esa-sceva/satcom-chunk-collection huggingface dataset:
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Data Preprocessing
#### 4.3.2 Data Labeling  
In this paper, two identical motors and a pair of identical bearings with the motor bearings are prepared. The stator, rotor, and bearing of one of the motors are artificially damaged to simulate the possible failure of the parts caused by the motor in the running state. Meanwhile, according to the six fault types set in the paper (the stator fault, rotor fault, bearing outer ring fault, bearing inner ring fault, bearing rolling body fault, and the synthetic fault of the front and rear bearings [21]), the faulty parts are, respectively, replaced to another motor that works normally, to collect data on the six fault states and normal states of the motor. Each fault type corresponds to its label, as shown in Table 3. At the same time, the comparison experiment with the fault-free motor is carried out to verify whether the method could determine the motor fault type fast and accurately when applied to the actual working scene.
## 2 Materials and Methods
### Calculation of the Developed Emission Factors
#### 2.3.2 Average Emission Factor
First, the virtual emissions of the charging process, thus the required charging power, are calculated. For this purpose, the hourly emission factor of the net power production \(Ef_{t,y}^{NPP}\) is multiplied by the power demand of the storage facilities' charging process \(E_{SII}\) (storage in) at hour \(t\). The annual sum of the virtual emissions is divided by the annual sum of the electricity quantity from storage \(E_{SIO}\) (storage out). This results in an annual emission factor \(Ef_{SIO,y}\) for the electricity produced from storage facilities, based on the average emissions emerged during the storage period (Equation (3)). The lower the emission factor for charging, the lower the emissions of the electricity provided from the discharging process. It is assumed that imported and discharged electricity is not used for charging. \[Ef_{SIO,y}=\frac{\sum_{t=1}^{8760}\left(E_{SII,t,y}\cdot Ef_{t,y}^{NPP}\right)}{ E_{SIO,y}} \tag{3}\]  
As shown in Equation (4), the total GHG emissions of gross power production at hour \(t\), minus exported GHG emissions and those from charging, plus imported GHG emissions and emissions of the amount of electricity discharged, are divided by the total net power consumption \(E_{t,y}^{NPC}\). This creates the hourly average emission factor of net power consumption \(AEF_{t,y}\). Depending on which fuel-specific emission factors are used, the AEF is referred to as \(\text{AEF}_{\text{fc}}\) for fuel combustion or \(\text{AEF}_{\text{fca}}\) for life cycle assessment. \[AEF_{t,y}=\frac{Em_{tot,y}^{GPP}-\left(E_{ex,t,y}\cdot Ef_{t,y}^{NPP}\right)+ \left(E_{im,t,y}\cdot Ef_{EUI,y}\right)+\left(E_{SIO,t,y}\cdot Ef_{SIO,y} \right)-\left(E_{SII,t,y}\cdot Ef_{t,y}^{NPP}\right)}{E_{t,y}^{NPC}} \tag{4}\]
## 4 Discussion
These technologies offer the promise of automating repetitive and laborious tasks, thereby enhancing worker well-being and productivity [86]. However, this shift towards automation also presentschallenges and opportunities, particularly in terms of its effects on job markets and the emergence of new types of employment [87]. Understanding the implications of these changes for job markets is noteworthy, as it can shed light on the potential effects on job satisfaction and workplace injuries [88]. By focusing on the human aspect of technological integration, it is possible to pave the way for a future where technology complements human capabilities, fostering collaboration and well-being [89]. Future investigations and projects may focus on such human-robot collaboration to improve human welfare and achieve better productivity. An interesting idea could be to achieve the proposed outcome using reinforcement learning and different measurement devices, as discussed throughout this paper. Another hot topic is the concept of Human Digital Twins (HDTs), which represents a relatively new and powerful approach in the field of human-robot interaction. This means creating detailed digital representations of human beings, offering a new opportunity to enhance how humans interact with robotic systems. The technology is in its early stages; however, it holds the promise of facilitating more intuitive, efficient, and personalized interactions between humans and robots [82]. As we venture deeper into the realm of collaborative robotics, reinforcement learning, and human-robot interaction, it is imperative to address the ethical considerations that come with these technological advancements. The integration of robots into human-centric environments raises important questions about privacy, autonomy, and the potential for unintended consequences [90]. How do we ensure that these technologies respect individual privacy and autonomy? What measures do we take to prevent misuse or abuse of such technologies, particularly in sensitive areas like health care and personal assistance [91]? Furthermore, the potential emotional impact on humans interacting with robots, especially those designed to mimic human behaviors and emotions, must be carefully considered [49]. It is essential to develop and adhere to ethical guidelines and standards that prioritize human well-being, ensuring that the deployment of these technologies enhances rather than diminishes human experiences and values. The application of collaborative robotics with reinforcement learning presents a very large horizon of possibilities. As further research continues to advance in this area, new paradigms and applications will emerge, aiming to transform a variety of sectors, from manufacturing and logistics to health care and space exploration. This article has explored some of the most exciting possibilities for the future of these technologies. In this context, we propose a series of actions that can serve as a guide for professional researchers interested in harnessing the full potential of human factors, collaborative robotics, and reinforcement learning.
# Decision Optimization for Power Grid Operating Conditions with High- and Low-Voltage Parallel Loops
## 1 Introduction
But things in AC/DC interconnected power grids would be quite different, usually more complicated indeed. The significant contributions of this paper are just at this area. Due to intensive loads in a relatively narrow region, several HVDC (high-voltage direct current) inverter stations would emerge in adjacent areas of the same AC power grid, called the multi-infeed HVDC configuration. In this situation, switching transmission lines, on the one hand, would decrease short circuit current, and, on the other hand, would stretch the electrical distance between different HVDC inverter stations. As a result, the multi-infeed short-circuit ratio (MISCR) may be increased or decreased under different conditions, which means the stability of power systems would be influenced [17]. Therefore, the decision for the optimal parallel loop opening scheme must consider the support ability for HVDCs, which is evaluated by MISCR. In this paper, a decision optimization method is proposed, which coordinates the relationship between parallel loop opening effect and support ability for multiple HVDC links. Thus, this method can be used to solve the parallel loop problem not only in pure AC power grids but also in AC/DC interconnected power grids, which distinguishes the proposed method from previous research. The decision optimization method is expounded in this paper. In Section 2 the complex network theory used for opening parallel loops is discussed in detail. GN algorithm and modularity indicators are introduced. In Section 3 the generation process of candidate opening schemes is proposed. The weighted modularity indicator and weighted MISCR are defined respectively, and combined to filter generated opening schemes. In Section 4 an evaluation model for candidate schemes is founded based on AHP, which considers the relationship between stability and short circuit current level of a power system. A fuzzy evaluation algorithm is used to find the optimal opening scheme. In Section 5a New England 39-bus system and an actual power system are analyzed. Simulation results validate the effectiveness and superiority of this proposed method.
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 4 Results
### Experimental Data
#### 4.2.3 Data Preprocessing  
The experiment involves the TextGCN method for data preprocessing, which follows these specific steps:  
1. Segmentation and deduplication: The text undergoes segmentation, and common stopwords are removed;
2. Construction of vocabulary list: A vocabulary list is created based on the corpus, mapping each word to a unique index;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Configuration Information** \\ \hline Operating System & Windows 11 \\ GPU & NVIDIA GeForce RTX 4080 \\ Memory & 16 G \\ RAM & 32 G \\ Programming Languages & Python 3.6.13 \\ Deep Learning Frameworks & PyTorch 1.8.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental environment information.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Numeric Value** \\ \hline Initial Learning Rate & \(1\times 10^{-3}\) \\ Learning Rate of BERT Module & \(1\times 10^{-5}\) \\ Dropout Rate & 0.5 \\ Number of GCN Layers & 2 \\ Number of BERT Layers & 12 \\ Word Embedding Dimension & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter settings.  
3. Text graph construction: A heterogeneous graph is constructed for the entire corpus, where nodes represent words and documents, and edges signify co-occurrence relationships between words, and between words and documents;
4. Feature representation: Text is encoded using BERT to generate semantic embedding representations of text nodes. These embeddings are then integrated with global structural features captured by the GCN.
# Feasibility of Low Latency, Single-Sample Delay Resampling: A New Kriging Based Method
## 4 Results
### Adjustment of the Nugget Value  
The nugget value was already adjusted in Figure 11. In theory, the variogram nugget is equivalent to the squared mean amplitude of an additional white noise distortion (Section 2.7). For the single-delay scenario with noise, the nugget was set to \(n_{g}=10^{-6}\) for noise with \(-60\) dB RMSA and to \(n_{g}=10^{-8}\) for 12-bit quantization. Kriging interpolation without nugget resulted in a significantly higher RMSE (Figure 12).  
The single-delay scenario is highly sensitive towards white noise with spectral components above \(f_{L}\). White noise with \(-60\) dB RMSA pushes the RMSE to \(-25\) dB for the missing samples case. After setting the nugget value, the RMSE dropped below \(E_{Max}=-40\) dB for OSR \(>2.89\). Adding the nugget also reduced the filter corner frequency to 0.35 Hz (Figure 4). The single-delay scenario is not significantly affected because \(f_{40}\) is below this corner frequency. However, adding a nugget to the group-delay case would reduce \(f_{40}\), which has about the same value as the corner frequency. Furthermore, the group-delay scenario had already a low noise sensitivity of 0.8 dB (Figure 8). The use of the nugget is only recommended for the single-delay scenario.
## 4 Results and Discussion
### Data Generation Configuration
#### 4.2.2 Domain Adaption  
The AGAR dataset comprises two types of agar dish images, featuring bright and dark backgrounds. To evaluate the augmentation method for domain adaptation, we generated two separate datasets, one containing only _dark_ background images and the other containing only _bright_ background images. For validation, we still used both backgrounds. For this purpose, we utilized the best-performing augmentation method discussed earlier for pre-training.  
The model pre-trained and fine-tuned exclusively on images with dark backgrounds achieved an AP\({}^{50}\) of 92.1 and an mAP of 60.6, which was on par with our baseline trained on both colors (see Table 3). It is possible that this fine-tuned model overfitted to dark background images, as the validation set contained a higher number of such images, which may account for this improvement. In contrast, the model trained solely on bright background images achieved an AP\({}^{50}\) of 90.6 and an mAP of 57.6. These results were slightly lower than those of the model trained on mixed backgrounds and the model trained on dark backgrounds, which was likely due to the uneven distribution of background colors in the dataset, as mentioned earlier.  
Figure 4: Comparison of the mAP of YOLOv8-Nano [48] after pre-training on synthetic images and fine-tuning on real images across all classes in the AGAR dataset [4]. The synthetic images utilize various opacity values for the inpainted colonies.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Both**} & \multicolumn{2}{c}{**Dark**} & \multicolumn{2}{c}{**Bright**} \\  & **AP** & **mAP** & **AP** & **mAP** & **AP** & **mAP** \\ \hline _S. aureus_ & 93.6 & 59.2 & 93.7 & 59.7 & 91.2 & 56.1 \\ _B. subtilis_ & 97.1 & 67.7 & 97.2 & 67.7 & 96.4 & 65.9 \\ _P. aeruginosa_ & 94.8 & 63.6 & 95.0 & 63.3 & 94.6 & 61.1 \\ _E. coli_ & 96.0 & 70.1 & 96.2 & 70.1 & 95.7 & 68.1 \\ _C. albicans_ & 78.9 & 42.6 & 78.4 & 42.3 & 75.1 & 37.0 \\ \hline Mean & 92.1 & 60.6 & 92.1 & 60.6 & 90.6 & 57.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of our dataset augmentation for domain adaptation. The baseline is YOLOv8-Nano [48], which has been trained on both backgrounds (dark and bright) of the AGAR dataset [4]. This is in contrast to models that are trained only on bright or dark backgrounds.
## 2 Chebyshev Graph Convolutional Neural Network Modeling Method
### Data Preprocessing Based on PathGraph Method  
In the data preprocessing module, to conduct an in-depth analysis of the internal connections and potential patterns among data samples, the PathGraph method is employed to transform the data into graph representations [14]. The PathGraph method effectively retains local order information among data samples through iterative partitioning and the construction of ordered node pairs. By introducing the weight mechanism, the graph structure model can more accurately characterize the complexity and diversity of relationships among data samples.  
\[C=\begin{bmatrix}v_{1}\\ v_{2}\end{bmatrix} \tag{1}\]  
\[D_{ij}=\sqrt{\sum_{k=1}^{n}\left(v_{1k}-v_{2k}\right)^{2}} \tag{2}\]  
\[w_{ij}=\exp(-\frac{D_{ij}^{2}}{2\sigma^{2}}) \tag{3}\]  
For each of the adjacent nodes \(i\) and \(j\), their features are denoted as \(v_{1}\) and \(v_{2}\), respectively, and their two feature vectors are stacked into a matrix \(C\) in a row-wise manner. The matrix representation is shown in Equation (1). Then, the distance \(D_{ij}\) between adjacent nodes \(i\) and \(j\) is shown in Equation (2), which measures the difference between the feature vectors of adjacent nodes. Finally, the Gaussian kernel function is employed to precisely model the similarity of adjacent nodes, and the edge weight \(w_{ij}\) in the graph is computed accordingly. The edge weight directly reflects the similarity between nodes; the higher the similarity, the greater the weight. Its calculation is shown in Equation (3).  
Here, \(C\) denotes the matrix formed by stacking the feature vectors of nodes \(i\) and \(j\); \(v_{1k}\) and \(v_{2k}\) represent the \(k\)-th dimension of the feature vectors of nodes \(i\) and \(j\), respectively, while \(n\) denotes the total number of feature dimensions; \(D_{ij}\) is the distance between nodes \(i\) and \(j\); \(w_{ij}\) is the weight of the edge between node \(i\) and \(j\); and \(\sigma\) is the standard deviation of the Gaussian kernel, used to control the kernel's width.
# An Improved Analytical Algorithm on Main Cable System of Suspension Bridge
## 2 Segmental Catenary Theory of Main Cable
### Stiffness Formulation  
Following describe the procedure of stiffness formulation of the elastic catenary cable element. Considering \(q\), \(S_{0}\), \(EA\) as constants, partial differentiation of both sides of Equations (1) and (2) yield the following incremental relationships between the relative nodal displacements and nodal forces.  
\[\left\{\begin{array}{l}dH\\ dV\end{array}\right\}=[K]\left\{\begin{array}{l}dx\\ dy\end{array}\right\} \tag{4}\]  
\[[K]=\left[\begin{array}{l}K_{11}\ K_{12}\\ K_{11}\ K_{22}\end{array}\right]=[B]^{-1} \tag{5}\]  
\[[B]=\left[\sum\limits_{m=1}^{i}b\right]=\left[\begin{array}{ccc}\sum \limits_{m=1}^{i}b_{11}&\sum\limits_{m=1}^{i}b_{12}\\ \sum\limits_{m=1}^{i}b_{21}&\sum\limits_{m=1}^{i}b_{22}\end{array}\right] \tag{6}\]  
\[\left\{\begin{array}{l}dH=dH_{i}^{(k)}=dH_{i}^{(k-1)}=dH_{L}\\ dV=dV_{i}^{(k)}=dV_{i}^{(k-1)}=dV_{L}\end{array}\right. \tag{7}\]  
\[b_{11}=\frac{\partial l}{\partial H_{i}}=-\left(\frac{S_{0}}{EA}+\frac{1}{q} \ln\frac{T_{j}+V_{j}}{T_{i}-V_{i}}\right)-\frac{H_{i}^{2}}{q}\left[\frac{1}{T_ {j}(T_{j}+V_{j})}-\frac{1}{T_{i}(T_{i}-V_{i})}\right] \tag{8}\]  
\[b_{12}=b_{21}=-\frac{H_{i}}{q}\left[\frac{1}{T_{j}}-\frac{1}{T_{i}}\right] \tag{9}\]  
\[b_{22}=-\frac{S_{0}}{EA}-\frac{1}{q}\left[\frac{V_{j}}{T_{j}}+\frac{V_{j}}{T_ {i}}\right] \tag{10}\]  
where: \([K]\) is the stiffness matrix due to cable shape change from end point (e.g., left end) to segment point \(i\); if the segment point \(i\) become the other end point (e.g., right end), \([K]\) is the stiffness matrix of the main cable for the whole span; \(dx\), \(dy\) are the cumulative amount of change in span and elevation respectively from end point to segment point \(i\); and, \(dH_{i}(k)\), \(dV_{i}(k)\) are the increment horizontal and vertical component of cable force at segment \(i\), respectively.
## 3 Experiment  
The theoretical analysis described in the previous chapter was used to evaluate the experimental data. The experiment was performed on a simple pipe and gives data that are necessary to calibrate the numerical model. The scheme of the experiment is shown in Figure 4.  
Steel pipes (0.8, 1 and 2 m long) were used. The length is important because it determines the eigenfrequency of the response. The pipe was hung on ropes to ensure that the system mounting had a very low eigenfrequency and did not affect the measurement.  
The pipe was filled with water, which was delivered by a pump through a hose and a valve at one end. The air can exit through the valve at the other end. As soon as the pipe was full of water, the valves were shut, and the pipe was ready to be excited with a hammer. The static pressure in the pipe was kept at 1.9 MPa to suppress the influence of air because there was always some amount of air left.  
Response to the excitement was measured using pressure transducers, which were mounted to the pipe wall at both ends. The sampling frequency of the transducers was set to the value of ten times higher than the expected frequency of the response (e.g., 26 kHz for the pipe 1 m long), and the uncertainty was 0.25% from the range, which was 10 MPa (one sensor) and 25 MPa (the other sensor). In addition there were accelerometers on the flanges for measuring the acceleration of both ends in the axial direction (range \(\pm\) 500 m s\({}^{-2}\)).  
The response was measured with water and without water. Thus, it was possible to determine four material constants, which are necessary for calibrating the numerical model.  
Figure 4: Scheme of the experiment.  
First, the modulus of elasticity \(E\) (see Figure 2) and damping \(b\) were determined from the measurement without water. The damping is supposed to follow a hyperbolic law:  
\[b=\frac{\zeta}{f}, \tag{48}\]  
where \(\zeta\) is the damping ratio. The shear viscosity was 0.001 Pa s. Then, the speed of sound and the second coefficient of viscosity were determined from the measurement with water. In both cases, the coefficients were computed numerically to make the determinant of the appropriate matrix equal zero in Equations (41) and (46). The eigenvalue of the system can be computed from a discrete Fourier transform of the measured response according to Figure 5 and Equation (49).  
\[s_{\mathrm{s}}=\frac{\pi}{\sqrt{3}}\cdot f_{w}\pm i\cdot 2\cdot\pi\cdot f, \tag{49}\]  
where the frequency \(f\) corresponds to the first eigen frequency of the system.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 6 Conclusions and Future Works  
A novel mechanism to collect and share real-time traffic information, namely a CRT Map, was proposed in this paper. Vehicles cooperate with each other to gather and share their local traffic map thereby constructing an overview of the whole network. Based on the CRT Map, we proposed a geographic routing, called CBR, which considers the traffic density of consecutive road segments in routing decisions. Simulation results show the proposed solution has a better performance than GyTAR and RIVER, in terms of end-to-end delay and packet delivery ratio, with a low overhead.  
In this paper, the CRT Map is utilized to support routing in VANETs. However, real-time traffic information is valuable in many applications, such as intelligent traffic management, self-driving cars, emergency dissemination, and so on. We are working on applying a CRT Map to relieve traffic congestion by traffic light control and coordination. Conversely, the CRT Map is designed for general purposes in this paper. Moreover, if it is dedicated to a routing purpose, some aspects can be further optimized. For example, information exchange can be performed on demand, instead of periodically. We are working on improving the message exchange process to reduce the routing overhead. Further results will be reported in our forthcoming papers.  
This work was supported by the Ministry of Science and Technology under Grant MOST 105-2221-E-194-031, Taiwan.  
[PERSON] and [PERSON] designed the proposed schemes; [PERSON] refined the proposed schemes; [PERSON] conceived and designed the simulations; [PERSON] performed the simulations; [PERSON] and [PERSON] analyzed the data; [PERSON] verified the analysis; [PERSON] and [PERSON] wrote the paper; and [PERSON] refined the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
###### Abstract  
In the last decade, the differences in the information communication technology (ICT) infrastructures between urban and rural areas have registered a tremendous increase. ICT infrastructures could strongly help rural communities where many operations are time consuming, labor-intensive and expensive due to limited access and large distances to cover. One of the most attractive solutions, which is widely recognized as promising for filling this gap, is the use of drone fleets. In this context, this paper proposes a video monitoring platform as a service (VMPaaS) for wide rural areas not covered by Internet access. The platform is realized with a Software-Defined Network (SDN)/Network Functions Virtualization (NFV)-based flying ad-hoc network (FANET), whose target is providing a flexible and dynamic connectivity backbone, and a set of drones equipped with high-resolution cameras, each transmitting a video stream of a portion of the considered area. After describing the architecture of the proposed platform, service chains to realize the video delivery service are described, and an analytical model is defined to evaluate the computational load of the platform nodes in such a way so as to allow the network orchestrator to decide the backbone drones where running the virtual functions, and the relative resources to be allocated. Numerical analysis is carried out in a case study.  
digital divide; fleet of drones; video monitoring; software-defined networks (SDNs); network functions virtualization (NFV); quality of service (QoS); analytical models; performance evaluation +
Footnote †: journal:  
_Future Internet_