esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Quality of Staggered PCD End Mill in Milling of Carbon Fiber Reinforced Plastics
## 5 Conclusions  
1. The experiment shows that the machining defects of CFRPs mainly include delamination, tearing and burrs. Tears are generally accompanied by burrs. Debonding and instability of CFRP surface material fiber is the cause of machining defects.
2. In trimming milling, the surface roughness increases with increasing feed rate and milling width and decreases with increasing milling speed. The feed rate has a greater influence on surface roughness. Increasing the milling speed and milling width can increase machining efficiency and improve machined surface quality.
3. In slot milling, when cutting along the fiber direction (fiber cutting angle 0\({}^{\circ}\)-90\({}^{\circ}\)), the machining surface has severe resin coating and burrs. In reverse cutting (fiber cutting angle 90\({}^{\circ}\)-180\({}^{\circ}\)), the machined surface has serrated fractures, and tear defects occur.
4. In slot milling, there will be no burr defects in the reverse cutting area, even if the tool is badly worn. Therefore, by choosing a reasonable milling path and ensuring the surface fiber is in the inverse cutting state, a prolonged tool life and good surface quality can be obtained.  
The authors would like to acknowledge the support of the International S&T Cooperation Program of China (Grant No. 2014 DFA70400), the Program for New Century Excellent Talents in Heilongjiang Provincial University (Grant No. 1254-NCET-007), and the Fundamental Research Funds for the Central Universities. [PERSON] and [PERSON] designed and conducted the CFRP milling experiment. [PERSON] established the regression model of surface roughness. [PERSON] and [PERSON] analyzed the effects of fiber orientation on the machined surface.  
The authors declare no conflict of interest.
# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
## 2 Method
### Source-Free Image Generation Module  
The existing unsupervised domain-adaptive vehicle re-identification methods usually need to access the source domain data, and transfer the well-marked source domain data to the unmarked target domain style through style-based transfer [18; 19] or a generative confrontation network [20; 21], so as to smooth domain bias to better apply source domain models to target domain data. However, in cases where certain data are subject to security and privacy restrictions, accessing source domain data can be extremely challenging. To address this issue, this paper constructs a source-free image generation module. Its purpose is to leverage the implicit domain information within the model to compel the generator to produce \"source-like samples\" in the target domain data, mimicking the style of the source domain. This generation of samples aims to bridge the knowledge gap between models.  
As shown in Figure 2, the first row consists of target sample images, while the second row shows the \"source-like samples\" generated through the source-free image generation module. The most significant characteristic of these samples is that their content matches that of the target domain data, while their style can match that of the source domain model. They serve as a bridge between the source domain and the target domain. In the subsequent model optimization process, training these samples in conjunction with target domain samples effectively enhances the generalization performance of the vehicle re-identification model.  
The source-free image generation module is designed to train an image generator \(G(\cdot)\) using the implicit domain information within the model, thereby generating \"source-like samples\" with the style of the source domain. These samples replace source domain data to match the source model. To describe the knowledge adapted in the \"source-like samples,\" in addition to the traditional knowledge distillation loss \(\mathcal{L}_{kd}\), a channel-level relationship consistency loss \(\mathcal{L}_{rc}\) is introduced. This loss focuses on the relative channel relationships between feature maps in the target domain and the \"source-like samples\". Therefore, the total loss \(\mathcal{L}_{SFIG}\) is calculated as shown in Equation (7):  
\[\mathcal{L}_{SFIG}=\mathcal{L}_{kl}+\mathcal{L}_{rc} \tag{7}\]  
In the following sections, the details of the two losses will be introduced in detail.
## 4 Experiment and Validation
### Comparison of Methods  
The proposed method is compared to the other three methods (VGGNet, Conv5, and ProtoNet) in order to fully illustrate its advantages. Maxpool and ReLU functions are employed between the layers of the five convolutional layers, three fully connected layers, and one softmax output layer that make up VGGNet. This paper comprises VGG16, which has a total of 16 sublayers. Conv5 is the proposed architecture in this paper, and it consists of five convolutional layers, five pooling layers, and one fully connected layer in the superposition. The fully connected layer in Conv5 is removed as the network's feature extraction component in the few sample methodology proposed in this paper. To achieve reasonably trustworthy comparison findings under the same conditions, it should be noted that the sample sizes of VGGNet and Conv5 are equivalent to those in Few-shot learning. Moreover, to compare the novel few-shot method proposed in this paper with the traditional few-shot method, the ProtoNet method is used in experiments on fault diagnosis. Conv5 is also used in the method's feature extraction step to guarantee experiment fairness. Each group of experiments is run 50 times to prevent the randomness that the experiments can cause, and the average accuracy was then computed as the output of each model.
###### Abstract
Moreover, DTs can potentially improve production planning and predictive maintenance [14], monitor virtual production lines by connecting all involved stakeholders [15] and optimize packaging, materials and logistics [16]. However, the DT as an independent digital model is not sufficient to realize the dynamics and the needs of supply chains as described above. The need for synchronization, knowledge sharing, responsiveness and optimization at the supply chain requires interconnected DTs with cognition capabilities (cognitive Digital Twins--CDTs) able to share information, reason on top of this, understand complexity and perform actions, which impact other actors in the network. The scope of this paper is the conceptualisation and development process of such a CDT model, focusing, beyond that, on its application to empower connected agile supply networks. Section 2 outlines the approach, justification of CDTs in agile supply chains followed by the description of main CDT enablers and the operational framework. The cognition process together with the deployment methodology and reference application scenarios are explained and discussed in more detail in Section 3, while Section 4 presents an example of configuration and operation in a circular supply chain. Finally, Section 5 summarises the study's conclusions and future work.
## 3 Analysis of Internal Vibration Signal Transmission Mechanism of Converter Transformer  
### The Process of Vibration Signal Transmission  
As shown in Figure 3, the main object of the inrush current generated at the time of closing is the winding of the converter transformer. Under the action of the inrush current, the winding bears a severe electrodynamic force, which in turn causes vibration. The signal generated by the winding vibration is transmitted to the surface of the converter transformer by means of the medium of insulating oil [19,20]. At the same time, the core vibrates due to the influence of the winding vibration, its own electromagnetic force and the magnetostrictive phenomenon. The vibration is transmitted to the surface of the box through connecting parts and insulating oil. However, compared with the influence of the winding vibration on the surface of the box, the effect of core vibration is relatively weak [21].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Harmonic** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** & **Mode 5** & **Mode 6** \\ \hline fundamental wave & 3.280 & 1.776 & 2.986 & 1.455 & 2.106 & 0.622 \\ DC component & 2.285 & 1.065 & 2.011 & 0.849 & 1.300 & 0.337 \\
2 nd harmonic & 0.926 & 0.963 & 0.990 & 0.876 & 1.018 & 0.482 \\
3 rd harmonics & 0.232 & 0.212 & 0.158 & 0.285 & 0.117 & 0.301 \\
4 th harmonic & 0.101 & 0.131 & 0.155 & 0.060 & 0.180 & 0.131 \\
5 th harmonic & 0.103 & 0.105 & 0.081 & 0.116 & 0.064 & 0.011 \\
6 th harmonic & 0.025 & 0.021 & 0.042 & 0.029 & 0.063 & 0.043 \\
7 th harmonic & 0.013 & 0.056 & 0.046 & 0.042 & 0.040 & 0.045 \\
8 th harmonic & 0.015 & 0.008 & 0.015 & 0.036 & 0.026 & 0.020 \\
9 th harmonic & 0.017 & 0.029 & 0.026 & 0.006 & 0.027 & 0.006 \\
10 th harmonic & 0.014 & 0.015 & 0.010 & 0.024 & 0.010 & 0.018 \\ \hline \end{tabular}
\end{table}
Table 2: Current components under different working conditions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Harmonic** & **Mode 1** & **Mode 2** & **Mode 3** & **Mode 4** & **Mode 5** & **Mode 6** \\ \hline DC component & 69.7 & 60.0 & 67.3 & 58.4 & 61.7 & 54.2 \\
2 nd harmonic & 28.2 & 54.2 & 33.2 & 60.2 & 48.3 & 77.5 \\
3 rd harmonics & 7.1 & 11.9 & 5.3 & 19.6 & 5.6 & 48.4 \\
4 th harmonic & 3.1 & 7.4 & 5.2 & 4.1 & 8.5 & 21.1 \\
5 th harmonic & 3.1 & 5.9 & 2.7 & 8.0 & 3.0 & 1.8 \\
6 th harmonic & 0.8 & 1.2 & 1.4 & 2.0 & 3.0 & 6.8 \\
7 th harmonic & 0.4 & 3.2 & 1.5 & 2.9 & 1.9 & 7.2 \\
8 th harmonic & 0.5 & 0.5 & 0.5 & 2.5 & 1.2 & 3.2 \\
9 th harmonic & 0.5 & 1.6 & 0.8 & 0.4 & 1.3 & 1.0 \\
10 th harmonic & 0.4 & 0.8 & 0.3 & 1.6 & 0.5 & 2.9 \\ \hline \end{tabular}
\end{table}
Table 3: Current content under different working conditions (%).
## 2 Methodology
### Bayesian Optimization  
Bayesian optimization originates from Bayesian theorem, which is a method of using Bayesian formulas to establish the probability distribution of the optimization process.  
\[p(f|D_{1:t})=\frac{p(D_{1:t}|f)p(f)}{p(D_{1:t})} \tag{1}\]\[D_{1:t}=\{(x_{0},y_{0}),\cdots,(x_{t},y_{t})\} \tag{2}\]  
Among them, \(f(\cdot)\) represents the objective function for evaluating performance. \(x_{t}\) is the decision vector. \(y_{t}=f(x_{t})+\varepsilon_{t}\) is the evaluation value of the objective function, \(\varepsilon_{t}\) is the observation noise. \(D_{1:t}\) is the set of observed values. \(p(D_{1:t}|f)\) is the likelihood distribution of the observed value \(y\). \(p(f)\) is the prior probability distribution of \(f\), which assumes the state of the unknown objective function. \(p(D_{1:t})\) represents the marginal likelihood distribution. \(p(f|D_{1:t})\) is the posterior probability distribution of \(f\), which describes the confidence level of the unknown objective function after modifying the prior probability distribution through the observed dataset.  
Bayesian optimization fully utilizes the information of the previous sampling point and finds the hyperparameter combination that globally optimizes the results by learning the shape of the objective function. The Bayesian optimization framework mainly consists of two core parts: the probability proxy model and the collection function. This article uses Gaussian processes (GP) as the proxy model and Expected Improvement (EI) as the collection function. The algorithm process is shown in Figure 1. Firstly, a probability proxy model is used to fit the objective function. then, the collection function is used to determine the next optimal sampling point based on the posterior probability of past observation points from areas that have not been sampled or may have optimal solutions. Finally, the corresponding parameter set is updated until the stop condition is triggered. The parameter optimization process is represented as follows:  
\[\theta_{t+1}=argmax\;g(\theta_{t})\theta\in\Psi \tag{3}\]  
In the equation, \(g(\cdot)\) is the collection function; \(\theta\) is the optimization parameter combination at time \(t\); and \(\Psi\) is a set of optimized parameter spaces.
## 3 Experimental Results and Analysis
### Fracture Toughness of Coal
#### 3.1.3 Changes in Fracture Toughness Relative to Bedding Plane Structure  
According to Figure 6a, the fracture toughness of the Crack-divider type, without immersion, measures 10.26 MPa\(\cdot\)mm\({}^{1/2}\), representing the highest value among the three types, indicating that this coal exhibits the strongest resistance to fracture. In contrast, the Crack-splitter type displays the lowest fracture toughness at 5.80 MPa\(\cdot\)mm\({}^{1/2}\), signifying the weakest ability to resist fracture. The fracture toughness of the Crack-arrest type is 7.67 MPa\(\cdot\)mm\({}^{1/2}\), placing it between the first two types. As the immersion time increased to 120 days, the relative order of fracture toughness among the three specimen types remained unchanged: Crack-divider type > Crack-arrest type > Crack-splitter type, as shown in Figure 6b-d. For the non-immersed, 30-day, 60-day, and 120-day immersed conditions, the Crack-divider type exhibited increases of 76.76%, 159.59%, 151.76%, and 134.05%, respectively, compared to the Crack-splitter type. This demonstrates that different loading types significantly impact the fracture toughness of coal, with notable differences in toughness values among the three types. The variability in fracture toughness is primarily attributed to the strength of the coal matrix, the strength of the bedding planes, the degree of bonding between the matrix and the bedding planes, and the extent of weakening of both the coal matrix and bedding planes due to mine water.
# Clustering of a Health Dataset Using Diagnosis Co-Occurrences
## 2 Materials and Methods
### Statistical Analysis: A New Hierarchical Agglomerative Clustering Algorithm  
The proposed clustering algorithm is based on the analysis of multimorbidity patterns to unveil diagnosis relationships. From the 151 recorded blocks of ICD-10 diagnoses, it produces hard diagnosis clusters, which are then used to classify the ED visits given their recorded diagnoses. The diagnosis gives a precise and concise expert opinion on the health problem justifying the visit and, therefore, it is the most clinically informative and pertinent variable. It was the only active variable as the use of other active variables like age, gender or prescription data would have introduced unnecessary complexity in the algorithm and in the interpretation of results. This is due to the specialized approaches implemented by multimorbidity clustering which focus on the diagnosis variable. Selection or stratification strategies can be used if specific populations are of interest. Here, we avoided this strategy as the focus is on a general population assessment. Stratification would have made the results harder to evaluate, because of the redundant patterns between strata that it would have produced. Contrary to clustering based directly on patients or visits, where distance measure is often required, this approach proposes a simple solution for the classification of ED visits through the recorded diagnoses. Multimorbidity is defined here by the occurrence of two ICD-10 diagnostic codes, from the 151 blocks of categories, in one episode of ED visits. An episode of ED visits is defined for each patient as a set of consecutive ED visits separated by a maximum of 6 months, to limit non-pertinent and \"noisy\" associations. Using these co-occurrences to gather the initial blocks through the new clustering algorithm, clusters of diagnoses are identified, characterized, and used for the classification of ED visits. In the following sections, we first introduce the similarity measure based on multimorbidity. We then present the new quality indicators and the original clustering algorithm.
# Incorporating Epistemic Uncertainties in Ship Operability Study
## 3 Results
### Probabilistic Operability Diagrams  
Cumulative probability distributions for each criterion are obtained by the procedure presented in Section 2.2 in a way that, for the nominal ship speed, for each sea state, given by a significant wave height _Hs_ and wave spectrum presented in Section 2.1.3, and for each heading angle \(\beta\), probabilities of not satisfying limit state functions given in Section 2.3 are calculated. Probability distribution functions for pitch, vertical acceleration at FP, slamming, propeller emergence for a heading angle of 180\({}^{\circ}\), and a roll angle for beam seas (\(\beta=90^{\circ}\)) are presented in Figure 4. Horizontal lines denote exceedance probabilities of 5%, 50%, and 95%, where it may be noticed that they correspond to different _Hs_, depending on the seakeeping criterion.  
The results are then presented in the form of probabilistic operability diagrams. Probability plots for each of the five criteria evaluated are presented in Figure 5 for three exceedance probabilities: 5%, 50%, and 95%. Radial coordinates indicate the sea state according to the Douglas scale, while the angular coordinate is a heading angle between  
Figure 4: Cumulative distributions (Horizontal lines denote exceedance probabilities of 5%, 50%, and 95%).  
Figure 3: Histogram of the sea states along the assumed shipping route in the Adriatic Sea.  
the ship and waves, where 180\({}^{\circ}\) denotes head seas. For the clarity of presentation, sea states are presented in the Douglas scale, given in Table 4.  
Obviously, the severity of the sea state for which each criterion is exceeded is increasing with an increased probability level. Thus, for sea states in the first diagram, there is only a 5% chance of exceeding each criterion. If the ship owner or ship master is averse to taking the risk, he will consider that diagram. On the other hand, if he is willing to take the risk, then a third diagram can be used, as the operability values would almost certainly be exceeded on the sea states indicated on the diagram.  
Figure 5: Polar plots for probabilities of exceeding limit states of: (**a**) 5%; (**b**) 50%; (**c**) 95%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Degree** & **Wave Height (m)** & **Description** \\ \hline
0 & 0 & Calm (glassy) \\
1 & 0–0.1 & Calm (rippled) \\
2 & 0.1–0.5 & Smooth (wavelets) \\
3 & 0.5–1.25 & Slight \\
4 & 1.25–2.5 & Moderate \\
5 & 2.5–4 & Rough \\
6 & 4–6 & Very rough \\
7 & 6–9 & High \\
8 & 9–14 & Very high \\
9 & \textgreater{}14 & Phenomenal \\ \hline \hline \end{tabular}
\end{table}
Table 4: Douglas scale of sea states.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 2 Related Work  
### Multi-Objective Imaging Satellite Task Planning Model  
For large-scale multi-objective imaging satellite task planning for large-scale regional mapping, we adopt the model proposed in the literature [43]. The objective functions of the model express the core requirement of regional imaging, which is to use as few satellite resources as possible to achieve maximum regional coverage. The decision variables represent the relevant parameters of the satellite imaging strips during each transit. The main constraint conditions are satellite maneuvering constraints. The specific description is below.  
**Decision variables:**  
\[\mathbf{x}=(x_{1},x_{2},\cdots x_{d}\cdots,x_{D}) \tag{1}\]  
\[\mathbf{y}_{d}=\left\{\begin{array}{l}1\,,\text{if the strip $x_{d}$ participates in imaging}\\ 0\,,\text{otherwise}\end{array}\right. \tag{2}\]  
where \(y_{d}\) indicates whether the strip with the swing angle \(x_{d}\) participates in regional imaging. If so, \(y_{d}=1\); otherwise, \(y_{d}=0\). Here, \(\mathbf{x}\) represents the set of swing angles for each satellite imaging, and its length is \(D\). Furthermore, \(x_{d}\) represents the imaging swing angle when the satellite passes the target area for the \(d\)-th time.  
**Objective functions:**  
\[\text{min}f(\mathbf{x})=1-\frac{S_{\text{cov}}(\mathbf{x})}{S_{obj}} \tag{3}\]  
\[\text{min}g(y_{d})=\sum_{i=1}^{D}y_{d}\Bigg{/}D \tag{4}\]  
where Equation (3) ensures the maximum coverage of the imaging area, \(S_{\text{cov}}(\mathbf{x})\) is the effective coverage area of all the satellite imaging strips, and \(S_{obj}\) is the target area. Equation (4) ensures the least imaging times, i.e., the least consumption of satellite resources.  
**Constraint conditions:**  
The experimental satellite is the SAR satellite in this paper. Because SAR satellites are imaged all day and night, we only consider the constraint of satellite maneuverability in this paper, which means that the satellite meets the maximum swing angle constraint during each imaging as follows:  
\[x_{\text{min}}\leq|x_{d}|\leq x_{\text{max}} \tag{5}\]  
where \(x_{\text{min}}\) and \(x_{\text{max}}\) are the minimum and maximum swing angles of the SAR satellites, respectively. When \(x_{d}>0\), it is the left sway in the flight direction, and when \(x_{d}<0\), it is the right sway in the flight direction.
## 5 Simulation Results
### Discussions  
In this article, both DoS and FDI attacks are studied in the new LFC considering AC/HVDC links and inertia emulation module by added ESS. From the results above, we see that the LFC system with AC/HVDC interconnections and emulated inertia could be more vulnerable to the two types of attacks in many adversarial scenario. Here we provide a brief discussion on how these attacks can be detected.  
The DoS attacks are trivially detectable as the absence of data can be treated as an anomaly [42]. However, they can also be misdiagnosed as a poor communication network condition. To detect such attacks, one may utilize the statistical properties of the missing data: we can assume that,  
Figure 10: State trajectory under zero-dynamics attacks on wide-area measurements with \(\mathbf{f}_{0}(i)\) in (35) being the injection on the DC power flow with a value of 0.5 p.u. (**a**) AC/DC system; (**b**) AC/DC system with virtual inertia.  
under normal conditions, each wide-area measurement may be missing with a given small probability. Then, the Bernoulli distributed random variables can be introduced to indicate whether the measurements data are available or not, and one can differentiate between cases of low probability of missing data under normal conditions, versus cases where missing data occurs with higher probability due to DoS attacks.  
For the FDI attacks, it is relatively easier to reveal the occurrence of the stationary ones. For instance, in our work [32], we have proposed a detector with adjustable design variables to have a fast response in the inertia context when the stationary FDI attacks occur. One can also detect the multivariate stationary attack which is equipped with vast attack resources and full knowledge of the targeted system, by designing a bank of detectors where each of them is responsible to detect a particular FDI intrusion. When it comes to the extremely powerful dynamic attack, the detection task becomes much more difficult. The zero-dynamics attack could keep stealthy to an arbitrary detector if certain conditions are satisfied. However, it is noteworthy that this is a rather conservative viewpoint, and for attacks not satisfying all the conditions in Section 4.2, one can still have a successful detection. To this end, we note that many of attacks discussed in this article could trigger alerts on communication network specific measures (e.g., Intrusion Detection System). This give us opportunities to design cross-domain detection schemes to improving the overall cyber attacks detection.