esa-sceva/satcom-chunk-collection huggingface dataset:
# Acoustic Emission Based Fault Detection of Substation Power Transformer
## 3 Methods
### Feature Dimension Reduction
#### 3.2.1 For Analysis with PCA  
Firstly, the two-dimensional spectrogram feature of 1 s AE signals data was transferred to a one-dimensional vector via a one-by-one connection of the column vector. After the treatment, each vector of the 1 s AE signals is composed of 9 \(\times\) 39 characteristic parameters. The central calculation of the PCA method is to find the covariance matrix for the new one-dimensional vector, and then find the eigenvalues of the covariance matrix and its corresponding eigenvectors, and finally find the optimal projection axis. For choosing the optimal projection axis, we set the eigenvalue contribution reconstruction threshold \(\mu\) according to the research needs. The eigenvalue contribution reconstruction threshold \(\mu\) is between 0 and 1. After the eigenvalues \(\lambda_{i}\) are sorted from large to small, the eigenvalue contribution is calculated as follows:  
\[\mu=\frac{\sum_{i=1}^{k}\lambda_{i}}{\sum_{i=1}^{n}\lambda_{i}}\quad 1\leq k\leq n \tag{4}\]  
In general, the larger the eigenvalue contribution is the more information about the original data we keep. The PCA method used in this paper mainly includes the following steps:  
Figure 2: The 9 \(\times\) 39 feature vectors of the 1s AE signals recorded from a certain transformer in the normal running state (**a**) and the discharging state (**b**).  
Constructing total sample matrix: We employ a total of 13,968 (90% of total samples) samples as training data, which take the feature row vectors of each AE signals data of the training sample as a row of the two-dimensional matrix into a 171 \(\times\) 13,968 matrix.  
Standardizing the total sample matrix: the mean of each row of the training sample matrix is obtained and the mean of the corresponding row is subtracted from each feature value in the row to obtain a new two-dimensional feature matrix. In the testing process, the feature vectors of the test sample also need the standardizing process a,  
\[{x_{ij}}^{*}=x_{ij}-\overline{x_{ij}} \tag{5}\]  
where \(\overline{x_{ij}}=\frac{1}{n}\sum_{i=1}^{n}x_{ij}\). The covariance matrix of the new feature matrix of the training sample is calculated. The eigenvalues and eigenvectors of the covariance matrix are calculated and the eigenvalues are arranged in descending order. The eigenvalue contribution will be calculated and discussed in the results and discussion section of this paper. By selecting the different eigenvector sequence length according to the order from large to small, the different projection matrix of the optimal projection axis is obtained and can be used for dimension reduction and performance comparison.  
After the standardization process, the feature of each 1 s data (training sample, test sample) is projected onto the projection matrix, and the new feature of the training sample and the testing sample is obtained which can achieve different dimension reduction effects by selected different eigenvector sequence length.
## 4 Results
### Experimental Results
#### 4.3.3 Short-Circuit Proof Algorithm  
In this section, the four scenarios simulated in Section 4.2.3 are reproduced in the experimental platform to validate the real capabilities of the proposed fault current limitation strategy. In the following lines, it is demonstrated that the time response for the fault current limitation action and voltage recovery offers superior time responses than in the current literature [38; 39; 40].  
Scenario 1--Phase to neutral short-circuit. Figure 18 shows the behaviour of the voltage and current of phases \(u^{\prime\prime}\) and \(v^{\prime\prime}\) when unipolar \(u^{\prime\prime}n^{\prime\prime}\) is produced. Analogously with Figure 12a, it can  
Figure 17: Inverter in grid-connected mode. Unbalanced set-point: \(P_{u}=P_{w}=30\) kW, \(P_{v}=-30\) kW and \(Q_{uvw}=0\) kvar.  
be seen in Figure 18a that, when the fault appears, the control maintains the current limited with a sinusoidal waveform presenting a minor oscilation that is resolved in less than 30 ms. Figure 12b shows the system behaviour when the fault is cleared, recovering the nominal voltage value progressively in less than two grid cicles, in the same way as the simulation reproduced in Figure 12b.  
Scenario 2--Phase to phase short-circuit. Figure 19 shows the behaviour of the voltage and current of phases \(u^{\prime\prime}\) and \(v^{\prime\prime}\) when a bipolar \(u^{\prime\prime}v^{\prime\prime}\) short-circuit is generated and recovered, analogous to simulations shown in Figure 13. In Figure 19a, it can be seen that the initial transient of current \(u^{\prime\prime}\) and \(v^{\prime\prime}\) is less than two times the maximum current, achieving steady-state values in less than 30 ms. When the fault is recovered, the voltage increases progressively achieving the steady-state in less than 40 ms as shown in Figure 19b.  
Scenarios 3-4--Three-phase short-circuit and Three-phase to neutral short-circuit. Figures 20 and 21 show the behaviour of the voltage and current of phases \(u^{\prime\prime}\) and \(v^{\prime\prime}\) when a tripolar and tetrapolar fault is produced. As in the simulated cases shown in Figures 14 and 15, the control algorithm limits the fault current in the expected time, being less than 40 ms. The fault recovery is also achieved rapidly, in less than two grid cycles.
## 3 Results
### Selection of Node Importance Evaluation Indicators
#### 3.1.2 Betweenness Centrality (BC)  
In complex networks, the number of shortest paths through a node as a percentage of the number of all shortest paths is defined as the inter-node centrality of that node. In practice, inter-node centrality is an important global geographic metric. It better reflects the importance of individual nodes in the network and is a statistical indicator of the importance of nodes or edges in the entire network. It can be written as follows:  
\[BC(v_{i})=\sum_{s\
eq i\
eq t}\frac{N^{(i)}{}_{st}}{N_{st}}, \tag{3}\]  
where \(BC(v_{i})\) denotes the BC of node \(v_{i}\). \(N^{(i)}{}_{st}\) denotes the number of shortest paths between nodes \(v_{s}\) and \(v_{t}\) that through node \(v_{i}\). \(N_{st}\) denotes the total number of shortest paths between nodes \(v_{s}\) and \(v_{t}\).
## 4 Analysis of the Bearing Capacity and Crack Width of SCGCs
### Crack Width
The failure mode of SCGC is similar to that of traditional concrete columns, but the mechanical properties of CAGC are different from ordinary concrete, and the SSR does not have a yielding platform, which leads to the existence of a special column stress situation and crack development, so this paper suggests the use of SCGC in real projects. The load carrying capacity prediction can be referred to Equations (5) and (6) and the crack width prediction can refer to Equation (18). The accuracy of these equations is verified in this paper and the results show that the equations are reliable.
## 5 WSN Application Design
### Node Placement Constraints
This leads to a reduction in transmission distances because of the ground effect suffered by the antennas of the nodes, which affects its radiation pattern in the directions parallel to the ground. This issue motivates the use of clustered barrier topologies (described in Section 3). An example of these can be found in [37], where two types of nodes are used in the deployment: (i) nodes located on the center of the lanes, with a transmission range of a few meters, and (ii) nodes on the roadside transmitting to neighbors at a distance of 50 meters. The second type of nodes can be placed at more elevated locations to reduce the ground effect. Transmissions occur from nodes at the lanes to their closest roadside node and, then, the messages are forwarded by the roadside string topology. The location of parking lots' nodes is different to that stated in previous paragraphs. Node separations are necessarily small, as they correspond to the separation between parking spaces. However, in order to transmit to data sinks or cluster heads, these separations may be larger. The problem, in this particular case, stems from the disturbance that metallic vehicles cause to the communications. As it was written in Section 4, nodes must be placed at the center of the parking spaces in order to achieve proper detection. However, this implies that whenever a vehicle parks in the space under consideration, it may block the signal sent and received by the node beneath. Therefore, the transmission ranges are considerably reduced, as shown in [28]. In the worst case, for a transmission between two nodes at ground level that are covered by two parked vehicles, only lateral communications are possible at a maximum distance ranging from 2 to 4 meters. If only one of the nodes is covered by a vehicle, transmissions are reliable up to 5 meters, erratic up to 10 meters and impossible for greater distances. In some cases, if transmission ranges are not large enough, the use of intermediate relay nodes may be necessary. This has been studied in [74], proposing an algorithm to optimize the emplacement of relay nodes in order to minimize power consumption.
# Combined Optical Fiber Transmission System Based on QNSC and BER-LM
## 1 Introduction  
In order to realize the security of an optical fiber physical layer, optical fiber physical layer encryption technology and physical layer key distribution technology are the current research focus. QNSC is a representative optical fiber physical layer encryption technology [1; 2; 3; 4]. It masks the original signal through high-order QAM or PSK modulation to improve transmission security [5; 6; 7]. In the previous paper, by measuring the bit error rate (BER) of the optical fiber loopback channel, the communication parties could negotiate to obtain a consistent key through quantization coding [8; 9]. The influence of polarization mode dispersion (PMD) on legitimate communication parties is similar, which ensures that both parties can negotiate to obtain a consistent key [10]. In 2008, the California Institute of Technology proposed a key distribution scheme using ultralong fiber lasers [11; 12; 13; 14; 15]. The quantum inseparability and immeasurability can also be used for key distribution [16; 17; 18].  
In general, a physical layer encryption scheme and a key distribution scheme are implemented in mutually independent channels. For example, the classical optical fiber encryption transmission channel cannot be compatible with the quantum key distribution (QKD) channel. The key distribution system based on ultralong fiber lasers cannot transmit encrypted signals. Because the classical encryption channel and key distribution channel are incompatible, the limited fiber channel resources cannot be fully utilized, resulting in a waste of spectrum resources, increasing system complexity and equipment costs.  
In this paper, based on the combination of QNSC and BER-LM, an integrated method of encryption transmission and key distribution in a single optical fiber channel is proposed. The feasibility of this method was verified by simulation. The simulation demonstrated that in the coherent transmission system of a single wavelength fiber channel, encrypted data transmission and key distribution could be realized simultaneously, and the data transmission rate was 20 Gbps. We compared the impacts of different key agreement bitson the performance of encrypted transmission. When the negotiation bit was set to seven, the Q factor of the encrypted transmission signal was nine, which met the transmission performance requirements.
## 5 Acceleration Amplification Coefficients  
To investigate the amplification or attenuation of seismic waves propagating in a bridge structure, Table 9 gives the Z-direction acceleration amplification factors for pier 2 and 4. These values were the ratio of the acceleration response to the input acceleration. Only pier 2, subjected to the Lanzhou wave, showed attenuation of the peak acceleration. In contrast, the acceleration amplification factor reached 1.68 for pier 4, which would increase the axial force on this pier. With multidirectional excitation, only pier 2, subjected to the El Centro wave, showed attenuation of the seismic response. Meanwhile, the amplification factor reached 1.5 for pier 2, subjected to the Lanzhou wave.  
These results indicated that the amplification effect was most obvious for vertical acceleration with the Lanzhou wave. Therefore, a vibration isolation design needs to be carried out to improve the seismic performance and energy attenuation of this type of bridge.  
The bridge prototype for this test was designed and completed by the engineer according to the specifications for the design. Although some research has been conducted herein,  
Figure 11: Collision between beams 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Piers.**} & \multirow{2}{*}{**Position**} & \multicolumn{2}{c}{**El Centro**} & \multicolumn{2}{c}{**Lanzhou**} \\ \cline{3-5}  & & **Z** & **X + Y + Z** & **Z** & **X + Y + Z** \\  & & **Case No.1** & **Case No.3** & **Case No.7** & **Case No.9** \\ \hline \multirow{2}{*}{2} & Middle of pier & \(1.077\) & \(0.948\) & \(0.974\) & \(1.501\) \\  & Top of pier & \(1.074\) & \(0.935\) & \(0.666\) & \(1.486\) \\ \hline \multirow{2}{*}{4} & Middle of pier & \(1.058\) & \(1.090\) & \(1.663\) & \(1.210\) \\  & Top of pier & \(1.025\) & \(1.323\) & \(1.680\) & \(1.425\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Acceleration amplification factors of pier.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case No.**} & \multirow{2}{*}{**Beam 1 and Pier 2**} & **Beam 1 and Pier 4** & **Beam 2 and Pier 2** & **Beam 3 and Pier 4** \\ \hline
2 & 5.077 & 66.268 & 45.723 & 67.810 \\
3 & 4.647 & 65.655 & 48.342 & 67.260 \\
8 & 6.656 & 45.145 & 25.115 & 44.980 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Relative displacements of the pier and beams (mm).  
it is a preliminary study that can be further verified. A profound study will be made by performing nonlinear analyses using accurate models [27; 28] and methods [29].
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 5 Performance Evaluation
### Results  
#### 5.2.1 End-to-End Inference Delay  
Figure 6 shows the inference latency comparison of three horizontal segmentation baseline algorithms with FLP on different DNN models. The input data were generated on four access devices, the edge server used a GPU, and the transmission rate was fixed to 100 Mbps. It can be seen that the inference latency of **Edge** is higher than **Devices** because **Devices** is a device cluster including four devices with specific arithmetic capacity. There is also an additional time cost for clusters of devices to upload data to the edge servers. **ANS** and **FLP** are able to make an effective trade-off between on-device processing and edge offloading, thus achieving better results than **Edge** and **Devices**. **FLP** is based on the ADDP framework and has parallel optimization for local device processing and a reasonable cut-point selection, resulting in less transmission latency than **ANS**, which is more prominent for the more complex DNN.  
Figure 6: End-to-end inference delay.
# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 5 Experiments
### Ablation Experiments
The experimental results are shown in Figure 12.  
The classification effect of the model under all four feature information categories shows an increasing trend, where the classification effect of the model under feature d is ahead of the other three, and it can be seen that the classification performance is better when there is more feature information under the same experimental conditions. Finally, we experimentally compare the algorithm proposed in this paper with the KNN algorithm, a supervised learning algorithm, which is one of the most classical machine learning algorithms for classification and regression. It is based on the idea that if the majority of the K most similar (i.e., most adjacent) samples in the feature space belong to a class, then that sample also belongs to that class. That is, the method decides the category to which the sample to be classified belongs based only on the category of the nearest one or more neighboring samples in the decision classification. The experimental results are shown in Table 10.  
As can be seen from the data in the table, the detection effect of OCSVM is improved compared with the KNN algorithm, with an average improvement of about 10% for each index. Therefore, the OCSVM method is more accurate in detecting the safety condition of tunnel personnel and improves the safety of tunnel personnel. In conclusion, we believe that we have provided an efficient and concise machine learning algorithm that can achieve \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **NO.** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{2}{*}{Dataset 1} & OCSVM & 92.37\% & 93.34\% & 92.80\% \\  & KNN & 82.12\% & 82.21\% & 81.75\% \\ \hline \multirow{2}{*}{Dataset 2} & OCSVM & 90.37\% & 91.25\% & 91.10\% \\  & KNN & 79.14\% & 80.32\% & 80.63\% \\ \hline \multirow{2}{*}{Dataset 3} & OCSVM & 91.21\% & 91.37\% & 91.28\% \\  & KNN & 80.21\% & 80.55\% & 80.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of experimental results using OCSVM model and KNN model. Figure 12: Cross-sectional experimental comparison chart. better identification and classification despite the large deviation of positive and negative samples in the dataset (i.e., the number of positive samples may be much larger than the number of negative samples), and can be successfully applied to the field of tunnel personnel safety status detection.
## 3 Results and Discussion
### Model Characteristics
#### 3.1.1 Gap Filling and Model Curation  
During the process of manual curation described in the methods section, a total of 66 reactions were manually added to the initial model obtained from the results of re-annotation to fill gaps. Additionally, evidence from the literature was always considered, or of the well-studied _S. cerevisiae_. On the other hand, 336 reactions were removed from the initial model, have been removed for being unconnected reactions, general reactions, reactions using metabolites that are not included in the model, or reactions for which it was manually verified that the model does not have the enzyme coding gene. Additionally, the compartment of 79 reactions was changed, and 94 reactions were altered to become balanced. The complete list of alterations can be found in File S2.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 3 Star Background Simulation
### Lookup Table Design  
The actual position of the star during the simulation time may not match the catalog owing to the motion of both the star and the Earth. Therefore, before the simulation starts, it is necessary to conduct spatial and temporal processing of the catalog. In our system, we mainly considered the effects of the proper motion of stars. Equation (5) calculates the right ascension and declination of the star at the beginning of the simulation in the J2000 coordinate system [18]:  
\[\left\{\begin{aligned} \alpha&=\alpha_{0}+\mu_{\alpha}(t_{1}-t_{0}) \\ \delta&=\delta_{0}+\mu_{\delta}(t_{1}-t_{0})\end{aligned} \right., \tag{5}\]  
where \(\alpha\) and \(\delta\) are the right ascension and declination of the star at the simulation moment. The values \(\alpha_{0}\) and \(\delta_{0}\) are the right ascension and declination recorded by the catalog. The values \(\mu_{\alpha}\) and \(\mu_{\delta}\) denote annual proper motion of the right ascension and declination. This, \((t_{1}-t_{0})\), is the time difference between the simulation moment and the initial moment in years.  
Additionally, the uneven distribution of stars in the catalog means that processing all the data in each frame would take excessive time. To address this issue, star sensors often use star map partitioning to accelerate the star detection process. For our database, we adopted a common partitioning method used in the field [36]. A longitude-latitude grid is used when the latitude along the optical axis is less than 60\({}^{\circ}\), with one grid per degree; other high-latitude regions adopt a latitude band division with one band per degree of latitude. We created a two-dimensional array of size 360 \(\times\) 180 to reduce the time complexity from O(n) to O(1) by space for time. This corresponding grid can be loaded based on the sensor's optical axis point and FOV size; the required data can be obtained through deserialization, avoiding the traversal of the catalog during simulation. Because the array stores star data of high-latitude regions in the first grid corresponding to the latitude, it also avoided complex cross-region issues. The three catalog division methods are shown in Figure 6.  
Figure 5: Number of stars of different band and magnitude in MSX catalog.  
Figure 6: Different partition methods: (**a**) Mash table; (**b**) Latitude table; (**c**) Comprehensive table.
# Health Project Prioritization in Meta, Colombia, Using Anp-Dematel
## 1 Introduction  
The development of effective healthcare strategies presents a multifaceted challenge requiring comprehensive consideration of sector-specific determinants. As both a fundamental economic driver and a social welfare mechanism, healthcare systems play a pivotal role in national development. Healthcare institutions in Colombia, however, face systemic challenges, including insufficient service coverage (23% of medical needs went unaddressed in 2022), suboptimal care quality, and operational deficiencies [1]. These limitations are exacerbated by structural barriers--from service delivery shortcomings to administrative bottlenecks--with 33.7% of constitutional injunctions (tutelas) directly attributable to procedural delays [2]. Such systemic inefficiencies, rooted in inadequate policy frameworks and resource mismanagement, require regional health authorities and planning institutions to implement and necessitate strategic reallocation toward high-impact health initiatives.  
Within Colombia's Meta department (29 municipalities), extractive industry royalties--mandatory payments made by companies exploiting non-renewable natural resources such as oil and minerals that are paid to the State--have historically financed criticalsocial infrastructure [3]. The 2020 Royalty System Reform (Law 2056) fundamentally altered resource distribution mechanisms, disproportionately affecting Meta due to local governance capacity constraints [4]. While newly established Territorial Planning Councils have strengthened participatory budgeting [5; 6] local health authorities and planning institutions must implement rigorous, criteria-based health investment strategies that leverage community expertise in identifying localized health priorities.  
This study employs multiciteria decision analysis to optimize health project prioritization in Meta, where geographical fragmentation, infrastructure gaps, and environmental health risks disproportionately affect rural and vulnerable populations (including indigenous communities and agricultural workers). Analysis of epidemiological data by the authors reveals acute challenges: elevated preventable mortality rates, diabetes prevalence (18.7%), childhood malnutrition (12.3%), and persistent vector-borne disease threats [7]. These interconnected issues demand an integrated analytical approach that enables the simultaneous consideration of technical, social, and spatial factors, facilitates the modeling of interdependent criteria, and supports transparent, evidence-based decisions aimed at improving equity and health outcomes in complex territorial contexts.  
The authors adopted the ANP-DEMATEL hybrid methodology to address this complexity by (1) modeling criterion interdependencies; (2) quantifying systemic relationships, and (3) enabling evidence-based resource allocation [8].