esa-sceva/satcom-chunk-collection huggingface dataset:
# Distinction Between Interturn Short-Circuit Faults and Unbalanced Load in Transformers
## 6 Conclusions  
The combination of fault detection techniques of different natures, such as signal analysis and thermography, provides effective redundancy in detection. In this work, this redundancy is achieved, and, additionally, the dual detection approach allows the identification of false positives when dealing with load imbalances. The mean value of the ISP, through FFT analysis, effectively detects both imbalances and faults but cannot distinguish between them when dealing with incipient faults.  
It was found that the amplitude of the _2f_ frequency component in a two-turn short-circuit fault has a similar effect to that of a four-turn imbalance, with only a 5.55% difference in amplitude. However, the same frequency component in the four-turn short-circuit test showed a 300% increase compared to the four-turn imbalance test. The operating temperatures of the transformer under full load in two-turn and four-turn short-circuit conditions showed temperature increase rates of 5.4 \({}^{\circ}\)C per minute and 26.7 \({}^{\circ}\)C per minute, respectively, making this the key discriminating factor between an incipient short-circuit fault and a small imbalance.  
It is important to note that fault detection or diagnostic techniques in experimental platforms or modeling approaches that introduce external resistances to create an imbalance or remove turns from the winding to simulate an imbalance cannot be considered equivalent to an incipient short circuit with the same number of removed turns. This would be an incorrect practice.  
The effective discrimination of these types of faults is essential in ensuring the continuity of electrical service in power systems and can be achieved using basic signal analysis techniques combined with thermography.  
Conceptualization, R.A.O.-M. and D.A.A.-V.; methodology, R.A.O.-M. and J.C.O.-G.; validation, R.A.O.-M., J.C.O.-G. and V.A.M.-R.; formal analysis, R.A.O.-M., D.A.A.-V. and J.C.O.-G.; investigation, R.A.O.-M. and D.A.A.-V.; resources, D.A.A.-V. and R.E.-P.; data curation, V.A.M.-R.; writing--original draft preparation, R.A.O.-M. and D.A.A.-V.; writing--review and editing, D.A.A.-V., J.C.O.-G. and V.A.M.-R.; visualization, R.A.O.-M. and D.A.A.-V.; supervision, R.E.-P.; project administration, D.A.A.-V. and R.E.-P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors are grateful for the financial support provided by the Secretariat of Science, Humanities, Technology, and Innovation (SECIHTI).  
The authors declare no conflicts of interest.
# Ad-BBR: Enhancing Round-Trip Time Fairness and Transmission Stability in TCP-BBR
## 3 Overview of BBR
### Motivation
#### 3.2.2 Partially Periodic Entry but Meaningless ProbeRTT Phase  
To prevent real-time RTT estimation failure caused by prolonged periods without exploration, BBR is designed to automatically enter the ProbeRTT phase if the minimum RTT remains unchanged for ten seconds or if network conditions--such as queuing congestion--necessitate exploration. However, when the link's bottleneck has high bandwidth, traffic is low, or the RTT remains stable, entering the ProbeRTT phase is unnecessary and may lead to a temporary reduction in the sending rate and performance degradation, particularly for long-term connections with steady but low-rate transmissions. Therefore, we propose adapting the ProbeRTT triggering mechanism using time- or event-driven conditions, so that the ProbeRTT phase is initiated only when it is truly needed to detect a new minimum RTT and drain any existing queues on the link.
## 5 Implementation and Results
### Single Variable Optimization  
To verify the optimization framework, we firstly take equally distributed angles, with only one variable \(\phi\) to parameterize the problem as in Section 5.1, such that \(\mathbf{p}=[\phi,-\phi,\phi,-\phi,-\phi,\phi,-\phi,\phi]\). The signs here represent the rotation direction of each propeller. This single variable setup helps us to verify the optimization results by plotting the relationship between the cost function and the variable \(\phi\). If implemented correctly, the optimization should generate the minimum value on the cost function curve. Some settings for the optimization, the variable is limited between \([-10^{\circ},\,50^{\circ}]\), whose initial value is set to 5 degrees. The constraint is chosen as the condition number of the \(\mathbf{B}\) matrix, which must be smaller than a threshold such that the resultant system is NOT ill-shaped in its controllability.  
In Figure 11, the trend between cost function and constraint with respect to the optimization variable \(\phi\) is shown. The initial value and optimal value calculated by the optimization framework is marked by yellow and red astray symbols. Clearly, the value calculated by the optimization achieves the global minimum of the cost function and resides well inside the constraint limit. Figure 12 shows a direct comparison between the initial AMS, the optimal AMS, and the RMS in the \(\mathbb{R}^{3}\) space. The coverage margin of the AMS over the RMS is largely increased, especially in the yaw axis where the initial margin was relatively low. The optimal AMS sacrifice a little performance on the roll axis compared to the initial one, but the resultant margin is still rather high. Note that, the system seems unable to handle the positive corner of the load-factor axis despite the optimal configuration. This reminds of a preliminary design issue, or too ambitious requirements presumed.  
Figure 11: Relation of cost function and constraints with the optimization variable, each is a one-to-one mapping since we have only one optimization variable. This figure shows that the optimizer successfully locates the optimal point which also fulfills the constraints.  
Figure 13 compares further the margin factor cumulative density function. For the initial and optimal AMS, the margin factors of all RMS vertices are gathered and counted to formulate the cumulative function. The curve of optimal AMS is clearly shifted right-ward, indicating an increased probability of safety margin. For the optimal AMS, only less than 10% of the margin factor is smaller than 1.5, whereas the number for initial AMS is more than 30%. Additionally, the biggest margin is increased from 3.4 to 4.3, with a marginal increase in the smallest value.
# An Efficient Library for Reliability Block Diagram Evaluation
## 2 Reliability Block Diagrams
### Quantitative Evaluation Using RBDs
#### 2.2.1 Quantitative Evaluation: General Formulas
The probability of _success_ of a bridge block can then be computed through the formula: \[p_{bridge}=p_{E}\cdot(1-q_{A}\cdot q_{C})\cdot(1-q_{B}\cdot q_{D})+\] (8) \[q_{parallel}=\prod_{i=1}^{N}q_{i}=\prod_{i=1}^{N}(1-q_{i})\]0  
One could argue that the formulas to compute probability of _success_ of series and parallel blocks are specific cases of the Koo\(N\) block: series block can be treated as a No\(N\) block, while parallel block can be treated as a 1 oo\(N\). On the other hand, the mathematical representation for the specific cases of series and parallel blocks is simpler, thus justifying the usage of two additional formulas.
## Article  
Whole Rock, Mineral Chemistry during Skam Mineralization-Case Study from Tongshan Cu-Mo Skam Profile  
**[PERSON] 1,2**, [PERSON] 1,2,* and [PERSON] 1,2  
* **Abstract:** Studying the activation, migration and precipitation processes of ore-forming elements is essential for understanding the genesis and mechanisms of skarm deposits. A typical skarm profile formed by the intrusion of Yanshanian granodiorite into the Carboniferous carbonate strata was studied. The profile is highly consistent with the classic skarn profile, ranging from the intrusion, weak alteration belt, skarn belt (inner and outer skarn belt) and mineralization belt (mainly characterized by Cu mineralization) to the surrounding marble without being affected by late-stage low-temperature or supergene weathering alteration. Whole-rock data show that the major and trace elements exhibit relatively small changes in the granodiorite and inner skarn, but huge variation in the boundary between the inner and outer skarn; Na, Al, Ti and Sr show significant decreases, while Fe, Mg, Zn, V and Ni show significant increases. The elemental content in the outer skarn is 10-100 times or more higher than that in the marble, but the elements such as Ca, Sr and Cs diluted from the marble. During the migration process from the inner skarn to the outer skarn, some elements (such as K, Rb and Ba) were depleted in the inner, but not enriched in the outer, indicating that they may migrate to farther locations. Grossularite developed in the inner skarn, with light rare earth element (LREE) depletion and heavy REE enrichment, as well as positive and negative anomalies of Eu (\(\delta\)Eu = 0.42-3.95). Andradite developed in the outer skarn, with zonation development, light REE enrichment, and heavy REE depletion and a positive Eu anomaly (\(\delta\)Eu = 0.36-46.83). Some negative Eu anomalies appear at the edges of garnets in the outer skarn, indicating fluctuations in \(f\)O\({}_{2}\) during the late skarn process. A positive correlation between Fe\({}^{3+}\) and REE\({}^{3+}\) in the garnets from the inner skarn, as well as between Al\({}^{3+}\) and REE\({}^{3+}\) from the outer skarn indicated that there are different YAG substitution mechanisms of REE between the inner and outer skarn. Low garnet REE contents and highly variable Y/Ho ratios in outer skarn suggest that the significant fluctuations in REEs may be primarily controlled by water-rock interactions. Considering the whole-rock major and trace element contents, as well as the trace element features of garnet, we found that whole-rock Na, Al, Ti and Sr elements, garnet Ti, Zr and Nb elements exhibit significant differences between the inner and outer skarn. These characteristics can be used to distinguish the boundary between the rock body and carbonate during the skarmification process.  
**Keywords:** Tongshan Cu-Mo skarn deposit; skarn profile; garnet; boundary between intrusion and surrounding rocks +
Footnote †: journal: Applied Sciences
# An Overview of Key Technologies in Physical Layer Security
## 2 Background
### Multiple-Input Multiple-Output (MIMO) Communication  
MIMO is a very powerful technology in wireless communication systems. A MIMO network structure consists of many antennas at the transmitter as well as at the receiver. MIMO technology has been well-studied and developed for the past decade, mainly because of its capability to significantly enhance performance and widen the coverage range of wireless communication systems [PERSON] et al. (2019). Some of its other important benefits that have been shown include, but are not limited to, its capability to provide higher data rates, improved reliability, and less noise and interference [PERSON] et al. (2019). Even though MIMO technology was proposed many years back, it only came to reality in the practical world in 2018 [PERSON] et al. (2019). In this review paper, we focus on the relevance and application of MIMO technology in PLS rather than its traditional capacity benefits in wireless communication networks. It was shown in [PERSON] et al. (2019) that MIMO systems are very robust against passive eavesdropping attacks since the secrecy capacity is directly proportional to the difference in capacities of the main and wiretap channels. Beamforming in massive MIMO was used in [PERSON] et al. (2019) to implement security in the physical layer. In [PERSON] et al. (2019), artificial noise generation was used to demonstrate the application of PLS in MIMO orthogonal frequency division multiplexing (OFDM) systems. A typical MIMO system is shown in Figure 9 wherein the transmitter, the intended receiver, and the eavesdropper are equipped with multiple antennas \(N_{A}\), \(N_{B}\), and \(N_{E}\). This is referred to as MIMO multiple-antenna eavesdropper (MIMOME) [PERSON] et al. (2019), and in this case the secrecy capacity can be expressed as  
\[C_{s}=\max_{K_{x}\succ 0,tr(K_{x}\preceq P)}\frac{\log_{2}|\mathbf{I}+\mathbf{H }_{b}\mathbf{K}_{x}\mathbf{H}_{b}^{H}|}{\log_{2}|\mathbf{I}+\mathbf{H}_{e} \mathbf{K}_{x}\mathbf{H}_{e}^{H}|} \tag{17}\]where **I** is the identity matrix, \(\mathbf{K}_{x}\) is the covariance matrix of the transmit signal \(x\), \(P\) is the maximum transmit power constraint, and \(\mathbf{H}_{b}\in\mathbb{C}^{N_{B}\times N_{A}}\) and \(\mathbf{H}_{e}\in\mathbb{C}^{N_{E}\times N_{A}}\) are the MIMO complex Gaussian channel matrices of the legitimate and wiretap channel, respectively. The signals received by the legitimate receiver and passive eavesdropper are given by Equation (18):  
\[\begin{split}\mathbf{y}_{b}&=\mathbf{H}_{b}\mathbf{ x}_{a}+\mathbf{n}_{b},\\ \mathbf{y}_{e}&=\mathbf{H}_{e}\mathbf{x}_{a}+ \mathbf{n}_{e},\end{split} \tag{18}\]  
where \(\mathbf{x}_{a}\in\mathbb{C}^{N_{A}\times 1}\) is the transmit signal, and \(\mathbf{n}_{b}\in\mathbb{C}^{N_{B}\times 1}\) and \(\mathbf{n}_{e}\in\mathbb{C}^{N_{E}\times 1}\) are zero-mean complex white Gaussian additive noise vectors. Equation (18) is used as a fundamental tool in optimization techniques in PLS and also to demonstrate the application of MIMO in Section 3.
# Post-Quantum Biometric Authentication Based on Homomorphic Encryption and Classic McEliece
## 4 Practical Realization of the Proposed Biometric Protection Scheme for Facial Recognition
### Experimental Results  
#### 4.2.1 Face Databases  
In order to demonstrate the efficiency of our proposal, two public databases were selected: FERET and LFW. Both databases exhibit variations in pose, expression, sex, age and illumination. The FERET database [39], distributed by NIST, was considered because it is a standard database. Since the acquisition conditions in the FERET database were semi-controlled (the same physical setup was employed but it was reassembled for each session), the LFW database was also considered. The LFW database [40] was designed for unconstrained face recognition by using images collected from the web. Since, during the face detection and crop process, some samples are lost, the extracted embeddings were 8160 of 994 individuals in FERET and 12,770 embeddings of 5749 individuals in LFW databases.
# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 3 Methods
### Asff-Fpn  
The accurate detection of container numbers poses a significant challenge due to the presence of large amounts of noise and cluttered information during the collection process. To harness the semantic information of high-level features and the fine-grained details of low-level layers, the DBNet detection network employs FPN to output multiple feature layers. However, this method merely resizes and sums the feature maps, failing to exploit the information encoded in features of varying scales. In this study, we introduce the ASFF module. The core concept of ASFF is to adaptively learn and integrate the spatial weights of feature maps at different scales, thereby maximizing the utilization of effective feature information. The structure of the ASFF-FPN module is illustrated in Figure 4.  
ASFF comprises the following two components: feature adjustment and adaptive fusion. Firstly, ASFF represents the four distinct feature levels obtained from the backbone network \(x^{l}(l\in\{1,2,3,4\})\). To integrate these varying-level features, the feature maps undergo upsampling and downsampling operations using diverse strategies, yielding four feature maps of identical scale. Subsequently, the feature maps of each scale are scaled by the weight parameters \(\alpha\), \(\beta\), \(\gamma\), and \(\delta\) obtained through network training. The specific formula can be expressed as follows:  
Figure 4: ASFF-FPN module.  
\[y^{l}_{ij}=\alpha^{l}_{ij}\cdot x^{l\to l}_{lj}+\beta^{l}_{ij}\cdot x^{2 \to l}_{lj}+\gamma^{l}_{ij}\cdot x^{3\to l}_{lj}+\delta^{l}_{ij}\cdot x^{4 \to l}_{lj} \tag{4}\]  
where \(y^{l}_{ij}\) represents the \((i,j)\)th vector in the output feature map \(y^{l}\). \(\alpha^{l}_{ij}\), \(\beta^{l}_{ij}\), \(\gamma^{l}_{ij}\), \(\delta^{l}_{ij}\) are the spatially significant weights of three different levels of feature maps at level \(l\), which are learned adaptively.  
Therefore, the AFF-FPN module, combined with an adaptive feature fusion network, achieves a deep fusion of semantic information across scales, significantly improving the accuracy of text detection.
# Improved Localization and Recognition of Handwritten Digits on MNIST Dataset with ConvGRU
## 4 Experiments
### Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **SSIM (\(\uparrow\))** & **PSNR (\(\uparrow\))** & **MSE (\(\downarrow\))** & **FVD (\(\downarrow\))** & \
\begin{tabular}{c} **Training** \\ **Time (h)** \\ \end{tabular} &
\begin{tabular}{c} **Memory** \\ **Usage (GB)** \\ \end{tabular} \\ \hline SimVP (CVPR’22) & 0.892 & 28.3 & 0.046 & 168.2 & 24.5 & 11.2 \\ DMVFN (CVPR’23) & 0.901 & 29.1 & 0.042 & 156.3 & 18.3 & 9.8 \\ PredRNN (NIPS’21) & 0.887 & 27.8 & 0.049 & 172.1 & 22.7 & 10.5 \\ PhyDNet (CVPR’20) & 0.885 & 27.5 & 0.051 & 175.4 & 23.1 & 12.3 \\ E3D-LSTM (ICLR’20) & 0.889 & 28.1 & 0.047 & 170.8 & 25.6 & 13.1 \\ ConvLSTM (NIPS’15) & 0.873 & 26.4 & 0.058 & 185.2 & 20.2 & 8.7 \\ MAU (ICCV’21) & 0.895 & 28.7 & 0.044 & 163.5 & 21.8 & 10.8 \\ MotionRNN (ICLR’21) & 0.898 & 28.9 & 0.043 & 159.7 & 19.5 & 10.1 \\ CrevNet (ICLR’20) & 0.888 & 27.9 & 0.048 & 171.3 & 23.8 & 11.5 \\ \hline
**Ours (ConvGRU)** & **0.913** & **30.2** & **0.038** & **148.5** & **16.8** & **8.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison with state-of-the-art methods. The system's ability to handle diverse writing styles represents a significant advancement in the field. Our analysis reveals consistent performance across different cultural writing patterns, with accuracy variations limited to less than 5% between Western, Asian, and Arabic numeral styles. This cultural robustness makes our system particularly valuable for international deployment scenarios. The computational efficiency of our approach deserves special attention. Through careful architecture design and optimization, we achieved a 40% reduction in memory usage compared to state-of-the-art methods while maintaining superior accuracy. The model requires only 256 MB of memory during inference, making it suitable for deployment on resource-constrained devices.
## 3 Key Challenges
### Integrated Design  
The Underwater CPNT network aims to provide integrated CPNT services [51]. Among these functions, communication service is a prerequisite for positioning, navigation, and timing. The position service provides location information for underwater vehicles, the navigation service plans the route from the current location to the destination, and the timing is essential to maintain temporal and spatial consistency. The network is the basic support for the realization of a large-scale underwater CPNT service.  
The aforementioned challenges reveal fundamental limitations in existing CPNT approaches. To overcome these barriers, such as software and hardware redundancy, as well as acoustic, electric, and magnetic incompatibility, which are mostly caused by separated design and implementation. We proposed an integrated design of the CPNT system, which can improve bandwidth usage efficiency and acoustic compatibility, reduce development cost, equipment size and power consumption.
# An Adaptive Routing Algorithm Based on Relation Tree in DTN
###### Abstract  
It is found that nodes in Delay Tolerant Networks (DTN) exhibit stable social attributes similar to those of people. In this paper, an adaptive routing algorithm based on Relation Tree (AR-RT) for DTN is proposed. Each node constructs its own Relation Tree based on the historical encounter frequency, and will adopt different forwarding strategies based on the Relation Tree in the forwarding phase, so as to achieve more targeted forwarding. To further improve the scalability of the algorithm, the source node dynamically controls the initial maximum number of message copies according to its own cache occupancy, which enables the node to make negative feedback to network environment changes. Simulation results show that the AR-RT algorithm proposed in this paper has significant advantages over existing routing algorithms in terms of average delay, average hop count, and message delivery rate.  
DTN; routing algorithm; relation tree; adaptive
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 1 Introduction  
### Background and Motivation  
Wireless sensor networks (WSNs) are considered one of the most valuable technologies in the 21 st century. A wireless sensor network is constituted of a number of sensor nodes for information collection and comprehensive surveillance. Each node takes a perception, communication, and computation function. They communicate with each other in a multihop and self-organization manner through wireless radio. In the current development situation, WSNs have found wide research applications in intelligent life [1; 2; 3; 4], industrial and agricultural production [5; 6; 7; 8], disaster prevention and control [9; 10; 11], military applications [12], etc. In many applications, WSNs play an irreplaceable role. In particular, the advent of the Internet of Things era has accelerated the development and implementation of wireless sensor network technology and gradually led modern life to intelligence and convenience.  
In the monitoring area, the sensors acquire data, transmit them to the destination node through multihop mode, and finally deliver them to the user using the external network. At the same time, with the miniaturization of sensor nodes, there are higher requirements for WSNs in terms of the capacities of data acquisition, computing, storage, and energy consumption. More importantly, in a WSN, the routing protocol plans the route of data transmission and is one of the keys to collecting information successfully. Therefore, the design of the routing protocol is a hot topic.
## 7 Results
### Traffic Demand Scale of 0.50  
A 50% traffic demand produced 70,721 vehicles during the first hour of the simulation. The C-EEDR application decreased the fuel consumption with the increase in the market penetration rate, as shown in Figure 10. Here, we noticed that the C-EEDR application maintained the same behavior (decreasing fuel consumption) even at a full market penetration rate (in contrast with the previous two traffic demand levels).  
However, the system-wide travel time increased as the market penetration rate increased even at low market penetration rates, as shown in Figure 11. The same behavior is noticed also in the stopped delay, as shown in Figure 12.  
We ran the two-tailed \(t\)-test (\(p\)-values ranged between [0.15-0.86]) between the IDEAL-based C-EEDR and C-V2X-based C-EEDR applications and found that there was no statistically significant difference between the corresponding travel time, stopped delay, and the fuel consumption between the means of the 10 different seeds for each of the 9 market penetration rates at a significance level of 0.05.  
Figure 11: Average network-wide Travel Time of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with traffic demand of scale factor 0.5.  
Figure 10: Average network-wide Fuel consumption of Energy-Efficient dynamic routing application using IDEAL and C-V2X communication on LA road network with traffic demand of scale factor 0.5.