the direction of the scattering zenith angle \(\theta_{s}\) and the relative azimuth angle \(\phi_{s}\) is as follows:  
\[\text{pBRDF}_{SSD}=\frac{256\pi^{4}}{\lambda^{4}}\bigg{(}\frac{{n_{1}}^{2}-{n _{2}}^{2}}{{n_{1}}^{2}+2{n_{2}}^{2}}\bigg{)}^{2}\,a^{6}\times\cos\theta_{i} \Big{(}{n_{2}}^{2}-\sin^{2}\theta_{s}\Big{)}^{1/2}|\gamma\delta|^{2}{n_{2}}^{3 }\frac{NF}{A}\times M_{ij} \tag{9}\]  
where \(n_{2}\) is the refractive index of the surface element material, \(|\gamma\delta|^{2}\) represents the depth of light penetration in the material, \(\gamma=\exp\bigl{(}in_{2}kd\cos\theta_{i}^{\prime}\bigr{)}\) is the phase delay produced by the refractive action of the surface of the optical element on the incident field, and \(\delta=\exp(in_{2}kd\cos\theta_{s}^{\prime})\) is the phase delay produced by the refraction of the surface of the element on the scattered field.  
In summary, by analyzing and calculating the pBRDF of the above four defects, the Mueller matrices of all types of microscopic defects on the surface of the optical element can be expressed as Equation (10). Among the 16 characteristic polarization detection elements, \(M_{11}\) is normalized and has no significant value. The detect and classify pockmarks, roughness, dirty particles, and SSBD on the surface of the optical element on the nanoscale can be achieved using the elements of \(M_{12}\) (\(M_{21}\)), \(M_{33}\) (\(M_{44}\)), and \(M_{34}\) (\(M_{43}\)).  
\[M=\begin{bmatrix}M_{11}&M_{12}&0&0\\ M_{21}&M_{22}&0&0\\ 0&0&M_{33}&M_{34}\\ 0&0&M_{43}&M_{44}\\ \end{bmatrix} \tag{10}\]
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.4.3 Comparative Experiments on Different Attention Mechanisms  
To validate the superiority of the MPCA attention mechanism, we compared mainstream attention mechanisms--including coordinate attention (CA), parameter-free attention (SimAM), and combined spatial-channel attention (CBAM)--at the end of the backbone network in YOLOv11n (augmented with BiFPN). As shown in Table 8, MPCA achieved 80.7% mAP50 for pulmonary organoid detection, surpassing the baseline by 0.8% and significantly outperforming other mechanisms, without notably increasing computational complexity.