esa-sceva/satcom-chunk-collection huggingface dataset:
# Metamorphic Testing of Relation Extraction Models
## 3 Approach
### Metamorphic Relations for RE
#### 3.2.1 MRs with the Replacement Operation  
Previous studies have revealed that neural network models have poor performance when confronted with randomly permuted contexts [25]. For RE, **its prediction heavily relies on the information taken by the input entities (including entity names and entity types)**[11], while the capability of different RE models for handling different entities and relations is still unknown. According to this, **we design MRs to investigate how consistently RE can handle different entities** by replacing the head or tail entity with some candidate entities; that is, we leverage the replacement operation to generate test groups and investigate the capability of RE models. The replacement operation, which constructs follow-up inputs by changing some parts of the source inputs with appropriate replacements, is commonly adopted in the practice of MT [21]. Our replacement operation can be conducted at different levels by considering the characteristics of different entities. **MR-R\({}_{1}\): Replacement with the same type of entity. **  
The same type of entity expresses the same category of information. Consider, for example, a set of entities such as French, American, and Chinese, which have the same entity type: NATIONALITY. Although they take specific information, they all describe the information of nationality. Therefore, when replacing the head or tail entity with another one having the same entity type, RE should provide the same prediction. Suppose that there is an entity \(e^{\prime}_{h}\) (\(e^{\prime}_{t}\)) whose entity type is identical to that of \(e^{s}_{h}\) (\(e^{s}_{t}\)). The follow-up input is constructed as \((s^{\prime},e^{\prime}_{h},e^{s}_{t})\) or \((s^{\prime},e^{s}_{h},e^{\prime}_{t})\), where \(s^{\prime}\) is generated from \(s^{s}\) by replacing \(e^{s}_{h}\) (\(e^{s}_{t}\)) with \(e^{\prime}_{h}\) (\(e^{\prime}_{t}\)). Then, \(\mathcal{R}(t_{s})\) and \(\mathcal{R}(t_{f})\) are expected to be identical. An example MG of MR-R\({}_{1}\) is shown in the first row of Table 2. In the given sentence \"French filmmaker [PERSON] - dies at 80\", RE predicts the relation between the entities \"French\" and \"[PERSON]\" as \"per:origin\". After the tail entity \"French\" is replaced by \"American\", where both \"French\" and \"American\" have the entity type NATIONALITY, the prediction result remains unchanged. To obtain candidate entities of the same type as the original entity, we applied the named entity recognition tool Stanford NER ([[https://nlp.stanford.edu/software/CRF-NER.html](https://nlp.stanford.edu/software/CRF-NER.html)]([https://nlp.stanford.edu/software/CRF-NER.html](https://nlp.stanford.edu/software/CRF-NER.html)), accessed on 10 July 2022) to analyze the sentences, yielding sets of candidate entities of various different entity types. Then, for a given input, we randomly selected a candidate entity with the designated type and used it as a replacement.
## 3 Signal Model  
In this section, we present OFDMA-based signal models for CCU point-to-point transmission and 3-cell coordinated transmission, including MU- and SU-network MIMO. Consider a CCS \(\{0(a),1(b),2(c)\}\) as shown in Figure 1, where each cell employs \(120^{\circ}\) sectoring and is divided into cell-center and cell-edge regions. Users located in cell-center and cell-edge regions are referred to as CCUs and CEUs, respectively. We apply the traditional point-to-point and 3-cell coordinated transmission to CCUs and CEUs, respectively. Assume that each sector and each user are equipped with \(N_{T}\) sector antennas and \(N_{R}\) receiver antennas, respectively. The antenna pattern for each sector antenna in decibel is specified by [18]  
\[A(\theta)_{\text{dB}}=-\text{min}\left[12(\theta/\theta_{\text{3 dB}})^{2},A_{m }\right],-180^{\circ}\leq\theta\leq 180^{\circ}, \tag{1}\]  
where \(\theta\) is the angle between the direction of interest and the main-beam of the antenna, \(\theta_{\text{3 dB}}=70^{\circ}\) is the 3-dB beamwidth in degree and \(A_{m}=20\) dB is the maximum attenuation for the sidelobe.  
The overall radio channel model at subcarrier \(k\) of user \(u\) with respect to BS \(b\) is represented by  
\[\lambda_{k}^{(b,u)}=H_{k}^{(b,u)}\sqrt{A\big{(}\theta^{(b,u)}\big{)}K^{(b,u)}L ^{(b,u)}}, \tag{2}\]where \(H_{k}^{(b,u)}\) denotes the small-scale Rayleigh fading, \(K^{(b,u)}\) represents the shadowing effect and \(L^{(b,u)}\) is the path loss. The cross-correlation model for the shadowing effect is given by  
\[\rho=\left\{\begin{array}{cc}0.8-|\theta|/150\;\;\text{if}\;|\theta|\leq 60^{ \lx@math@degree}\\ 0.4\;\text{if}\;|\theta|>60^{\lx@math@degree}\end{array}\right., \tag{3}\]  
where \(\theta\) is the difference of angle-of-arrival between two radio links observed at the receiver. The propagation loss model between a user \(u\) and a BS \(b\) is given by [19]  
\[L^{(b,u)}=\beta_{0}\times\left(d^{(b,u)}\right)^{-\gamma}, \tag{4}\]  
where \(\beta_{0}\) represents the propagation constant, \(d^{(b,u)}\) is the propagation distance in kilometer and \(\gamma\) denotes the path loss exponent. According to [PERSON] theorem, the achievable channel capacity is given by  
\[W\times\log_{2}(1+\Gamma/\eta)\;\text{bits}/\sec, \tag{5}\]  
where \(W\) denotes bandwidth of the channel, \(\Gamma\) represents the SINR. The parameter \(\eta\equiv-\ln(5\times P_{e})/1.5\) is the SINR gap with the target bit error rate (BER) \(p_{e}\)[20]. In this paper, the target BER is given by \(10^{-4}\).
# Multiscale Dynamic Attention and Hierarchical Spatial Aggregation for Few-Shot Object Detection
## 2 Related Work  
### Few-Shot Object Detection Methods  
FSOD seeks to detect objects with limited annotated samples, addressing critical challenges in real-world applications. Based on the algorithm architecture, they can be categorized into two main types: transfer-learning-based methods and twin-network-based methods. Transfer-learning-based methods leverage pretrained models on large datasets, enhancing performance with few samples through fine-tuning, generative models, or attention mechanisms. For instance, AsyFOD [18] presents an asymmetric adaptation paradigm that adapts the model to unseen domains through domain-invariant feature learning, while [19] utilizes semantic embeddings to improve generalization to new classes. In contrast, twin-network-based methods, often employed in meta-learning frameworks, use shared-parameter networks to compare sample similarity, enabling fast adaptation to novel tasks but facing challenges with scalability [3]. For example, ICPE [20] introduces a novel information-coupled prototype elaboration framework, which dynamically adjusts prototype aggregation to enhance detection accuracy, while FFA [21] focuses on distilling fine-grained prototype representations for more accurate few-shot object detection. In the broader context of object detection, algorithms are traditionally classified into two categories: single-stage and two-stage methods [22]. Single-stage methods (e.g., YOLO [23]) directly predict the class and location of objects, while two-stage methods (e.g., Faster-RCNN [24]) first generate candidate regions and then perform classification and localization.
# Group Authentication Scheme for Neighbourhood Area Networks (NANs) in Smart Grids
## 3 The Smart Grid Network Model  
In this section, we present the smart grid network model considered for the discussion and detail the requirements for its secure operation. We also explain the potential security threats we consider for a case study to test the proposed solution. The smart grid network model considered for our discussion, is shown in Figure 2.  
It comprises of three network segments:  
1. Home Area Network (HAN): one smart meter (SM) and N smart devices (SDs). This group of devices is interconnected in a star topology with a SM as the star point.
2. Neighbourhood Area Network (NAN): mesh network (not necessary full mesh) of M SMs. SMs are divided into G groups. Group g (g = 1,..., G) has Mg SMs. Hence the following equation is considered: \[M=\sum_{g=1}^{G}Mg\] (1)  
One SM of each group is selected as Group Controller (GC). The GC is hereafter termed as the gateway node, GW.
3. Wide Area Network (WAN): Network (e.g., Internet) that connects GCs to the Network Operations Centre (NOC).  
The data generating elements are part of the HAN. This data traverses the entire network to reach the NOC. The smart meters, which are a part of the NAN, generate data, as well as receive data from the NOC. Therefore, traffic to the NAN elements is two-way. Data may or may not be forwarded into the HAN by the smart meters, depending upon the deployment requirement.
## 3 Results and Discussion
### Correlation Plot  
As mentioned earlier, the concentration variations in PM\({}_{2.5}\) and each chemical component are different from time to time, then the correlation was plotted for ionic components (sulfate, nitrate, ammonium), EC, OC, total trace metals, and total PAHs with respect to PM\({}_{2.5}\) concentration, which is shown in Figure 4. The concentrations of ionic components, EC, OC, and total metals increased with increasing PM\({}_{2.5}\) concentration, and their concentrations are considered to vary in the similar manner (Figure 4a-c). The EC and OC, both of which contain carbon, correlate to each other as shown in Figure 4b. Although we do not know the reason, both EC and OC seem not to increase when the PM\({}_{2.5}\) concentration is higher than 20 \(\upmu\)g\({}^{-3}\). EC concentrations tend to be higher when wind speeds are low (not shown in Figures). This is presumably because local emissions become stagnant at low wind speeds. As shown in Figure 4c, PAHs tend to increase when the concentration of PM\({}_{2.5}\) is low. The variation in PAHs is different from PM\({}_{2.5}\) and other chemical components. The correlations revealed that PAHs have a slightly different trend from other chemical components.  
Figure 3: Time series of mass concentrations of PM\({}_{2.5}\) and its chemical component in spring 2020. (**a**) PM\({}_{2.5}\), total ions, and total trace metals. (**b**) OC, EC, and total PAHs.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 5 Performance Evaluation  
### Evaluation Methodology  
In this section, we verify the performance of our algorithm via OPNET 14.5 simulations and compare it with the SSF that is the default AP association scheme in the 802.11 standards and Categorized algorithm proposed in [17]. Our simulation considers two typical 802.11 WLANs environments, which are shown in Figure 2. It includes (a) the uniform distribution where all clients are randomly distributed and (b) the hotspot distribution where most of the clients are deployed in a few of the circle-shaped hotspot areas. The network topology of WLAN contains a server, a switch, 20 APs and multiple mobile terminals. The 20 APs are deployed uniformly in 1000 \(\times\) 1000 m\({}^{2}\) area. While, the density of clients is dynamically changing and its number ranges from 40 to 200 at the interval of 20. Under different network densities, each algorithm simulation runs 30 times, and obtains the average performance for comparison. The coverage area of each AP is set to 150 m and thus the overlapping area exists between two adjacent APs. Since the transmission rate of the clients is difficult to obtain in realistic deployment, we acquire it by utilizing the relationship between the transmission rate \(R_{ij}\) and the Signal to Interference plus Noise Ratio (SINR) \(\gamma_{ij}\), which is shown in Table 2[22].  
The performance evaluation metrics include the average AP utilization, system throughput, delay, and the fairness index, where the average AP utilization is calculated by Equation (7). The fairness index that ranges from 0 to 1 and reflects the client fairness in time is defined as follows:  
\[\beta=\frac{\left(\sum_{i=1}^{M}y_{i}\right)^{2}}{M(\sum_{i=1}^{M}y_{i}^{2})}, \ \beta\in[0,1] \tag{14}\]  
where \(y_{i}\) is the allocated transmission time for client \(i\).
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Evaluation Methodology
#### 3.1.2 GF-2 Fusion Evaluation  
Table 3 shows the quantitative results of seven statistical indexes for GF-2 fusion images (Figure 5). In Table 3, the standard deviation in the four PCA bands is higher than that of other fusion techniques. The grayscale divergence produced by PCA fusion is also high, suggesting these images contain the most spatial information. The NNDiffuse results also exhibit high divergence and include more information in each band. It is evident that the information entropy in each band of the fused images is higher than that of the corresponding multi-spectral data. The four PCA bands exhibit the highest information entropy, indicating the information contained in each band is richer than in other fused images. The correlation coefficient is also higher in the fused images than in the original multispectral data. Specifically, PCA fusion produced the highest correlation coefficients in each band (among fusion techniques), suggesting the information is more uniform in these results.  
According to the value of the correlation coefficient, PCA fusion also produced the highest information retention rates in each band to the original multispectral data, although PCA can result in a loss of spectral information. In contrast, the spectral distortion and deviation index produced in each band by GS fusion were the lowest among fusion techniques, suggesting spectral information retention to be higher with GS fusion. The gray mean value of B1 and B2 in the NNDiffuse results was closest to that of the original multispectral images. The same was true for B3 and B4 in the PCA fusion results. The result shows that NNDiffuse and PCA are similar. Comprehensively considering the amount of information included in the image and the degree of spectrum retention, NNDiffuse could be considered more optimal for GF-2 data in the enclosure seine farming area.
# Simulation Study on the Installation of Helical Anchors in Sandy Soil Using SPH-FEM
## 3 Simulation Results and Discussion
### Influence of AR on Installation Force and Torque
The fitting results indicate that the influence coefficient of \(AR\) on the installation force ranges from 3.9 to 4.7 and tends to increase with the installation depth. Figure 11 shows that when a high \(AR\) is used for installation, there is also a linear positive correlation between the installation torque and \(AR\) at various depths. Similarly, the slope of the fitted line is defined as the influence coefficient of the \(AR\) on the installation torque. The fitting results show that the installation torque influence coefficient remains essentially unchanged with the increase in installation depth, being approximately 1.3 for the sandy soil used in this simulation. This value is significantly smaller than the influence coefficient of \(AR\) on the installation force, indicating that \(AR\) has a more pronounced effect on the installation force.
## 4 Conclusions  
The superplastic deformation behavior for the Al-Mg-Fe-Ni-Zr-Sc alloy in a temperature range of 420-540 \({}^{\circ}\)C and a strain-rate range of 0.002-0.1 s\({}^{-1}\) was studied. The testing conditions provided the strain-rate-sensitivity \(m\) coefficient of 0.35-0.55 and elongation to failure of 250-570%. Two mathematical models (Arrhenius type and artificial neural network) of the stress-strain behavior in superplastic conditions were developed. The following conclusions were drawn:  
* The alloy studied containing coarse Al\({}_{9}\)FeNi particles of crystallization origin and fine L\({}_{12}\)-structured Al\({}_{3}\)Zr dispersions exhibited a non-recrystallized grain structure before the start of the superplastic deformation. Due to dynamic recrystallization, a fine-grained structure was formed during deformation, which provided high-strain-rate superplasticity with elongation-to-failure above 500%. A higher strain rate provided a finer grain size for the alloy.
* The effective activation energy of the superplastic deformation suggested that the alloy deformation was controlled by the grain boundary self-diffusion of aluminum. The strain-rate-sensitivity coefficient \(m\) values suggested that the grain boundary sliding mechanism was predominant and its contribution increased with increasing strain in the studied strain range.
* The Arrhenius-based model and the three-layer artificial neural network were developed. The hidden layer of the used artificial neural network contained 20 neutrons that exhibited a minimum error level. In fitting and approximating the hot deforma  
Figure 14: Correlation between the experimental and predicted flow stress obtained by (**a,b**) ACE and (**c,d**) ANN models at (**a,c**) 0.002–0.01 s\({}^{-1}\) and (**b,d**) 0.03–0.1 s\({}^{-1}\).  
tion behavior of the investigated alloy, the two constructed models demonstrated good performance. For the unmodeled deformation conditions, the cross-validation approach verified good predictability of the established models. However, relative to the artificial neural network model, the Arrhenius-based model demonstrated better predictability with a lower error level.  
Conceptualization, A.V.M.; methodology, A.A.K. and A.D.K.; investigation, A.A.K. and O.V.R.; modeling experiments, A.O.M.; writing--original draft preparation, A.O.M.; writing--review and editing, A.V.M.; supervision, A.V.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Russian Science Foundation, RF [Grant Number 17-79-20426].  
Not applicable.  
The raw and processed data required to reproduce these results are available by contacting the authors.  
The study was funded by Russian Science Foundation, RF [Grant Number 17-79-20426]. The authors gratefully acknowledge [PERSON] for her helpful recommendations.  
The authors declare no conflict of interest.
## 2 Related Work
[23] introduced a vehicle sensor data processing method by integrating IMU with the onboard sensor to estimate the yaw misalignment of the IMU in the automobile without the assistance of any external information. [PERSON] et al. [24] presented a novel sensor fusion method that utilized the visual-aided strategy to complement the sensor information based on the lane line data obtained from an onboard camera in intelligent vehicles. [PERSON] et al. [25] illustrated a novel sensor processing method by fusing the information of a global navigation satellite system (GNSS) with IMU in automated vehicles. [PERSON] et al. [26] proposed a GNSS and IMU fusion-based method, which overcame the measurement signal delay and addressed errors induced by the low sampling rate of GNSS. [PERSON] et al. [27] proposed a sensor fusion framework based on vehicle chassis sensors and GNSS. [PERSON] et al. [28] introduced a novel sensor fusion strategy in the autonomous vehicle field by integrating the vehicle dynamic model with GNSS-IMU fusion-based methods. In addition, there are some works that have focused on potential insecurity scenarios identified and recovered by sensor fusion, which greatly enhances the vehicle security, especially due to the recoverability of hazards. For instance, [PERSON] et al. [29] proposed a novel advanced emergency braking system (EBS) using sensor fusion that has the capability of autonomously detecting insecurity driving states and subsequently triggering the vehicle's braking system to prevent or alleviate the collision. [PERSON] et al. [30] also presented an active fault-tolerant control (AFTC) approach for autonomous vehicles using a fuzzy neural network. The AFTC can effectively detect any abnormality in wheel speed, thereby preventing potential instability problems that may arise in CAVs. Relative to the works mentioned above, we also used the data content of CAN messages to develop our anomaly detection model for conquering various sophisticated attacks. Different from the other models, we utilized CWT to obtain more complete time and frequency domain information from the data content of in-vehicle messages, and our proposed model aims to take full advantage of the advanced classifier to obtain a better performance of anomaly detection. In addition, we considered that the raw in-vehicle sensor signals contain plentiful noise and have nonstationary characteristics, which increase the difficulty for the deep learning model to extract the more abstract features, causing errors in the anomaly detection performance [9]; in this study, we proposed a data preprocessing method that converts the raw vehicle sensor data into a two-dimensional CWTS and then use the constructed CWTS as the input of our deep learning model rather than using the raw sensor signals, which we performed to inherit the advantages of deep learning technologies for a better detection performance.
## 2 Related Works
### Virtual Topology Satellite Routing  
This type of routing algorithm solves the dynamic topology problem of the constellation by discretizing the satellite network structure in time. In the virtual topology routing algorithm [33], the network topology at the beginning of each time interval is taken as the constant static topology during this period. By applying the shortest path algorithm on the ground, the forwarding table for each satellite is calculated. This strategy has low complexity. However, it is not ideal for unexpected situations in the network, such as link failures and cache congestion, which cannot be responded to and handled in a timely manner. It is suitable for satellite networks with high stability and a uniform load flow in space segment satellite loads and inter-satellite links [34]. [PERSON] et al. [16] proposed the integration of the DFS and the Dijkstra algorithms into an SDN and virtual topology. The DFS algorithm is utilized to identify specialized nodes, while the Dijkstra algorithm is employed to handle the maximum traffic load.  
Routing techniques based on virtual nodes have become one of the most popular research areas in satellite networks. By mapping actual satellite nodes onto static virtual nodes, such techniques shield the effects of the dynamic characteristics of satellite networks on routing. However, this approach might also lead to the congestion of links in high-latitude regions and increased queuing delays. To address this issue, [PERSON] et al. [35] proposed a distributed probabilistic congestion control method. It utilized periodic link state exchanges between satellites to obtain the real-time congestion information of adjacent nodes. Based on the congestion status, it probabilistically selected the next hop to balance the load across links in all directions. However, the link state information exchange in this method could experience delays, resulting in the outdated congestion status acquisition of nodes, which would reduce the accuracy of routing decisions.