esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A review article from the year 2020 lists only 10 materials processed with MBJ that could be sintered to relative densities above 90% by that time [22]. Often, individual material adaptations are necessary. Several materials with good sintering properties are known from metal injection molding (MIM), but MIM powder is usually finer than the powder used in the MBJ process, thus both are not directly comparable. Next to Co-Cr alloys, Cu alloys, Ni-based alloys, WC-Co, and Ti6 Al4V, the most commonly used steels in the MBJ process are 316L [3; 23; 24], 304L [22; 25], 17-4 PH [13; 26; 27], 420 [22; 28], and H13 [29]. However, when it comes to medical applications, where AM can particularly offer the major advantage of customized parts, there is limited availability of possible materials and, to the best of the authors' knowledge, the only material investigated in the literature in the field of nickel-free stainless steel for binder jetting is the material used in this study [30]. Since other stainless steels, such as 316 L, contain high amounts of nickel, they are of limited use for medical applications, jewelry, and watches. Nickel can trigger allergies and cause undesirable reactions in connection with the human body. However, nickel is also an important element for ductility in stainless steel as it acts as an austenite stabilizer. Several approaches in literature describe how nickel-free stainless steels can achieve the desirable properties by adding nitrogen, which also stabilizes the austenite [31]. In powder metallurgy, there are already developments regarding nickel-free stainless steels for MIM [30; 32; 33]. In summary, by reviewing the existing literature, it can be stated that the range of materials for additive manufacturing is limited so far. In particular, special materials such as Ni-free steels for medical applications are limited. One reason in the MBJ area is that many conventional materials are not designed for additive manufacturing, and in the binder jetting area, are lacking in good sintering properties. As a result, residual porosity remains in the material, which reduces the mechanical properties. One way of improving the properties is hot isostatic post-densification with HIP. The present study investigates the microstructure and mechanical properties of a newly developed high-nitrogen nickel-free stainless steel produced by MBJ. A particular focus is on the question of the extent to which subsequent heat treatment via hot isostatic pressing (HIP) can close the remaining residual porosity in the material and whether this is accompanied by an improvement in the mechanical properties, such as hardness and tensile strength and elongation at fracture.
###### Abstract
* The drone flight path was optimized based on the traveling salesman problem (TSP) and enhanced particle swarm optimization (EPSO). In addition, the optimization results were compared with the most adopted drone flight operations in the real world. The results showed that path planning optimization is an effective solution to overcome drone battery limitations. Furthermore, the utilized method and algorithm can find the global optimum for the path planning problem which can significantly reduce the mission time of drones to collect data on large-scale farms. The rest of the paper is organized as follows. A review of recent works is presented in Section 2. Section 3 describes the materials and methods used in developing the FMS which are divided into three subsections, remote sensing, LoRa-based communication network, and optimal path planning. Measurement and simulation results are presented and discussed in Section 4. Finally, the paper ends with a conclusion in Section 5.
## 3 Time-Frequency Coded Cooperative Transmission Based on MC-CDMA and Power Allocation
#### 3.1.2 Phase 2  
In phase 2, cooperative sensor \(P_{k}\) recodes the data stream of source sensor \(S_{k}\) detected according to Figure 4 to get the time-frequency coded frame \(\mathbf{d}_{k}^{2}\). Then \(\mathbf{d}_{k}^{2}\) will be retransmitted to the sink node after MC-CDMA modulation illustrated as in Figure 2a. The transmitting signal vector is given by:  
\[\begin{split}\mathbf{T}_{R}=&[\underbrace{c_{k}(1)d_{ k}^{2}(1)e^{/2\eta_{i}},\cdots,c_{k}(G)d_{k}^{2}(1)e^{/2\eta_{G}}}_{G},\cdots, \underbrace{c_{k}(1)d_{k}^{2}(M)e^{/2\eta_{(M-1)-\alpha}},\cdots,c_{k}(G)d_{k }^{2}(M)e^{/2\eta_{(M-1)-\alpha}}}_{G}]\\ \in&(1\times Nc)\end{split} \tag{9}\]  
Therefore, the received signal vector from \(K\) cooperative sensors at the sink node is:  
Figure 4: Time-frequency coding of the original data stream. \(t=1\): Phase 1; \(t=2\): Phase 2.  
\[\mathbf{r_{p,D}}=\sum_{k=1}^{K}\sqrt{E_{\mathbf{r_{k}}}}\times\mathbf{T_{\mathbf{r_{k}}}}\times\mathbf{ h_{\mathbf{r_{k}},D}}+\mathbf{N} \tag{10}\]  
where, \(E_{\mathbf{r_{k}}}\) is the average transmit power of cooperative sensor \(P_{k}\); \(\mathbf{h_{\mathbf{r_{k}},D}}\) is the channel gain matrix between cooperative sensor \(P_{k}\) and the sink node \(D\).
###### Abstract  
Service Function Chaining (SFC) is an emerging paradigm aiming to provide flexible service deployment, lifecycle management, and scaling in a micro-service architecture. SFC is defined as a logically connected list of ordered Service Functions (SFs) that require high availability to maintain user experience. The SFC protection mechanism is one way to ensure high availability, and it is achieved by proactively deploying backup SFs and installing backup paths in the network. Recent studies focused on ensuring the availability of backup SFs, but overlooked SFC unavailability due to network failures. This paper extends our previous work to propose a Hybrid Protection mechanism for SFC (HP-SFC) that divides SFC into segments and combines the merits of local and global failure recovery approaches to define an installation policy for backup paths. A novel labeling technique labels SFs instead of SFC, and they are stacked as per the order of SFs in a particular SFC before being inserted into a packet header for traffic steering through segment routing. The emulation results showed that HP-SFC recovered SFC from failure within 20-25 ms depending on the topology and reduced backup paths' flow entries by at least 8.9% and 64.5% at most. Moreover, the results confirmed that the segmentation approach made HP-SFC less susceptible to changes in network topology than other protection schemes.  
failure protection; service function chaining; network function virtualization; software-defined networking; segment routing
# Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network
## 2 Calculation Method of Transport Efficiency  
### Shadow Efficiency  
Capacity (traffic supply) is an important foundation of efficiency research, and the matching relationship between capacity and passenger flow (traffic demand) is the key to efficiency. In order to quantitatively explain the relationship between capacity, passenger flow and efficiency, this paper refers to the concept of \"shadow price\" in economics and operational research for reference, and then introduces the new concept of \"shadow efficiency\" to explain the phenomenon of partial capacity improvement being inconsistent with overall efficiency improvement. In other words, shadow efficiency equals the variation value of the system's global utility, which is caused by the slight change in the individual unit capacity of a certain part of the urban rail transit system.
# Research on Distributed Fault Diagnosis Model of Elevator Based on PCA-LSTM
## 5 Conclusions  
1. The PCA-LSTM-based elevator fault prediction method proposed in this study extracts feature information from various operational data of elevators and minimizes the reliance on manual labor for traditional elevator fault diagnosis.
2. The second core idea focuses on distributed elevator fault diagnosis. Through experiments, the proposed fault prediction method achieves an accuracy of more than 90% in classifying the fault types of multiple elevators under the same working conditions, and the distributed technology enables local fault diagnosis within the elevator, which alleviates pressure on the central processor.
3. Currently, the distributed elevator fault diagnosis technique based on the PCA-LSTM model proposed in this paper delivers more satisfactory results in terms of spatial distribution. However, concerning model integration and parameter exchanges, the communication time between adjacent elevators is prolonged, which, in turn, increases the training time. Consequently, the speed of elevator parameter exchanges in the distributed scenario requires further consideration in future developments.  
The implementation of distributed fault diagnosis systems for elevators presents several challenges inherent to their distributed nature. Firstly, synchronization issues arise, as data from various elevators must be aggregated and processed in real time to ensure timely fault detection. This necessitates robust algorithms capable of handling asynchronous data inputs without compromising the system's responsiveness. Secondly, data privacy is a critical concern, as information collected from multiple elevators may contain sensitive user or operational data. Implementing stringent data protection protocols and secure data transmission mechanisms is essential to safeguard this information. Lastly, the scalability of models across multiple elevators is challenging, as the system must adapt to varying operational conditions and elevator architectures. Developing adaptive models that can generalize well across different setups while maintaining high diagnostic accuracy is crucial for the widespread applicability of these systems.  
Conceptualization, C.C. and X.R.; methodology, C.C.; software, X.R.; validation, C.C., X.R. and G.C.; formal analysis, C.C.; investigation, C.C.; resources, C.C.; data curation, X.R.; writing--original draft preparation, X.R.; writing--review and editing, X.R.; visualization, X.R.; supervision, X.R.; project administration, X.R.; funding acquisition, G.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on Dynamic Opportunistic Maintenance Scheduling Method for Cluster Wafer Fabrication Equipment Group, grant number 71661016, and \"The APC was funded by 71661016\".  
All of the study's data are available in the manuscript.  
The authors thank the anonymous reviewers for their helpful comments and suggestions.  
The authors declare no conflicts of interest.
## 7 Case Study
### Comparison of Uniform and Non-Uniform Deployment
The associated residual energy when the life ends (under the optimal sensor deployment scheme) is illustrated in Figure 10. Due to the energy hole problem, sensors that are close to the base station need to transmit more sensing data, and they consume energy much faster than the ones in the periphery of the target area. For uniformly deployed WSN, the unbalanced energy consumption cannot be avoided, and energy will be surely left. The non-uniform deployment scheme can solve this problem, and both the number of sensors needed and residual energy of the non-uniform deployment are much less than that of the uniform one. In fact, the number of sensors and the residual energy are relatively stable as \(R_{a}\) increases under the non-uniform scheme, whereas they increase sharply for the uniform one. This implies that the non-uniform scheme is much better than the uniform one when \(R_{a}\) becomes large. Figure 3.0 Optimal numbers of sensors deployed under different \(\tau*\). Figure 3.1 Residual energy in WSN under different \(\tau*\).
# A Generic Automated Surface Defect Detection Based on a Bilinear Model
## 4 Conclusions  
The conclusions from the work are presented as follows.  
* A generic method of automated surface defect detection based on a bilinear model is proposed. Firstly, as a feature extraction network of the bilinear model, D-VGG16, which consists of two completely symmetric VGG16, is designed, and the features extracted from the bilinear model are output to the soft-max function to realize the automatic classification of defects. Then the heat map of the original image is obtained through applying Grad-CAM to one of the output features in D-VGG16. Finally, the defects in the input image can be located automatically after processing the heat map with a threshold segmentation algorithm.
* The training of the proposed method is carried out in a small sample, end-to-end, and in a weakly-supervised way. Even though the number of training images used in the experiments were no more than 1300, over-fitting did not occur during the training process of all the datasets, and the surface defects can be automatically located using only training images labeled at image-level.
* The experiments has been performed on four datasets with different defective features. This shows that the proposed method can be effectively applied to surface defect detection scenarios with texture, color and shape features, even a diode glass bulb surface defect dataset with complex texture and the fluorescent magnetic powder surface defect dataset with strong interference factors. The overall performance of the proposed method is superior to other methods.  
The proposed method has certain limitations for automatic localization in the datasets with complex textures. Since the whole network is composed of four VGG16, and the Grad-CAM used in automatic localization is time-consuming, it takes a long time to detect and locate defect in the testing stage. Future work will focus on solving the above effect of automatic location and real-time performance of the method in this paper.  
Figure 12: Comparison of \(F_{1}\) curve obtained from four methods. **(a)** Diode glass bulb surface defect dataset; **(b)** Magnetic powder surface defect dataset.  
**Author Contributions:** [PERSON] designed the algorithm, performed the experiments and wrote the paper. [PERSON] modified the paper. [PERSON] and. [PERSON] supervised the research.  
**Funding:** This work was supported by National Natural Science Foundation of China (Grant Nos. 11602292, 61701421, 61601381).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 State Estimation Based on Nonlinear Gaussian Systems  
### Observer Design  
The Kalman filter is designed on the ROV of the 4-DOF dynamic model, and the dynamic model of the ROV has been given in Section 2.3. The state variables are defined as follows:  
\[\hat{x}=[x\ y\ z\ \varphi\ u\ v\ w\ t_{x}\ b_{y}\ b_{z}\ b_{\varphi}], \tag{13}\]  
where \(\hat{x}\in R^{12\times 1}\). In a linear system, the state space equation can be expressed as  
\[\dot{x}_{k} =Ax_{k-1}+Bu_{k-1}+Ew_{k-1}, \tag{14}\] \[z_{k} =Hx_{k}+v_{k},\]  
Expanding the matrices in Equation (14) yields  
\[A=\begin{bmatrix}R(\psi)V\\ M^{-1}(-D(v)v-C(v)v+R^{\mathrm{T}}(\psi)b)\\ F_{b}\\ 0_{\psi\times 1}\end{bmatrix},B=\begin{bmatrix}0\\ M^{-1}\\ 0\end{bmatrix}, \tag{15}\]  
\[E=\begin{bmatrix}0&0&0\\ 0&M^{-1}&0\\ 0&0&1_{4\times 4}\end{bmatrix},H=\begin{bmatrix}1_{4\times 4}&0_{4 \times 8}\end{bmatrix}, \tag{16}\]  
where \(A\in R^{12\times 1}\), is the state-transition matrix, \(B\in R^{12\times 4}\) is the input matrix, \(E\in R^{12\times 12}\) is the noise matrix, \(H\in R^{4\times 12}\) is the measurement, \(w\) is the process noise, \(w\sim(0,Q_{k})\), and \(v\) is the measurement noise, \(v\sim(0,R_{k})\).
## 3 Datasets and Evaluation Metrics
### Datasets
#### 3.1.1 Real Multi-Frame HDR Datasets

Table 1: _Cont._Another important resource in multi-frames is the IISc VAL [48] dataset, which provides 582 sequences of real LDR images accompanied by their HDR deghosting versions, with a resolution of 1000 \(\times\) 1500 pixels. The wide variety of scenarios, both indoors and outdoors, and the presence of moving objects make this dataset particularly suitable for testing multi-frame fusion and deghosting algorithms. Complementing the above, IISc VAL 7-1 [76] presents 84 sequences of real LDR images, with their corresponding HDR images, also in 1000 \(\times\) 1500 pixel resolution. This set focuses on evaluating HDR reconstruction methods in dynamic scenes and could be of great help in research into deghosting and multi-frame fusion in dynamic environments. The [PERSON] et al. [77] dataset contains 105 scenes captured with multiple exposures, resulting in HDR images with a resolution of 2142 \(\times\) 1422 pixels. The images were taken with a Nikon D700 (Nikon Corporation, Tokyo, Japan) camera and cover a wide range of scenarios, from artificially lit interiors to outdoor landscapes. This is a relevant dataset for studies with high dynamic range images, as well as for testing deghosting algorithms and HDR reconstruction from multiple exposures. In addition, the dataset by [PERSON] et al. [78] contains 16 sequences of images captured in indoor and outdoor scenes, with a resolution of 1024 \(\times\) 682 pixels. Focusing on scenes with movement, this dataset could be an ideal addition for testing deghosting in HDR fusion algorithms. The HDR-Eye dataset [79] consists of 46 real HDR images captured in various natural scenes, including humans, stained glass windows, sculptures, among others, with a high resolution of 1920 \(\times\) 1080 pixels. This dataset provides images from multiple frames, making it suitable for evaluating HDR reconstruction algorithms, especially in scenarios involving complex lighting and detailed textures. The Laval Indoor dataset [80] comprises 2100 high-resolution indoor panoramas captured with a Canon 5D Mark III (Canon Inc., Tokyo, Japan) camera, with a resolution of 2048 \(\times\) 1024 pixels. The images were collected in multiple exposures to create HDR panoramas of indoor environments, making it a valuable resource for testing HDR reconstruction methods in indoor scenarios. Similarly, the Laval Outdoor dataset [80] includes 205 high-resolution outdoor panoramas also captured with a Canon 5D Mark III (Canon Inc., Tokyo, Japan) at a resolution of 2048 \(\times\) 1024 pixels. This dataset offers multi-frame HDR images of outdoor scenes, providing a variety of lighting conditions and environments, which is beneficial for evaluating HDR algorithms in outdoor scenarios.
## 2 Materials and Methods
The Swarm Vector can also be interpreted as a weighted superposition of the Lbest and Gbest positions. Steps 5 through 9 in the lower section of Figure 1 show the original AIS BPSO algorithm. This is kept the same for the DH-BPSO, only replacing the global best position with the Swarm Vector. Following the logic shown on the left of Figure 1, the difference vector \(d\) in Equation (4) is the exclusive disjunction (XOR) between the original position of a particle \(x_{i}\) and the Swarm Vector in step 5. Thus,  
\[x_{i}\oplus(\text{Swarm Vector})=d. \tag{4}\]  
where \(\oplus\) denotes the exclusive disjunction operation. The position of the 1 bits in this vector correspond to differing bits between the particle's current position \(x_{i}\) and the Swarm Vector. In step 6, the 1 bits are randomly removed from the difference vector \(d\) by taking the conjunction of the difference vector \(d\) and a pseudo-random vector \(r\) of 0s and 1s. This yields the Swarm Change Vector. \[d\wedge r=\text{Swarm Change Vector} \tag{5}\]  
Figure 1: The proposed Dynamic Hybrid Binary Particle Swarm Optimization (DH-BPSO) algorithm to update each particle’s position every iteration. If the Swarm Vector in Equation (4) is replaced with the best position of the individual particle, the logic along the bottom right side of Figure 1 will apply, and Equation (5) will then result in the Individual Change Vector. The disjunction of the Swarm Change Vector and Individual Change Vector is taken in step 7. The result is a series of bits where the 1s indicate bits to be flipped from the original position and is known as the Change Vector--the functional equivalent of a particle's velocity. Hence,  
\[(\text{Swarm Change Vector})\vee(\text{Individual Change Vector})=\text{Change Vector}. \tag{6}\]  
Before calculating a particle's new position, velocity limits set by the minimum and maximum velocity parameters are enforced in step 8. If the number of 1s in the Change Vector is between the minimum and maximum velocity parameters, the particle position is updated as usual. If the number is under the minimum velocity, random 0s are flipped until the condition is satisfied. Conversely, if the number is over the maximum velocity, random 1s are flipped. Finally, the new position of the particle \(x_{i+1}\) is found in step 9 by taking the exclusive disjunction of the Change Vector and the particle's current position \(x_{i}\). Finally,  
\[x_{i}\oplus(\text{Change Vector})=x_{i+1}. \tag{7}\]  
This DH-BPSO algorithm is executed using the IronPython2.7 interpreter built in to ANSYS HFSS.
## 3 Results and Discussion  
### Overview of the Research Cruise  
The LOF science team focused its survey efforts on two major regions of the Solomon Islands archipelago: the more populated western half of the country and the sparsely populated islands of the country's far east (Figure 2). Given the short-term (one-month) nature of the cruise, neither temperature (Figure 3a) nor salinity (Figure 3b) varied appreciably across the sites or over time (ranges of 28.9-30.9 \({}^{\circ}\)C and 34.0-34.8, respectively). Live coral cover (Figure 3c), which is described extensively in a sister article [21], averaged 35% across the 69 survey sites (of which reef corals were sampled from 33), even reaching 84% at one (see OSDF). Algae was actually more common than coral, averaging 46 \(\pm\) 19% (std. dev. for this and all other error terms unless noted otherwise). That being said, the mean coral/algae ratio was slightly above one (1.1 \(\pm\) 1.2). Although it is tempting to link the higher coral cover (and lower algal cover) of the remote, eastern half of the country with its markedly lower number of human inhabitants (see OSDF for population and other demographic data.), we regrettably did not measure key parameters, such as nutrient levels, that could more directly attest to human impacts [36; 37]. As mentioned in detail below in the context of the CHI predictive models, nutrient data, and additionally abundance of key coral food sources (e.g., zooplankton [38; 39]) should certainly be incorporated, if at all possible, in future such AI modeling analyses of coral health.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 6 Performance Evaluation of IoB-DTN  
In this section, we evaluate the performance of IoB-DTN protocol. First, we assess four buffer management policies. Then, we compare the impact of the number of copies spread in the network. Finally, we evaluate the impact of the variation of the transmission power of sensors.
## 4 Results  
To evaluate the SICS concept, three prototypical implementations were built: A SICS controller in the server mode (SM--Figure 5a), a SICS controller in the server-based mixed mode (SMM--Figure 5b) and a SICS controller in the client mode (CM--Figure 6b).  
In the following, only the two SICS controllers in SMM and SM mode are considered in more detail, since these could be particularly suitable for future control applications.