esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The research presented here identified sources of variability in established methods for processing raw DSC data to determine DPM values, including the calculation of MCM and core crystallinities, before proposing an alternative method with improved processing standardisation. Using this method, the DPM of sacrificial parts was found to correlate strongly with the mechanical properties of adjacent tensile samples (R-Squared values of 0.9976, 0.9991, and 0.9068 for ultimate tensile strength, Young's modulus, and elongation at break, respectively) when sinter speed was used to alter the energy density.  
These results confirm that the approach previously established for laser sintering can also be expanded to the high-speed sintering process, with practical implications for quality control/assessment. Users of high-speed sintering systems may be able to confirm the quality and repeatability of the parts they produce through the inclusion of small, sacrificial samples positioned close to the main parts, to be subjected to DSC analysis.  
Further work in this area should include the expansion of the range of energy densities tested by varying other key HSS parameters such as the part bed temperature and the lamp power, including combinations of these parameters, to confirm that the DPM is linked to the overall energy density imparted during the HSS process and not to these individual parameters. An expanded build containing multiple smaller DPM samples and tensile samples throughout should also be tested to determine the repeatability of the proposed DPM processing method, in addition to identifying the accuracy and reliability of mechanical property prediction. The effect of the proximity of any sacrificial DSC samples to parts whose properties require prediction should also be considered, as the increased separation of adjacent parts will improve powder removal. Finally, expansion to other materials should be considered to improve the overall usefulness of the proposed method in a commercial setting.  
Conceptualization, C.M.; Methodology, R.B., A.H. and C.M.; Formal analysis, R.B. and A.H.; Investigation, R.B. and A.H.; Writing--original draft, R.B. and A.H.; Writing--review & editing, C.M.; Supervision, R.B. and C.M.; Project administration, C.M.; Funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by EPSRC grant number EP/P006566/1.  
The data presented in this study are available on request from the corresponding author (privacy).  
The authors declare no conflict of interest.
## 4 Prediction Based on GA-PSO-BP Neural Network
### Experimental Validation of Simulation Results  
Hydroforming equipment and forming dies are shown in Figure 14. The equipment is 315 t special hydraulic moulding equipment, driven by servo motors. The forming die mainly consists of left and right push heads, upper and lower dies, and the experimental process uses tensile oil as a lubricant between the tube and the die [10].  
Experimental testing of the simulation results used the previously mentioned machine learning forming parameters for aluminium alloy variable diameter tubes based on the GA-PSO-BP algorithm. The results were compared with the wall thickness and expansion height of the tube by selecting seven and five points at different locations on the outer contour of the axial profile of the tube. The results are shown in Figure 15, and the experimental and simulation results for the maximum thinning and thickening rates after tube forming are 15.3%, 3.3%, 14%, and 1.7%. The error between the experimental results and the simulation results is within \(\pm\)5% with high accuracy, and the feasibility of combining finite element simulation on the intelligent control method of the aluminium alloy large section rate of change tube hydroforming was also verified.  
Figure 14: Servo hydroforming equipment and dies.  
Figure 15: Comparison of simulated and experimental results: (**a**) Thickness distribution. (**b**) Distension height distribution.  
Friction plays a crucial role in tube hydroforming [17]. Different coefficients of friction were achieved in the experiments through active lubrication or artificial roughness treatment. When the tube blank was left untreated, a coefficient of friction of approximately 0.12 was obtained. With active lubrication applied, the coefficient was reduced to about 0.05 [18], whereas artificial roughness treatment resulted in a coefficient of 0.2. Additionally, the coefficient of friction could be adjusted by varying both the type of lubrication employed and the degree of roughness introduced through artificial treatment.
# WAID: A Large-Scale Dataset for Wildlife Detection with Drones
## 4 Experiments and Results
### Experiments of State-of-the-Art Algorithms on WAID
#### 4.2.2 Evaluation Metrics  
Currently, the most widely used and effective evaluation metrics for object detection are _Precision_, _Recall_, and Mean Average Precision (\(mAP\)). We utilize these evaluation metrics to assess the performance of advanced object detection algorithms on the WAID dataset, aiming to compare the capabilities of different algorithms.  
The _Precision_ metric measures the proportion of true positive predictions among all positive predictions made by the algorithm. In other words, it is the ratio of the number of true positive samples to the total number of samples predicted as positive. The _Recall_ metric, on the other hand, measures the proportion of positive samples that are correctly detected by the algorithm. It is the ratio of the number of true positive samples to the total number of actual positive samples. _Recall_ is an important indicator for assessing the accuracy of a detection algorithm. The formulas are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN}. \tag{6}\]  
Here, \(TP\) (True Positive) represents true positive samples, \(FP\) (False Positive) represents negative samples incorrectly identified as positive, and \(FN\) (False Negative) represents positive samples incorrectly identified as negative.  
The \(mAP\) is a comprehensive metric used to evaluate the overall performance of object detection algorithms. It is based on the _Precision_-_Recall_ curve and calculates the Average Precision (\(AP\)) for each class, followed by taking the average \(AP\) across all classes to obtain \(mAP\). The formula is as follows:  
\[mAP=\frac{\sum_{1}^{N}\int_{0}^{1}P(R)dR}{N}. \tag{7}\]  
Here, \(P(R)\) represents precision at recall \(R\), \(N\) is the number of object classes.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 2 Background
### Damage Detection and Quantification  
The laser scanning technique is introduced to visualize and quantify the damage. Any desired point on the structure surface can be irradiated by a laser pulse using a galvanometer, and the pitch between the two adjacent points is set to a constant. When a laser pulse is shot at a point, ultrasonic waves are generated because of the thermal expansion of the material. The ultrasonic waves propagating in the structure are then recorded by the transducer placed at a fixed point. The recorded ultrasonic responses from undamaged and damaged structures are used to calculate NNPE values for each excitation point to capture the differences between the baseline and current-line attractors. The most significant differences should occur at the damage location. Therefore, the damage can be detected by the calculated NNPE values. Notably, only a small area covering the damaged location in the damaged structure was scanned in this study to readily detect the damage because the damage location is not known in advance. Furthermore, the pitch between two adjacent points in the scanned area is determined before laser scanning, by counting the number of points that correspond to larger NNPE values in the visualization picture. The size of the damage can be quantified too.  
A schematic diagram of the proposed technique is provided in Figure 4.  
Figure 4: The schematic diagram of the damage detection technique.  
Figure 3: The schematic of feature extraction.
## 4 Analysis of Pipeline Overvoltage under the Action of Multi-Tower Flow  
When the power overhead conductor is struck by lightning, the lightning current can cause the line insulator to flashover. In this situation, the lightning current disperses along the surface of the flashover insulator through the tower grounding grid. Due to the presence of resistive coupling, the potential of the soil area near the grounding grid and the steel layer of the adjacent pipeline will increase. As a result, there will be a potential difference between the soil area near the pipeline and the steel layer of the pipeline, leading to a potential difference in the insulating layer. In this paper, based on the ATP-EMTP simulation results, the mine current of T\({}_{0}\) and T\({}_{\text{RI}}\) tower grounding grid is introduced into the COMSOL model.  
When the lightning impulse current disperses through the tower grounding grid, it will cause a nearby rise in ground potential. The equation for calculating the potential \(V_{\text{t}}\) is shown in Equation (1):  
\[V_{\text{t}}=k\times i_{\text{t}}\times R \tag{1}\]  
where \(k\) is the impact coefficient, \(i_{\text{t}}\) is the current into the mine through the tower, and \(R\) is the tower grounding resistance.  
Without considering the tower foundation, the grounding resistance \(R\) of the power tower can be calculated according to term Equation (2) when the grounding device is a box extension shape.  
\[R=\frac{\rho}{2\pi L}\left(\ln\frac{L^{2}}{hd}+A_{\text{t}}\right) \tag{2}\]  
In term Equation (2), \(L=4(l_{\text{ga}}+l_{\text{g1}})\), where \(l_{\text{ga}}\) and \(l_{\text{g1}}\) are the length of the frame and the epitaxial ray, respectively; h is the buried depth of grounding device; \(d\) is the diameter of the grounding body; and \(A_{\text{t}}=1.76\).  
The ground potential Vt near the grounding grid will increase due to the dispersion of lightning current. However, as the distance from the grounding grid increases, the ground potential will gradually decrease. The equation for calculating the ground potential rise caused by resistive coupling at a point outside the insulating layer of a gas pipeline is represented by Equation (3).  
\[V_{x}=V_{\text{t}}\frac{r_{\text{e}}}{r_{\text{e}}+x} \tag{3}\]  
In Equation (3), \(x\) is the distance from the point to the tower grounding device and \(r_{\text{e}}\) is the radius of the equivalent hemispherical grounding body of the tower. Its \(r_{\text{e}}\) calculation term equation is shown in term Equation (4).  
\[r_{\text{e}}=\frac{L}{\left(\ln\frac{L^{2}}{hd}+A_{\text{t}}\right)} \tag{4}\]  
The theoretical term equation suggests that the lightning overvoltage of gas pipelines is influenced by factors such as the spacing between pipelines, the amplitude of lightning current, and soil parameters. Therefore, a simulation analysis will be conducted to examine these influencing factors.
# PERM1--An Emerging Transcriptional Regulator of Mitochondrial Biogenesis: A Systematic Review
## 4 Discussion
### PERM1 and Skeletal Muscle
#### 4.1.3 Mitochondrial Biogenesis  
PERM1 is an indispensable modulator of maximal mitochondrial oxidative capacity and a strong enhancer of PGC-1\(\alpha\)-mediated mitochondrial biogenesis [11; 12; 13]. PERM1 knockdown in mouse skeletal muscle has been found to (i) decrease SDH activity, sub-sarcolemmal mitochondria formation, mitochondrial content, (ii) alter the regulation of proteins involved in membrane transport and ROS breakdown, and (iii) reduce skeletal muscle strength and endurance [26]. Interestingly, PERM1-overexpressing muscles from mice have been found to exhibit a higher density of mitochondria, enhanced spare respiratory capacity, increased vascularization, and improved fatigue resistance [11]. Furthermore, after dual innervation surgery (hypoglossal nerve sutured to the masseter nerve), rat skeletal muscle has been shown to upregulate levels of _PERM1_, _CKMT2_, and _CaMKII_, along with a potential shift toward a more oxidative muscle fiber profile [28].
## 4 Analysis of the Modulational Instability
### The Anomalous-Dispersion Regime
#### 4.1.3 The Impact of Asymmetry Parameters
These results are presented in Figure 4b, which makes it obvious that the gain and bandwidth of the MI quickly decrease with the increase of \(\eta\) from small values \(\sim\)0.1 to \(\eta=2\). With the further increase of the asymmetry ratio to values \(\eta>2\), the largest MI gain slightly increases, while the bandwidth remains practically constant.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 3 Interest Flooding Attack: Concept and Taxonomy
### Taxonomy of IFA
#### 3.2.2 Attack Intensity  
Attack intensity refers to the rate at which malicious interest packets are sent. In constant-rate attacks, adversaries maintain a steady stream of interest packets, exerting a predictable but sustained strain on network resources. This intensity attack model has been extensively analyzed in previous studies, such as a study that evaluated the limitations of existing countermeasures against steady-rate attacks [30].  
Conversely, variable-rate attacks involve fluctuating the rate of interest packet transmission, potentially to evade detection mechanisms that rely on identifying consistent traffic patterns. Another study discusses how such attack patterns can be effectively analyzed using an SIS epidemic model, considering the impact of Negative Acknowledgement (NACK) mechanisms on IFAs [31]. These variable-rate attacks can be more insidious, as they mimic legitimate traffic bursts, complicating the differentiation between normal and malicious activities. A recent paper further explores statistical abnormality detection mechanisms tailored to variable attack intensities, demonstrating how fluctuations in request rates can bypass traditional IFA detection strategies [32].
## 6 Performance Analysis
### Communication Cost Analysis  
We only analyze our scheme in detail since the analyzing process is the same. In our scheme, the online learning devices will send the anonymous identity and signature \(\{M_{i},T_{t},PK_{i},R_{i},PID_{i}\}\), in which \(PID_{i}=\{PID_{i,1},PID_{i,2}\}\in G,\sigma\in Z_{q}\) and \(T_{t}\) is the timestamp. Therefore, in our scheme, the communication cost is \(40\times 4+20+4=184\) bytes, while the scheme proposed by [PERSON] et al. [47] requires 296 bytes, the scheme by [PERSON] et al. [48] requires 388 bytes and the scheme by [PERSON] et al. [6] requires 144 bytes.  
Table 3 provides a summary of the comparison among the different schemes in terms of computation costs and communication costs.
## 5 Numerical Examples  
### Thermodynamic Analysis of Plate  
In order to validate the accuracy of the proposed PD model in simulating thermodynamic response, a plate under thermal shock load is considered. The geometry and loading conditions are shown in Figure 3. The material properties are given in Table 1. The horizon \(\delta\) is set to be 3.0 mm, and \(l\) is set to be 1.0 mm. The initial temperature of the structure is set to be 20 \({}^{\circ}\)C. A heat flux \(q\) is applied on the structure bottom. The convective heat transfer boundary is applied on the top and right surfaces with coefficient \(h\) and reference temperature \(T_{\rm ref}\). The time increment is chosen as \(\Delta t=0.2\) us.  
Figure 4 shows results simulated by the proposed model and the FEM (obtained by ANSYS 19.0 \(\times\)64). There is good agreement between the history curves of point A in the temperature field [see Figure 4a], which verifies the accuracy of the proposed model for transient thermal problems. Moreover, the displacement histories [see Figure 4b] and distributions along the \(y\) direction [see Figure 4c,d] further indicate that the proposed model can handle the coupled effect due to the temperature changing. It can be seen that the absolute value of the relative error of temperature and displacement at point A during thermal diffusion is less than 0.04% and 3%, respectively. The relative error of temperature along the path \(x=100\) mm is larger at the geometric boundary and smaller in the center region. In addition, the relative error of displacement is greater than 20% near \(y=0\) mm and decreases with y-coordinate increase. This can be attributed to the deviation of the material point coordinates from the actual boundaries of the structure, which is usually 0.5\(l\). These discrepancies have been shown to be further reduced by decreasing the horizon radius or refining the discretization [57]. Figure 5 shows the contour plots of the
# Condition Monitoring of Industrial Equipment Based on Multi-Variables State Estimate Technique
## 1 Introduction
Furthermore, the adequate actual failure events data are difficult to obtain in industrial fields, the labeled data are the key component for improving the vibration monitoring accuracy in model training. A concise and effective method is establishing an exact nonlinear normal state envelope space using normal vibration data, normal operating condition is completely included in the normal state envelope space, and the distance between current condition and boundary of success envelop space is used to quantify the condition monitoring. [PERSON] [17] proposed vibration condition monitoring method applying symptom observation matrix; discrete observation symptom vectors were obtained by analyzing historical data, and the vectors create symptom observation matrix, using next the singular value decomposition for the given symptom observation matrix, fault symptoms could be extracted. MSET(multi-variables state estimate technique) was first proposed by [PERSON] [18] and applied in sensor and gearbox monitoring [19; 20]. MSET is proved as an alternative method for constructing normal state envelope space. In general, our paper focused on proposing an improved and universal condition monitoring method and judging criteria, although the vibration condition monitoring for a specific equipment is applied and discussed in our paper, obviously, the condition monitoring for the other measurement parameters for different industrial equipment, such as temperature, current, etc., could be realized by reusing the proposed method. In this paper, the normal state envelope space construction method for industrial equipment condition monitoring is proposed based on MSET, and the variables selecting for normal state envelope space are discussed. For a further feasibility discussion, a specific application in blower fan vibration condition monitoring is proposed. The weighted-similarity between the observation vibration vector and estimated vibration vector is investigated. While the potential failure occurred, there will be distinguished difference in similarity distribution, which allows incipient failure to be detected at early stage. The rest of paper is organized as follows: In Section 2, introduction of a typical industrial blower fan, which is applied for discussing in our research, is provided, and the parameters for blower fan are exhibited. In Section 3, the structure for vibration condition monitoring model using MSET is discussed. In Section 4, the vibration condition monitoring simulation is presented, including variables selection, model construction, similarity calculation, and alarm threshold determination. In Section 5,the actual industrial data are applied for validation, and actual failure data for blower fan have been used for revealing the effectiveness of proposed method. Finally, Section 6 concludes the work.
## 3 VN Allocation and Enforcement Methodology
### Model Formulation  
In this work, the physical network of datacenter is modeled in the form of a graph \(PG=(S,M,E)\), in which \(S\) represents the set of all switches, \(M\) is the set of physical servers, and \(E\) stands for the set of all links in the network. A path from node \(u\) to node \(v\) is an ordered sequence \(e_{1}\), \(e_{2}\),..., \(e_{l}\) of distinct links where e1 and el have their respective end nodes at \(u\) and \(v\) and the adjacent links \(\left\{e_{j-1},e_{j}\right\}\) have a common distinct end node \(w\) where \(w\
eq u\
eq v\). We denote a path between the nodes u, v as \(p_{u,v}\) and the set of all paths between them as \(P_{u,v}\). Each link \(e(u,v)\in E\) has a capacity \(C\left(e\right)\), representing the maximum bandwidth it can support.  
Next, the VN will be modeled. Let \(VN=\left(VS,\overline{VM},VE\right)\) be a set of elements, which contains the logical switches, logical servers, and logical links. The VN should be mapped to PG for sharing the resources in the physical network of datacenter. Therefore, \(VS\), \(\overline{VM}\) and \(VE\) is mapped to \(S\), \(M\) and \(E\), respectively. The notations used in this paper are listed in Table 1. A switch \(S_{i}\) has \(k_{i}\left(k_{i}\geq 1\right)\) ports \(\left\{port_{j}^{S_{i}}\mid j\in\left[0,k_{i}-1\right]\right\}\), \(k_{i}\) is constant. In the VDC, the switch in SCG model should be consistent with the real switch. Because the commodity switches are used as the underlying interconnecting elements of network infrastructure in the cloud datacenters, the native resources of switch in the SCG model should not exceed the resources of commodity switch in reality, such as the number of ports and port bandwidth. This means \(interface(VS_{i})<interface(S_{j})\), \(vib(VS_{i,m})<ib(S_{j,n})\), and \(veb(VS_{i,m})<eb(S_{j,n})\). Tenants request bandwidth guarantees between entities by placing bidirectional edges between the corresponding vertices in the SCG model. Each edge \(e=\left(VS_{i,m},VS_{j,n}\right)\) between the \(m\)-\(th\) port of \(VS_{i}\) and the \(n\)-\(th\) port of \(VS_{j}\) can be labeled with a heterogeneous weight pair \(\left\langle u,v\right\rangle\) that represents per-link bandwidth guarantees. Specifically, VM connected to the \(m\)-\(th\) port of \(VS_{i}\) is guaranteed bandwidth \(u=veb(VS_{i,m})\) for sending traffic to VM connected to the \(n\)-\(th\) port of \(VS_{j}\), is another way to say, VM connected to the \(n\)-\(th\) port of \(VS_{j}\) is guaranteed bandwidth \(u=vib(VS_{j,n})\) to receive traffic from VM connected to the \(m-th\) port of \(VS_{i}\). VM connected to the \(n\)-\(th\) port of \(VS_{j}\) is guaranteed bandwidth \(v=veb(VS_{j,n})\) for sending traffic to VM connected to the \(m\)-\(th\) port of \(VS_{i}\), is another way to say, VM connected to the \(m\)-\(th\) port of \(VS_{i}\) is guaranteed bandwidth \(v=vib(VS_{i,m})\) to receive traffic from VM connected to the \(n\)-\(th\) port of \(VS_{j}\). Obviously, we have \(veb(VS_{i,m})=vib(VS_{j,n})\) and \(veb(VS_{j,n})=vib(VS_{i,m})\).