esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features
## 5 Comparison of Fault Isolation Results with Different Fault Features  
In this paper, the time-domain, frequency-domain and time-frequency domain features are used to isolation the misalignment fault. In order to compare the superiority of the extracted fault features, the paper also adopts time-domain indexes, frequency-domain indexes, IEMD energy entropy, and their combinations of any two kinds as the inputs of SVM, and the recognition results are shown in Figure 6 and Table 6.  
Figure 5: Fault isolation results of other algorithms. (**a**) SVM testing results; (**b**) GridSearch-SVM testing results; (**c**) GA-SVM testing results.  
From Figure 6 and Table 6, it can be seen that when the time-domain, frequency-domain and time-frequency domain features are used separately as inputs of the PSO-SVM isolation model, the accuracy of the training set and the testing set are low, and the recognition results are not good. When two kinds of features are combined as the inputs, the training set accuracy is above 91%, the testing set accuracy is more than 84%, and the recognition rate is increased. When the three mixed-domain features are combined, the correct rate of the training set reaches 97.9441%, and the accuracy of the testing set reaches 92.1053%. Therefore, it can be concluded that the mixed-domain  
Figure 6: Testing results of PSO-SVM using different input features. (**a**) using time-domain features only; (**b**) using frequency-domain features only; (**c**) using IEMD energy entropy features only; (**d**) using time-domain + frequency-domain features; (**e**) using frequency-domain + IEMD energy entropy features; and (**f**) using time-domain + IEMD energy entropy features.  
features combining three kinds of domain extractions proposed in this paper can effectively reflect the characteristics of the misalignment fault, and can be used to classify the fault types. It is also interesting to note that, whether used individually or combined with energy entropy, the time-domain features seem to provide better identification results than the frequency-domain data. This is because, when the wind turbine transmission system fails, it will produce impact components, and time-domain features can well reflect them, so better results can be obtained when using time-domain features.
## 4 Grade Prediction Method Based on DNNs
### Pearson Correlation Coefficient, Definitions of Distance and Thought on the KNN Algorithm
#### 5.1.3 Manhattan Distance  
The Manhattan distance [40] is used to calculate the addition of the absolute distances between two points in space in the coordinate system direction, as proposed by [PERSON]. The Manhattan distance between two points \(a(x_{11},x_{12},\cdots x_{1n})\) and \(b(x_{21},x_{22},\cdots x_{2n})\) in an n-dimensional space is expressed as Formula (5).  
\[d_{ab}=\sum_{k=1}^{n}\sqrt{(x_{1k}-x_{2k})^{2}} \tag{5}\]  
An improved Manhattan distance measurement method based on the Pearson correlation coefficient is proposed. Assuming that the n + 1 dimension is the dimension that must be calculated, it is weighted as per the correlation coefficient. Its expression is shown in Formula (6).  
\[d_{ab}=\sum_{k=1}^{n}\rho_{X_{k}X_{n+1}}\sqrt{(x_{1k}-x_{2k})^{2}} \tag{6}\]
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 4 The Charge Interchange between Capacitors
### Simulation of the Energy Recovery Phenomena  
Computer simulations help us to perform fast analysis of physical systems, and they are widely used in the field of power converters. This section presents a brief simulation of a resonant SC circuit. The intention is to have a quick idea about the stored energy behavior and to have some parameters which can be quickly replied in commercial software. The computer simulations applied to the dc-dc converters are a research field by itself; different approaches and commercial software may bring slightly different results and simulation times due to their convergence [118]. The simulation results discussed in this section were performed using the _Synopsys Saber_ software (Version 4.0, Synopsys Inc., Mountain View, CA, USA).  
The schematic is shown in Figure 24a, along with the main parameters. Two capacitors of 0.2 F are connected in parallel with different initial voltages. An ideal diode (no voltage drop) was inserted to prevent the current from becoming negative. The diode is ideal, but the inductor has a small equivalent series resistance (\(ESR=0.001\)) not shown in the schematic, only to prevent errors in the numerical simulation.  
Five simulations were performed with an end time of 0.2 s, and a time step of 1 us. The initial condition or initial voltages in the capacitors are calculated to have 10 Joules (initially). The ratio between the initial voltage in \(C_{2}\) over the initial voltage in \(C_{1}\) are: 0, 0.2, 0.4, 0.6, and 0.8. The specific voltages are listed in Table 3.  
The results graphics are shown in Figure 24b. The stored energy in the capacitors decreases until it reaches the value predicted by (5). That would be the final stored energy in a pure switched-capacitor circuit. It can be compared to Figure 21. However, after a time, the energy (stored in the inductor) is returned to the capacitors.
# Orbital-Based Automatic Multi-Layer Multi-Pass Welding Equipment for Small Assembly Plates
###### Abstract  
To address the technical challenges, production quality issues, and inefficiencies caused by the heavy reliance on traditional manual processing of small assembly plates in the shipbuilding industry, this paper presents the design and analysis of a track-based automatic welding device. This equipment provides a solution for achieving batch and continuous welding in the field of automatic welding technology. The design section includes the mechanical design of the equipment's core mechanisms, the design of the operating systems, the development of visual scanning strategies under working conditions, and the formulation of multi-layer and multi-pass welding processes. The analysis section comprises the static analysis of the equipment's mechanical structure, kinematic analysis of the robotic arm, and inspection analysis of the device. Compared with manual welding, multi-layer and multi-pass welding experiments conducted using the equipment demonstrated stabilized welding quality for small assembly plates. Under the conditions of single plates with different groove positions and gaps, when the gap was 4 mm, processing efficiency increased by 7.35%, and processing time was reduced by 10.2%; when the gap was 5 mm, processing efficiency increased by 10.7%, and processing time decreased by 7.39%. The welding formation rate for the overall processing of single plate panels and web grooves increased by 11.48%, total material consumption decreased by 13.4%, and unit material consumption decreased by 13.5%. For mass production of small assembly plates of the same specifications, processing time was reduced by 16.7%, and there was a 41.4% reduction in costs. The equipment effectively addresses the low level of automation and heavy dependence on traditional manual processing in the shipbuilding industry, contributing to cost reduction and efficiency improvement.  
Keywords:rail-mounted automatic welding equipment; small assembly plates; robotic arm; mechanical design; simulation analysis +
Footnote †: journal: applied sciences
# From Cyber-Physical Convergence to Digital Twins: A Review on Edge Computing Use Case Designs
## 5 Overview of the AI Market and Current State of the Telecommunications Industry
### Current State of the Telecommunications Industry
#### 5.1.1 Consumer Expansion into the Content Arena  
Since the emergence of smart phones, traffic patterns in wireless networks have had a dramatic change. This dramatic change is attributed mainly to the large number of applications introduced by 5G. Therefore, expanding into the content arena can be the simplest and most natural way for network providers that are seeking to grow in wireless communications, more so because user behavior and their traffic patterns are application-specific and they vary rapidly within short time intervals [4]. As a result, network providers have begun aggregating streaming platforms in parallel with launching their own services. The rising bandwidth requirements and the growth of the IoT means that network providers need to make near-constant upgrades to their infrastructure in order to keep up. Even though wireless infrastructure is quickly becoming less critical to profitability, the congestion in the radio spectrum has actually pushed the designers of 6G to adopt new spectrum bands to support 6G communications. Ultimately some network providers already have their own over-the-top (OTT) services or alternatives with favorable rates to their customers. To this effect, there has been a response from telecom companies in Sub-Saharan Africa, such as Vodacom, MTN, Airtel, Globacom, Orange, and Safaricom towards bolstering their OTT services. Based on market research, since 2017, MTN has been increasing the number of subscription offers in several countries in Africa  
Figure 1: Market forecast with respect to the AI market and Intel revenue forecast. (**a**) The global AI software market forecast from 2019 to 2025 [83]. (**b**) Intel revenues from two different company segments from 2018 to 2022 [84].  
south of the Sahara by launching various mobile applications such as access to television (TV) channels and streaming content. The company has been offering subscriptions that provide users with access to content only as well as a combination of access to content and internet data [85]. This means that algorithms for 6G communications must have higher performance than those of the existing 5G networks to operate on those various dimensions of 6G.
## 2 Materials and Methods
### Sample Pretreatment and DNA Extraction  
_Baijiu_ samples (5 g) were combined with 0.1 mol/L phosphate-buffered saline (PBS) solution (30 mL) in sterilized 50 mL centrifuge tubes, and these tubes were centrifuged at 9000 rpm for 5 min at room temperature. Subsequently, the supernatant was discarded, and the precipitate was resuspended in a 0.1 mol/L PBS solution (5 mL) to collect microorganisms. This process was replicated three times to ensure biological reproducibility. In order to extract the macro genomic DNA, the Simga-Aldrich GenEluteTM soil DNA isolation kit (Merck, Darmstadt, Germany) was utilized, following the manufacturer's instructions. The extracted DNA from the triplicate samples was pooled, and the quality and quantity of the mixed DNA were assessed via a NanoDrop ND2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The DNA concentration was adjusted to 20 nanograms per microliter. The integrity of the extracted DNA was verified through agarose gel electrophoresis and stored at \(-80\)\({}^{\circ}\)C until further analysis.  
PCR amplification and sequencing were conducted utilizing the previously established methodology [12]. The ITS2 region of the fungal ITS DNA gene was amplified using the primers ITS1: 5'-TCCGTAGGTGAAACCTGCG-3' and ITS4: 5'-TCCCTCCGCTTATTGTATATGC-3'. The PCR reaction mixtures contained: 1.0 \(\mu\)L of genomic DNA (20 ng/\(\mu\)L), 1.0 \(\mu\)L of each primer (10 \(\mu\)M), 0.3 \(\mu\)L of Ex Taq (5 U/\(\mu\)L), 2.5 \(\mu\)L of Ex Taq buffer (10\(\times\)), 2.0 \(\mu\)L of dNTP mixture (2.5 mM), and 17.2 \(\mu\)L of double-distilled water. PCR amplification was carried out as follows: the initial denaturation step at 94 \({}^{\circ}\)C for 5 min, followed by 30 cycles of denaturation at 94 \({}^{\circ}\)C for 5 min, annealing at 55 \({}^{\circ}\)C for 30 s, extension at 72 \({}^{\circ}\)C for 90 s, and a final extension at 72 \({}^{\circ}\)C for 10 min. The resulting PCR products were purified from a 1% (\(w/v\)) agarose gel and quantified using a NanoDrop ND2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
## 4 Discussion
### Comparison with ESFs  
The development of the COF as a function of sliding distance under dry sliding wear conditions for the ASF samples is different from that of the ESF sample (Figure 2). The curve of the ESF sample shows three distinct regions: an initial sharp increase in the COF as sliding distance increases, followed by a rapid decrease and finally a very slow, gradual decrease. The ASF samples also have an initial sharp increase, but the rapid drop region is not observed. The E-spheres have a relatively high porosity level and are weak. The weaker E-sphere particles on the surface of the ESF sample become more easily plastically deformed, which leaves the static partner to slide against largely open pores, reducing the contact area and decreasing the COF. The gradual decrease in the COF is due to the continuous deformation of E-sphere particles and reductions in contact area. The ASW particles are stronger and harder than the E-spheres due to lower porosities. They experience much less plastic deformation, leading to higher COF values (Table 2). Under the lubricated condition, however, the ASFs have very similar wear properties as the ESF, because lubrication dictates the wear behaviour and levels out many of the differences in morphology and composition between the ASFs and the ESF.  
Figure 6 shows the micrograph of the worn surface, micrograph of the static partner and wear track map for the ESF samples obtained under the same dry and lubricated sliding wear conditions. Under the dry sliding test condition, the track is worn evenly throughout the surface of the sample, which is similar to the worn tracks of the ASF samples. The static partner is unevenly worn, with an irregular wear surface and a significant amount of abrasive wear, which is caused by some pieces of the E-sphere particles broken off during the test.  
Overall, the morphology and characteristics of the wear tracks of the ASF samples are similar to those of the ESF sample and in fact similar to those of MMCs, indicating similar wear behaviour for these particulate-reinforced composite materials [31; 33]. However, the wear parameters are different between these materials, mainly due to the different mechanical properties.  
Table 2 shows that the S-ASF and M-ASF samples have a superior wear resistance than the ESF sample, while the L-ASF sample is inferior to the ESF sample, as seen in the differences in track depth and specific wear. The ASFs have lower \(\Delta\)Ra but higher \(\Delta\)Rz values, showing that they have overall less rough (lower \(\Delta\)Ra) but more even (higher \(\Delta\)Rz) surfaces than the ESF sample. The ESF sample loses more weight during the test, probably due to broken particles coming out as debris, which is not observed for the ASF samples.
## 3 Methodology and Models
### Data Collection and Preprocessing
#### 3.1.3 Data Cleaning  
Additional data cleaning was necessary to ensure that all features were in the correct format for modeling. Non-numeric columns were removed, and all remaining features were converted to a consistent numeric format. This step was crucial for the success of subsequent machine learning algorithms, which require numeric input data.
# Density Functional Theory Study on the Adsorption of Co(II) in Aqueous Solution by Graphene Oxide
## 4 Study on the Mechanism of GO Adsorption Process
### Influence of GO Structure on Adsorption  
GO can be divided into graphene sheets and oxygen-containing functional groups on them. The loose, porous structure of GO enables it to have strong physical adsorption capacity. In addition, a cation-\(\pi\) bond interaction can occur between the \(\pi\) bond and Co\({}^{2+}\) on the graphene sheet [16]. However, although the metal cation-\(\pi\) bond interaction can improve the adsorption capacity of a single GO to a certain extent, it also weakens the negative charge and hydrophilicity of GO, thus inducing the GO aromatic layer to form multiple structures and gather around the cation [17], resulting in a decrease in the overall adsorption efficiency of the adsorbent.  
Oxygen-containing functional groups on GO also play an important role in the adsorption of metal ions. By calculating the electrostatic potential of GO containing different functional groups in a stable configuration, it was found that the graphene sheet is mainly positively charged, while the oxygen atoms in the oxygen-containing functional groups on it are surrounded by negative areas. Among all functional groups, the oxygen atoms on the epoxy group are the most electronegative, followed by hydroxyl, carboxyl and base-edge hydroxyl groups. The more electronegative the group is, the easier it is to bond with metal cations. The electrostatic potential diagram of GO containing different functional groups in a stable configuration is shown in Figure 3.
## 2 Research Methodology  
We present an overview of the harmonic interference resilient backscatter system in Figure 2a. In this setup, the reader transmits a continuous wave signal or command at a frequency of \(f\). The backscatter tag reflects the incident signal while modulating its information, shifting the frequency of the backscattered signal to \((f+\Delta f)\). The reader's receiving end captures the tag's backscattered signal at the \((f+\Delta f)\) frequency point and decodes the received data packet. Subsequently, the EPC information from the backscattered signal is extracted for identification purposes by decoding. In addition, the suppression of harmonic interference during frequency shifting is achieved through software implementation. Next, we discuss the key components of this harmonic interference resilient backscatter system, including the adaptive pulse-width frequency shifter and the EPC encoder.
## 5 Experiments and Discussion  
To verify the LOS path planning and image motion compensation method, a series of simulation experiments were conducted. The applicability conditions of the compensation method were determined, and its limitations were analyzed.
# Visual MAV Tracker with Adaptive Search Region
## 4 Results
### Performance Evaluation  
The performance of the models was quantitatively compared in terms of accuracy, robustness, and EAOC of the test sequences. The results of the accuracy and robustness are listed in Table 2. CSR exhibited an accuracy of 0.613 and a robustness of 0.086. The accuracy of CSR is satisfactory. However, this accuracy is valid only under easily trackable conditions; in extreme conditions, CSR fails rapidly, and this failure is not considered in the calculations of the accuracy. In contrast, the robustness is low because of frequent truncations and subsequent failures. In the absence of SC and PP, SR can lose the MAV even if it moves slowly.  
SC exhibits a significantly improved robustness of 0.583. In contrast, the increase in accuracy by SC is negligible because the truncation of the target in SR cannot be corrected by SC. The optimization of SR size improves both accuracy and robustness. Although PP slightly reduces the accuracy, it significantly increases the robustness. This suggests that PP is necessary for the reliable tracking of dynamically moving targets.  
To verify the performance of the proposed model, tracking was also conducted with another recent tracker (i.e., Ocean [34]). This tracker was used for benchmarking because they achieved high scores in VOT 2018 and 2019. This model was fine-tuned using Dataset1, which was used in this study. The dataset was resized to fit the original dataset, and the trackers were trained for 500 epochs, which is sufficiently large considering the epoch numbers used in previous studies. The proposed model achieved similar or higher accuracy and robustness compared to this previous tracker, as shown in Table 2.  
The effects of SC, SO, and PP can also be evaluated using EAOC. If the EAOC value is high for a frame, accurate tracking is achieved, and failure does not occur in most frames of that sequence. Owing to early failure, the EAOC of the CSR noticeably decreases across the frames, as shown in Figure 11. Although SC and SO have an increased EAOC value, the value is low after 300 frames. In contrast, the EAOC value remains high when PP is applied, which suggests that PP is necessary for long tracking tasks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Accuracy** & **Robustness** \\ \hline CSR & 0.613 & 0.086 \\ CSR + SC & 0.632 & 0.583 \\ CSR + SC + SO & 0.846 & 0.685 \\ CSR + SC + SO + PP & 0.811 & 1.0 \\ Ocean & 0.756 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tracking performance comparison.  
Figure 10: Recovery example of PP.  
Among the four models used in this study, CSR + TP + SO + PP is the most accurate and reliable model for tracking. Thus, the inference time of the model was measured. When GeForce RTX 3090 GPU and Xeon Silver 4215R 3.20 GHz CPU were used for inference, the computation time was approximately 24 ms per frame (i.e., 410 FPS). This computation speed suggests that the model can be used for real-time tracking.
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Access Control and/or Authorisation Delegation
#### 6.1.1 Confidentiality  
It can be observed that all systems in this domain are likely protected against \(\mathcal{A}_{weak}\) in terms of confidentiality. \(\mathcal{A}_{weak}\) can only observe communications, but cannot corrupt system parties and subsequently is unable to extract secrets. Moreover, many protocols include strong security measures such as encryption, key management, secure transport layers (e.g., DTLS [21; 25]), and decentralised architectures (e.g., blockchain [17; 26; 27]) to protect sensitive data.  
Figure 12: Number of secure (green) and insecure (red) delegation protocols against three types of adversaries in terms of violating confidentiality, integrity, and availability for (**a**) Access Control and/or Authorisation, (**b**) Authentication, (**c**) Rights, and (**d**) Computation delegation application domains.  
When \(\mathcal{A}_{middle}\) is considered, a significantly smaller number of protocols are classified as secure. Although \(\mathcal{A}_{middle}\), similar to \(\mathcal{A}_{weak}\), lacks direct corruption capabilities, they may have accomplices within the system who assist them in obtaining sensitive data. These internal allies may potentially exploit vulnerabilities in access control mechanisms or gain direct access to cryptographic keys or secret information. For example, some protocols [25; 28; 29; 30] are vulnerable to such internal threats, because \(\mathcal{A}_{middle}\)'s accomplices with legitimate access privileges may bypass protection layers or misuse their access to retrieve encrypted data. In contrast, blockchain-based protocols [26; 31; 32; 33], which rely on immutable ledgers, provide stronger confidentiality guarantees in the presence of internal threats. They use cryptographic validation and distributed consensus, making it significantly more difficult for an insider to manipulate or access sensitive data undetected.  
As \(\mathcal{A}_{strong}\) is assumed to be a powerful adversary with extended access to the system and the ability to corrupt or manipulate critical system components, it is expected that more than half of the protocols under consideration are vulnerable. Similar to \(\mathcal{A}_{middle}\), most of the affected protocols rely on traditional access control mechanisms, which are more susceptible to insider threats and compromise. In contrast, blockchain-based protocols demonstrate greater resistance due to their decentralised architecture, which makes it significantly more difficult to bypass or manipulate system components, even by insiders.