esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Axial Compressive Stress and Internal Pressure on a Pipeline Network: A Review
## 2 Axial Compressive Stress  
A steel pipeline may be subjected to various external loads such as hydrodynamics stresses, axial stresses, and external collapse pressures. Past studies [18] have shown that external loads cause a higher reduction in the failure pressure of a pipe. This review will focus on the most important external load, the axial compressive stress. Axial compressive stress occurs when a pipeline moves [19; 20; 21; 22], especially in buried pipes, as large portions of the pipes are only restrained by soil friction, whereas above-ground pipelines are generally restrained by anchors and guides. The increase in the hydrocarbon demand also encourages hydrocarbon to be explored in areas with unpredictable geotechnical conditions, such as the Subarctic and the Arctic regions. These regions often see active landslides and geotechnical movements, which may cause axial compressive stresses on these pipelines [21; 22; 23; 24; 25; 26; 27].  
Pipe bends, the weakest link of the piping system, are subjected to the Bourdon effect. The bending moment results in the opening of the pipe elbow, which subjects the pipe to ovalization. As a result, the pipe experiences axial compressive stress and tensile stress. The former has a more significant impact on the pipeline's integrity [28; 29].
# Revisiting Label Smoothing Regularization with Knowledge Distillation
## 6 Conclusions  
Label smoothing is a critical regularization method for classification problems. The equivalence of LSR and knowledge distillation has caught the attention of researchers recently. In this paper, we proposed a simple, but effective teacher-free knowledge distillation method, LSR-OS-TC, without external knowledge or data, specifically LSR-OS-TC with a softer temperature model output and a manually designed teacher. We also analyzed the gradient of KL-loss in the LSR-OS-TC method and stated two matters that need attention for KD methods with manually designed teachers: multiplying \(\tau\) instead of \(\tau^{2}\) with the KL-loss and using a lower temperature. Experiments showed that LSR-OS-TC can perform consistently among models and datasets with the same hyperparameters. This consistency indicates that LSR-OS-TC can be a reliable substitution of label smoothing regularization. We believe that LSR-OS-TC is applicable to existing classification problems like speech recognition [23] and machine translation [24] directly and improves the performance more than LSR.  
Figure 8: Graph of test error vs. temperature \(\tau\) of met1 on the GTZAN dataset. Blue: LSR-OS-TC with factor \(\tau\); orange: LSR-OS-TC with factor \(\tau^{2}\).  
**Author Contributions:** Conceptualization, [PERSON]; methodology, J.W.; experiments implementation, J.W. and P.Z.; experiments design, J.W. and Y.H.; formal analysis, J.W.; investigation, J.W. and Q.H.; writing--original draft preparation, J.W.; writing--review and editing, Y.H., Y.L. and Q.H.; visualization, J.W. and P.Z.; supervision, Q.H. All authors read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by National Natural Science Foundation of China (NSFC) (61571192, 61771200).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## 5 Wire-Grid Sparse Antennas
### Occa Method
#### 5.4.4 Discussion and Future Work
An illustrative example is the approximation of a biconical antenna by radial conductors only, without transverse ones, because no currents flow in them due to the symmetry. On other hand, the OCGA itself can be modified as an optimization method to obtain the desired characteristics of the required antenna. Finally, it is possible to hybridize the OCGA with GA to optimize the structure designs, but this will be at the expense of computational costs. In addition, the analysis of the characteristic mode [303] of the antennas obtained using OCGA might help to understand the antenna's working principle, and to select its excitation location in order to obtain an optimal antenna structure, which should be considered in future studies of the OCGA method.
## 4 Conclusions  
Powders obtained from organic strawberries by the most commonly used methods--FD, CD and SD--are characterised by significantly different bioactive component contents, antioxidant properties and physicochemical and sensory properties. Samples obtained from fruit that were freeze dried and then ground into powders had a high content of vitamin C, polyphenic compounds and the highest  
Figure 3: Microstructure of raw strawberry and strawberry powders as shown by scanning electron microscopy (magnification 50x for raw strawberry and 500x for strawberry powders); (**a**) raw fruit, (**b**) FD powder, (**c**) CD powder and (**d**) SD powder.  
antioxidant activity, similar to raw strawberries. The SD and CD powders were characterised by 60% and 80% reduced vitamin C content, several times lower polyphenol content and significantly lower antioxidant properties, respectively. Powders obtained from strawberries, especially by the FD method, can be classified as 'high-value foods' due to their concentrated content of bioactive ingredients, as only 12 g of FD powder covers the body's daily vitamin C requirement, and a small addition to the food (about 5 g per portion) authorises it to be labelled with the nutritional statement 'high vitamin C content,' which means that this food contains no less than 24 mg of vitamin C.  
In the case of the CD and SD powders, their food addition should be 2-3 times higher to allow them being labelled with the abovementioned statement. The powders, depending on their method of preparation, had significantly different water binding capacity and solubility, determining their purpose. The WSI was the highest for the SD powders, which indicates that they can be used as 'instant' food components. Taking into consideration the high content of the bioactive ingredients and high operational cost of powders obtained by the FD method, these powders should be used mainly as a valuable component of high value foods and food supplements. Organic strawberry powders obtained by the CD and SD methods can also be used to enrich food with vitamin C of natural origin, but this requires 2-4 times higher addition levels compared to FD powders. These powders should primarily be widely used as natural organic food additives, replacing additional substances, including those marked with the 'E' symbol, and improving sensory properties, including the taste and colour of products.  
Conceptualization, A.S. and F.S.; methodology, A.S. and E.H.; formal analysis, A.S. and E.H.; investigation, A.S. and E.H.; data curation, A.S.; writing--original draft preparation, A.S. and F.S.; writing--review and editing, A.S.; visualization, A.S.; supervision, F.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. All authors declare that they have no conflict of interest.  
This article does not contain any studies with human or animal subjects.
## 3 The Principle of CEEMDAN-WP and RDT
### The RDT Method  
RDT is a basic means with which to extract structural free-attenuation vibration signals from random vibration response signals. The extraction is usually regarded as a structural random decrement signature (RDS), which indicates the free vibration of the dynamic system under a supposed stationary random vibration and certain initial conditions [40; 41]. In this study, the displacement response signal \(x(t)\) (i.e., Signal R2) is divided into a series of time sample functions \(x(t_{i}+\tau)\)(\(i=1,2,3,\cdots,N\)). \(N\) is the number of samples and \(\tau=t-t_{i}\). Furthermore, the RDS can be obtained via the average of the time segments \(x(t_{i}+\tau)\) of the structural response, which can be expressed as  
\[R(\tau)=\frac{1}{N}\sum_{i=1}^{N}\{x(t_{i}+\tau)|x(t_{i})=C\} \tag{15}\]  
where \(C\) is the initial condition assumed by \(x(t)\) at time \(t_{i}\).  
Based on the RDS, the natural frequency is presented as  
\[f=\frac{1}{T} \tag{16}\]  
where \(T\) is the measured period of the RDS.  
The damping ratio is obtained by a logarithmic decrement approach. The logarithmic decrement \(\beta\) can be expressed as  
\[\beta=\frac{1}{n}\ln\left|\frac{A_{i}}{A_{i+n}}\right| \tag{17}\]  
where \(A_{i}\) and \(A_{i+n}\) are the amplitudes of cycles \(i\) and \(i+n\), respectively. Ultimately, the structural damping ratio \(\xi\) is derived as  
\[\xi=\sqrt{\frac{\beta^{2}}{4\pi^{2}+\beta^{2}}} \tag{18}\]
## 5 Conclusions  
By including elastography as a parameter of the BIRADS score and considering both upgrading and downgrading the conventional BIRADS as opposed to only downgrading according to EFSUMB  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Se [\%]** & **Sp [\%]** & **PPV [\%]** & **NPV [\%]** & **ACC [\%]** \\ \hline
**FLR \textgreater{}= 4.5** & 79.57 & 95.20 & 51.39 & 98.35 & 94 \\
**FLR \textgreater{}= 4.34** & 81.72 & 94.05 & 49.35 & 98.64 & 93 \\ \hline \end{tabular}
\end{table}
Table 9: Optimal FLR values—diagnostic power.  
recommendations, a more efficient risk-stratification reporting system was established in the form of BIRADS TM. The proposed BIRADS TM risk-stratification system, while having slightly lower accuracy compared to BIRADS EFSUMB, yields better diagnostic value, as shown by higher AUROC values. Further development and standardization of the method may improve the evaluation of high-risk nodules.  
The second proposed risk-stratification reporting system in the form of the worst-case BIRADS shows the lowest accuracy, specificity and positive predictive values but, on the other hand, yields the highest sensitivity and negative predictive values of all the analyzed risk-stratification methods. This shows it is the most conservative approach, leading to the highest number of assumed malignant lesions and, in turn, to an increase in biopsy rates, which would be against our goal of reducing unnecessary invasive diagnostic method usage. For these reasons, we do not consider it advisable.  
The strong points of the study are the evaluation of a significant number of solid breast nodules and the assessment of multiple risk-stratification reporting systems combining classical ultrasound and elastography (both qualitative and semiquantitative methods), two being novel proposals and one (BIRADS TM) being shown to yield potentially better diagnostic value than the current EFSUMB recommendations.  
The limitations of the study are represented by the lack of histopathological results for all the evaluated nodules (only 93 of the malignancies were found as a result of histopathological investigations). The other nodules were diagnosed based on a combination of mammography, breast MRI and Tru-Cut biopsy.  
Conceptualization, D.S. and I.E.; methodology, D.S. and I.E.; software, F.B.; statistics, I.M.; validation, C.A.D. and D.S.; formal analysis, I.E. and D.S.; investigation, I.E. and D.S.; resources, D.S.; data curation, F.B; writing--original draft preparation, I.E.; writing--review and editing, D.S. and V.I.; visualization, D.S. and I.E.; supervision, D.S.; project administration, I.E. and D.S.; surgical intervention, C.D., M.C. and D.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Methodology
### Model Definition
In essence, the distillation loss function is the difference between soft student predictions and soft teacher labels. While student loss function is the difference between SM prediction and ground truth using the softmax function with the temperature of 1. The entire process can be compacted in an algorithm for easier comprehending, the algorithm of the proposed model is presented below (Algorithm 1):  
```
Input: Deep CNN model \(TM\) Output: Shallow Student model \(SM\)
1. Declare \(TM\), \(SM\), Temperature \(T\), Train_set \(X\) and Softmax function \(F\)
2. Initialize \(TM\), \(T>1\), = model weight
3. Input train set images \(X\)
4. Do train \(TM\): Forward pass

Apply Softmax with temperature (T) on TM logits to soften the labels

Distilled Knowledge \(Z\gets F(y)\)
5. End do
6. Initialize \(SM\) Model:
7. Feed distilled knowledge \(Z\), train_set \(X\)
8. Do while CE \(\leq 0\):

SM training phase I with \(z\) as target

\(SM\_{\text{loss}}=\text{F}(SM/T=1)\)

\(SM\) training phase II with \(X\) as target

Distillation_loss = F(\(TM/T\))

General Loss = \(\alpha(SM\_loss)+(1-\alpha)Dist\_loss\)

9. End do

10 Stop
```  
**Algorithm 1**Proposed Model Algorithm  
The above algorithm describes the process of formulating a knowledge distillation model, where the teacher and student models were built. The teacher model was trained on the full dataset with a softmax activation and a temperature variable T greater than 1 to soften the labels. The student model, on the other hand, was trained on the dataset and logits of the teacher model as a target which serves as distilled knowledge. Later on, the student model was trained on the full dataset with a temperature of 1 and ground truth as the target. The student model performance was as good as the teacher model with reduced parameters and low resource requirements.
## 5 Simulation Results  
In this section, the effectiveness of our approach is assessed by examining both the predictive performance and the underlying factors influencing the model's decisions. Before comparing the baseline LSTM and GA-Optimized LSTM models, it is instructive to determine which features in the dataset played the most critical role in detecting GPS spoofing. Consequently, we begin with an analysis of feature importance to shed light on the parameters that strongly impacted classification outcomes.
# Fine Recognition of MEO SAR Ship Targets Based on a Multi-Level Focusing-Classification Strategy
## 2 Signal Modeling and Characteristic Analysis of MEO SAR Ships
### Signal Model Establishment  
Let \(R_{p}(\eta)\) be the simultaneous slant range between the SAR antenna and point scatter \(P\). The echo signal can be expressed as [29]  
\[\begin{array}{ll}S_{p}(\tau,\eta)&=\sigma_{0}\cdot w_{r}\big{(}\tau-2R_{p}( \eta)/c\big{)}\cdot w_{a}(\eta-\eta^{p})\\ &\cdot\exp\big{\{}-j4\pi R_{p}(\eta)/\lambda\big{\}}\cdot\exp\Big{\{}j\pi K_{ r}\big{(}\tau-2R_{p}(\eta)/c\big{)}^{2}\Big{\}}\end{array} \tag{1}\]  
in which \(\eta\) is the azimuth slow time, \(\tau\) is the fast time along range direction, \(\sigma_{0}\) is the scattering intensity, \(w_{r}\) is the range signal envelope, \(w_{a}\) is the azimuth signal envelope, \(\lambda\) is the wavelength, \(c\) is the light speed, and \(K_{r}\) is the frequency modulation (FM) rate of the transmitted signal. For a static target, \(R_{p}(\eta)\) is a function of the shortest slant range. After range compression, the signal can be expressed as  
\[\begin{array}{ll}S_{p,rc}(\tau,\eta)&=\sigma_{0}\cdot p_{r}\big{(}\tau-2R_{ p}(\eta)/c\big{)}\cdot w_{a}(\eta-\eta^{p})\\ &\cdot\exp\big{\{}-j4\pi R_{p}(\eta)/\lambda\big{\}}\end{array} \tag{2}\]  
where \(p_{r}(\cdot)\) is the signal range envelope after range compression. The movements of a ship include translational motions as well as rotational motions caused by waves. The synthetic aperture time of an MEO SAR system ranges from several seconds to over ten seconds. The long aperture time leads to significant slant range errors caused by target motions. This  
Figure 1: Diagram of the proposed MEO SAR ship target recognition strategy.  
leads to a mismatch between the signal parameters of the moving ship and the traditional imaging algorithm, consequently resulting in azimuthal defocusing in the final SAR image. Point target simulation experiments are conducted to analyze the impact of ship motions on SAR imaging. The simulation parameters of the MEO SAR and LEO SAR are shown in Table 1. The resolution is set to 1.5 m. The orbit heights of MEO SAR and LEO SAR are 7500 km and 600 km, respectively.
# Research on the Trusted Traceability Model of Taishan Tea Products Based on Blockchain
## 4 Analysis of Blockchain-Based Taishan Tea Traceability Model  
### Blockchain-Based Taishan Tea Traceability Model Architecture  
In the planting stage and picking stage, Taishan tea planting presents a small-scale nature of multiple growers, which makes it difficult to deploy a unified IoT cluster and requires the targeted selection of suitable IoT devices for data collection. In the processing, warehousing, and logistics links, the production mode of Taishan tea is relatively fixed and large-scale, which supports the large-scale deployment of IoT clusters and the automatic sensing and collection of basic information. The whole industry chain of agricultural products is characterized by many nodes of participating subjects, a long industry chain, a wide range, a large amount of data, and multiple sources of heterogeneity; therefore, the principle of hierarchical data uploading is adopted to alleviate the pressure of the blockchain network.  
We have thoughtfully designed the architecture diagram of the Taishan tea traceability system, which is conceived based on our in-depth dialog with Taishan tea enterprises and extensive study of Taishan tea-related information. This architecture diagram adopts the form of a flowchart, which intuitively and clearly shows the complete chain from data collection driven by IoT technology to data refinement processing until the final application. At each key node of the Taishan tea industry chain, IoT equipment operates efficiently,accurately capturing and aggregating core data. Subsequently, these data undergo a series of rigorous data processing processes to ensure the accuracy, integrity, and security of the data, providing impenetrable technical support and data protection for the full traceability of Taishan tea products. The above is shown in Figure 3.
## 3 Results
### Impact of the NN in Hybrid Processing  
Figure 10 shows the impact of decreasing or increasing the number of neurons in the hidden layer for the NN. Figure 10a,b show the results for the hybrid two-stage SD/SC and hybrid two-stage SD, respectively. The delay is fixed considering a 12 km transmission. As a reference, a hybrid two-stage SD/SC with 16 neurons in the hidden layer with optimal delay is shown.  
Figure 10: Analyzing number of neurons for the digital equalization in the hybrid structure.  
Figure 9: Simulation results when comparing optimal delay and fixed delay for 12 km of fiber length for hybrid processing. The receiver topology considered in Figure 2 is highlighted inside the parentheses in the legend of the figure.  
Figure 10a shows that between 8 and 16 neurons is the optimal number of neurons in the hidden layer. For less than 8 neurons, the SNR penalty increases. For more than 16 we cannot see significant improvements. Although the NN could potentially find and adjust the optimal required delay, this is not happening even by increasing the number of neurons in the hidden layer. Increasing the number of neurons makes the convergence process more difficult. Figure 10b also shows that the ideal number of neurons in the hidden layer is also between 8 and 16. Similar behavior as Figure 10a for using less or more neurons is repeated here. Even considering the SNR penalty, using fewer neurons is still a possibility for the inter-data center scenario, because of the reduced complexity of the nonlinear equalizer.
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Ablation Study
#### 3.3.5 ASPP Module  
In order to verify the contribution of ASPP module to the model and the influence of different sampling rates on the experimental results, we choose the model without the ASPP module as the control group, and the rest three models set the multi-sample rates as [1,3,6,9], [1,3,7,11] and [1,4,8,12], respectively. The experimental results are shown in Table 6. It can be found that the detection accuracy of the model with ASPP module is higher than that of the control group while the highest detection accuracy is obtained when the multi-sample rate is set to [1,3,7,11]. Compared with the multi-sample rate set to [1,3,6,9], the module set to [1,3,7,11] can obtain a larger receptive field, so as to capture more contextual information, therefore improve the detection accuracy. However, the cracks are tiny, and the size of crack will be further reduced after down sampling. The excessive multi-sample rate will lead to the transformation of a \(3\times 3\) atrous convolution into a simple \(1\times 1\) convolution [39], so that the detection accuracy of setting the multi-sample rate to [1,4,8,12] is lower than setting to [1,3,7,11]. In practical applications, we have to consider the characteristics of the detection object, and choose the appropriate sampling rates, to achieve the best detection performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**ASPP Rate** & **Epochs** & **Accuracy** \\ \hline - & 100 & 93.49\% \\ [1,3,6,9] & 100 & 95.36\% \\ [1,3,7,11] & 100 & 95.53\% \\ [1,4,8,12] & 100 & 94.32\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between ASPP at different atrous rate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & **Epochs** & **Accuracy** \\ \hline Con\_1, Con\_2 & 100 & 95.47\% \\ Con\_2, Con\_3 & 100 & 96.87\% \\ Con\_3, Con\_4 & 100 & 95.88\% \\ Con\_4, Con\_5 & 100 & 94.81\% \\ Con\_5, Con\_6 & 100 & 95.05\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between SSE Module at different locations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Skipping Span** & **Epochs** & **Accuracy** \\ \hline Con\_5, Con\_6 & 100 & 95.05\% \\ Con\_4, Con\_6 & 100 & 95.30\% \\ Con\_3, Con\_6 & 100 & 95.64\% \\ Con\_2, Con\_6 & 100 & 95.88\% \\ Con\_1, Con\_6 & 100 & 96.62\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between SSE Module at different skipping span.