esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
## 3 Results and Discussions
### Results of Deep Autoencoder Training
Cluster 2, observed within the 250-375 kHz range across specimens, corresponds to fiber-matrix debonding or interfacial debonding, representing intermediate damage as the adhesive layer's integrity deteriorates under stress. This frequency range aligns with reports in the literature that associate 250-350 kHz and 250-375 kHz frequency bands with matrix/fiber debonding [68]. The predominant frequency band for a large number of signals in each JLS specimen was centered at approximately 250-375 kHz, suggesting that the load was transmitted through the adhesive layer, thereby initiating interlaminar crack growth and generating acoustic events. Figure 13: Typical AE frequency domain waveforms form JLS 3: Cluster 1; Cluster 2; and Cluster 3 based on latent features. Cluster 3, characterized by high frequencies between 300 and 500 kHz, is associated with fiber breakage, indicating catastrophic failure and the final stage of damage in the bonded CFRP specimens. This observation correlates with established findings in CFRP literature, where frequencies in the 400-500 kHz range are linked to fiber breakage and interlaminar cracking [68; 69]. Studies using supplementary techniques such as Digital Image Correlation (DIC) have validated that fiber rupture or pull-out events typically generate high-frequency AE signals within this range. For example, recent work by [PERSON] and [PERSON] has categorized delamination and fiber pullout within 400-600 kHz, while fiber failure is observed above 600 kHz [35]. Similarly, [PERSON] et al. identified interface failure within the 270-400 kHz range, with fiber breakage occurring at higher frequencies in open-hole CFRP laminates under tension-shear loading [32]. Our findings are further supported by studies on different CFRP configurations and adhesive applications. For instance, [PERSON] et al. associated matrix cracking (50-180 kHz), interface failure (180-400 kHz), and fiber breakage (400-670 kHz) in woven carbon fabric laminates under flexural loading [70]. [PERSON] and [PERSON]'s work on thick adhesives in wind turbine blades revealed microcracks between 80 and 110 kHz, matrix macroracks between 210 and 280 kHz, and fiber breakage in the 410-480 kHz range [71]. These comparisons underline a consistent correlation between specific AE frequency bands and particular damage modes in CFRP structures, reinforcing the validity of the AE analysis performed on JLS specimens in this study. Although this new approach has been developed, the old method will also be included. Therefore, a complementary study based on damage mode characterization using AE features will be discussed.
## 3 Results and Discussion
### Model Analysis  
In order to eliminate spectral noise in SERS and to improve detection accuracy, 400-1800 cm\({}^{-1}\) SERS was pretreated with four spectral preprocessing methods; then, it was modeled by PLS. In PLS, the sample set portioning based on the joint \(x\)-\(y\) distance (SPXY) method [33] was used to divide the samples into two groups, among which 80 samples were calibrated and 40 samples were predicted. Table 2 presents the modeling results of the calibration set and the prediction set with different spectral pretreatments and algorithms.  
On the one hand, the prediction accuracy of soil water-soluble nitrogen (\(0.88<R_{p}^{2}<0.91\), \(8.76\) mg/L \(<\) RMSE\({}_{p}<9.36\) mg/L, \(3.00<RPD<3.34\)) was similar when SERS was processed with those methods. Among them, the \(R_{p}^{2}\) of 1 st-der was the highest. The reason might be that the first-order method could distinguish between the overlapping peaks and eliminate interference from other backgrounds, which improved the spectral resolution and separated the main characteristic peaks  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Preprocessing**} & \multirow{2}{*}{**Principal Components**} & \multicolumn{3}{c}{**Calibration Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \cline{3-6}  & & \(R_{e}^{2}\) & **RMSE\({}_{e}\) (mg/L)** & \(R_{p}^{2}\) & **RMSE\({}_{p}\) (mg/L)** & **RPD** \\ \hline RAW & 5 & 0.90 & 9.16 & 0.90 & 9.36 & 3.10 \\ S-G & 5 & 0.91 & 9.17 & 0.90 & 9.17 & 3.00 \\ MSC & 5 & 0.90 & 9.28 & 0.90 & 8.98 & 3.03 \\ SNV & 5 & 0.91 & 9.19 & 0.88 & 9.16 & 3.24 \\ DT & 5 & 0.90 & 9.21 & 0.90 & 9.18 & 3.08 \\
1 st-der & 5 & 0.87 & 9.40 & 0.91 & 8.76 & 3.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The modeling performance with partial least squares (PLS) and different preprocessing methods. RMSE\({}_{e}\): root mean square error of the calibration set; RMSE\({}_{p}\): root mean square error of the predictive set; RPD: residual predictive deviation. RAW: raw spectra; MSC: multiplicative scatter correction; SNV: standard normal variation; DT: de-trending.  
Figure 6: Characteristic peaks of SERS at eight different concentrations of water-soluble nitrogen: (a) 118.3 mg/L; (b) 104.6 mg/L; (c) 91 mg/L; (d) 77.3 mg/L; (e) 63.7 mg/L; (f) 50 mg/L; (g) 36.4 mg/L; (h) 22.7 mg/L.  
for quantitative analysis [34]. On the other hand, no matter which algorithm was used, the prediction accuracy of soil water-soluble nitrogen varied in a small range, indicating that the experimental results were highly reliable. All the \(R_{p}^{2}\) values reached above 0.88, and the all the \(RPD\) values were more than 3, which indicated that the determination of water-soluble nitrogen in soil using SERS was effective.
## 4 Conclusions  
In recent years, finite element calculation as well as modern computer graphics and image processing capabilities have advanced by leaps and bounds, while electromagnetic experimental data remain abstract and difficult to understand. Finite element theory and artificial intelligence technology have achieved leapfrog development in the field of electromagnetics. It has become an inevitable trend to carry out electromagnetic experiment teaching through software. In terms of improving the quality of personnel training, strengthening the cultivation of students' practical and innovative abilities is the fundamental requirement. Experimental teaching is the most common practice method for students in school and is also the most direct way to verify knowledge.  
Based on finite element theory and neural network theory, a magnetic field visualization teaching platform was established using finite element calculation and MATLAB software. The experimental platform based on finite element theory is more accurate, but the calculation time is slower; the experimental platform based on neural network theory has a faster calculation time, but the numerical value is slightly wrong. The combination of the two uses the results calculated by the finite element theory as the training data and uses the neural network to establish an experimental platform, so that the results can be calculated more quickly when the experimental requirements are met. Compared with the finite element calculation method, the calculation speed of the magnetic field distribution calculated by the neural network is faster. Taking the calculation of the magnetic core inductance magnetic field as an example, the calculation time can be shortened by about 170 times.  
The electromagnetic experiment teaching platform based on a neural network embeds new theoretical knowledge that can not only show students the process of realizing electromagnetic experiments using a deep learning method, but also provide a resource sharing platform and software support services for related scientific research work. The platform can play an important role in improving teaching quality, improving students' interest in learning as well as their practical ability, and have a positive impact on cultivating comprehensive and innovative talents.  
Conceptualization, G.Y.; methodology, G.Y. and H.L.; validation, G.Y.; writing--original draft preparation, J.L. and D.K.; writing--review and editing, Z.W. and F.L.; project administration, G.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Comparison of magnetic flux density calculation for nonlinear materials.  
**Funding:** This research was funded by the S&T Program of Hebei (Grant No. 20550901K and No. 21557612K) and University-Industry Collaborative Education Program (Grant No. 201902307005 and No. 202002158011).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimized Dissolved Oxygen Fuzzy Control for Recombinant _Escherichia coli_ Cultivations
## 4 Results and Discussions
### DOC Fuzzy Controller Application in _E. coli_ Cultivations
#### 4.2.1 Initial Experiments  
Initial tests consisted in _E. coli_ cultivations, partially supervised by the fuzzy controller. In one of them, the decision tree was responsible for DOC control up till the eighth h, whilst the remaining period was overseen by the fuzzy controller (Figure 8).  
Figure 8: Initial _E. coli_ cultivation tests results. Fuzzy controller was activated for almost two h during the cultivation. Dynamics of air (**a**) and oxygen (**b**) flow rates, dissolved oxygen behavior (**c**), and experimental adjustment in limits for QairVar and QO\({}_{2}\)Var (**d**) during the _E. coli_ cultivation test.  
The first period (data suppressed) was essential in order to stabilize the process, in some extent, for the fuzzy controller. Figure 8a,b show air and oxygen flow rates dynamics (respectively). The abrupt fall of oxygen flow rate was due to pressure loss in the gas line, which caused the dissolved oxygen depletion, as shown in Figure 8c. After pressure regularization, the elevated air flow rate was replaced by oxygen enrichment, leading to oscillations in DOC. This behavior contributed to a frequent tuning of QairVar and QQ\({}_{2}\)Var limits during the re-stabilization period. However, these variations were small (maximum deviations of \(-0.80\) and \(+0.53\) from simulated values). Therefore, adjusted QairVar and QQ\({}_{2}\)Var parameters were close to the simulation results (Figure 8d).  
Overall, the fuzzy controller was able to kept the dissolved oxygen concentration near its setpoint value (30%) during the cultivation with a fast recovery after a critical deoxygenation period. The subsequent oscillatory period was short due to the adjustable QairVar and QQ\({}_{2}\)Var range values, which proves the robustness of the controller parameters. However, fluctuations in DOC were observed before the disturbances, which led us to pursue further adjustments in the fuzzy controller structure.
## 4 A Real Case Study
### The Number of Required Operators  
We applied the allocation procedure described in Section 3.5 to determine the number of operators needed. The load imposed by each machine on an operator was calculated as the product of the number of product types assigned to the machine and the fixed setup time per product. These values, representing the total setup time per machine, are presented in Table 2 and were used to guide the machine-to-operator assignment. The feasibility of assigning multiple machines to a single operator was explained in Section 4.2.  
Our analysis indicates that machines 1 through 4 can be assigned to a single operator without violating either the total load constraint or setup-time overlap constraints. However, machine 5 must be assigned to a second operator. The mathematical model (5) failed to yield a feasible solution when all five machines were assigned to one operator. Therefore, the result of our algorithm is a solution that requires two operators.  
Specifically, Operator 1 is assigned to machines 1-4, and Operator 2 is assigned to machine 5. We note that this solution is not necessarily optimal and is not balanced between the two operators. The heuristic algorithm does not explore all possible assignments; it maintains the original production sequence and does not permit reallocation of slot times to further minimize the number of operators. In addition, the algorithm preserves all idle times at the end of the cycle rather than distributing them throughout the schedule, which may further limit optimization.  
Figure 6: Donut chart showing the production cycles of Machines 1–5 assigned to operators. **Legend: Blue/Gray—**Production time; **Red—**Setup time; **Yellow—**Idle time.
## 4 Discussion
### Destructive Measurements  
After the NDE measurements were carried out, the DED sample was sectioned, polished and micrographed. The micrographs of the DED sample are shown in Figure 9 along with a reference micrograph of conventional IN718. In the conventional sample in Figure 9a, the grains are somewhat equiaxed and show a uniform grain size distribution. The presence of Laves phases (white outlines in the grain boundaries) can also be observed, which is once again consistent with known microstructures. Figure 9b,c shows the DED sample in the X-Y and Z planes. We can observe the following: (i) micro- and macrocolonies of grains with non-uniform grain size distribution, (ii) dendritic or long slender grains [47], and (iii) strong grain boundary precipitation of metastable Laves phases [48]. A separate phase analysis was also carried out using X-Ray diffraction (XRD) and details are reported elsewhere [40]. The phase distribution was found to be similar between the conventional and DED structures. However, in the DED structure, the \(\gamma^{\prime\prime}\) phase of \(Ni_{3}Nb\) did not precipitate due to the rapid solidification and segregation of Nb as Laves phases [40]. These micrograph scales were chosen to show the \"macrograins\". At the micrograin scale in Figure 9b, a network of connected white lines resembling a cellular structure can be observed. At the lower magnification in Figure 9c, the larger macrograins become visible (as outlined in orange).  
Figure 8: Backscatter C-Scan of the sample from different directions; (**a**) D1, (**b**) D2 and (**c**) D3. The ultrasonic beam was focused at the mid-plane of the sample. The spatial heterogeneity can be observed in the amplitude.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 4 The PingBasedDown Algorithm
### Link Evaluation Model  
Link evaluation model is proposed to evaluate the link quality and solve the LEP. Specifically, this model provides an implementation of function \(s^{\prime}_{l}\). In addition to the two original states, a fuzzy state Pending is introduced to avoid any arbitrary judgments. This model consists of the following five modules and Table 3 shows the parameters of this model. First is Noise filtering module, which filters the noise brought by applications from the collected RTTs. The filtered input then goes to the next module named Jitter accumulating module to quantify the jitter. The filtered input also goes to the module named Latency estimating module, which estimates the pure latency, i.e., latency without processing or retransmitting delay, of the link. The quantified jitter and the estimated latency then go through the Normalization module to calculate a normalized result. Finally, the result is compared with two thresholds to evaluate the state of the link. Next, we make detailed description on these modules.
## 1 Introduction
The statistical characteristics of the output SNR of a dual-branch SSC receiver are presented in Section 3, while Section 4 gives the mathematical expressions for the MGF and the moments of the output SNR of multi-branch SEC and SECps in the i.i.d. case. Moreover, Section 5 shows a thorough performance analysis of SSC, SEC, and SECps. Numerical and simulation results are considered and discussed in Section 6. Finally, the main conclusions are summarized in Section 7. The acronyms used across the paper are listed in Table 2.
## 4 Case Study
### Discussion
#### 4.2.2 VPP Operating Result Analysis
Nevertheless, in the future, the benefits impaired by PVP1 will be compensated for during the distribution of cooperation benefits, thereby ensuring the equitable distribution of benefits and maximizing the benefits of each member. Since the heat load of PVP1 can merely be procured from VPP to fulfill its heat demand, PVP1 can only reduce the heat purchase cost by minimizing the heat load when the heat price is elevated. The analysis of PVP2 and PVP3 is analogous to that at this stage, and thus we will not elaborate. Figure 10: Results of electrical energy interactions between PVP members for PVP1. As depicted in Figure 11, the general trend of electric energy transaction prices among PVPs is largely consistent with that of purchasing electricity from the upper-level grid, as all PVP members aspire to gain more benefits from electric energy transactions. Nevertheless, it can be discerned from the figure that during the periods when PVP1 and PVP3 have a relatively higher photovoltaic output, the transaction electricity price between them is merely marginally higher than the feed-in tariff. This is due to the fact that everyone's photovoltaic output is elevated at that time, and there is even a surplus of electricity. At such times, only by reducing the transaction electricity price can other members be enticed to purchase electricity. Through point-to-point electric energy transactions among PVP members, they are capable of having the pricing power of electric energy transactions and can better ensure their profits. Simultaneously, they can diminish their reliance on the upper-level VPP.
## 5 Experimental Results and Discussion
### Subjective Evaluation of the Proposed Watermarking Model  
The subjective evaluation aims to ensure that the watermarked images preserve high visual quality as perceived by human observers, considering factors such as image clarity, overall quality, and the presence of distortions. By leveraging the synergy of the FIM and the AEM, we can obtain the encoded image \(F_{en}\) as described in Equation (4). By optimizing the loss function presented in Equation (5) during our FIAE-GAN model training, we aim to minimize the disparity between the original and watermarked images, thereby enhancing invisibility. Figure 8 demonstrates the subjective invisibility of FIAE-GAN, presenting both the original and the encoded images. As shown in Figure 8a,b, the original and encoded images are visually indistinguishable, indicating that the watermark has been successfully embedded in imperceptible areas.  
Additionally, Figure 9 compares the histograms of the cover and watermarked images to evaluate the subjective performance of the proposed scheme. This comparison examines the distribution of pixel values in the watermarked image relative to the original image to gauge the extent of distortion caused by the watermarking process. The results show minimal variation in pixel values between the two images, indicating high visual quality with negligible distortion.  
Furthermore, Scale-Invariant Feature Transform (SIFT) is employed to assess the effectiveness of the watermarking technique by comparing the feature descriptors of the original and watermarked images [44; 45]. SIFT is a widely used  
Figure 8: Watermarking performance of FIAE-GAN. (**a**) Original image. (**b**) Encoded image.  
that detects and describes local features in an image by identifying key points such as edges, corners, and blobs. Its ability to generate robust, repeatable descriptors invariant to scale, rotation, and lighting conditions makes it ideal for assessing the impact of watermarking on the original image content. As illustrated in Figure 10, the results demonstrate a significant number of matching descriptors, with minimal distortion observed between the images.
## 2 Related Works  
### Background and Definitions of IOTA Technology  
IOTA is a decentralized, open-source cryptocurrency and distributed ledger technology (DLT) designed to address the limitations of traditional blockchain systems, particularly in the context of the Internet of Things (IoT). Unlike conventional blockchains, which rely on miners to validate transactions, IOTA uses a structure known as the \"Tangle\". The Tangle is a Directed Acyclic Graph (DAG) that offers a scalable and efficient alternative to blockchain's linear transaction structure. In this system, each new transaction must confirm two previous transactions, creating a web-like structure that grows exponentially as more transactions are added, eliminating the need for miners and allowing for feeless and faster transactions.  
The concept of decentralized identity management is deeply intertwined with IOTA's vision of a more connected and autonomous world. Traditionally, online identities have been centralized and controlled by institutions such as governments or corporations. However, with the increasing demand for privacy and control over personal data, decentralized identity management systems are emerging as a promising solution. In such systems, individuals can control their own identity data without the need for intermediaries, creating a more secure and transparent ecosystem.  
IOTA's Tangle plays a key role in this new paradigm by providing a secure, immutable ledger for storing and verifying identity-related data in a decentralized manner. Through the Tangle, users can create and manage Self Sovereign Identities (SSIs), which are digital identities that allow users to maintain full control over their personal data. By leveraging the benefits of the IOTA architecture--such as scalability, speed, and zero-fee transactions--the technology paves the way for more robust and private identity solutions that are not reliant on traditional centralized authorities. As the world continues to embrace digital transformation, IOTA's Tangle and its approach to decentralized identity management could play a crucial role in shaping the future of data privacy and online security.