esa-sceva/satcom-chunk-collection huggingface dataset:
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 4 Experiments and Results
### Training Word-Embedding Vectors  
In this paper, we used 300 dimensions of pre-trained word-embedding vectors [35] from Google News Dataset. This comprised of around 100 billion words, which provided three million word-embedding vectors that promise 77.1% accuracy for 20,000 word analogy test examples [36]. This test follows an \"A is to B as C is to D\" scheme. For example, \"Bangkok is to Thailand as Beijing is to China,\" which can  
Figure 2: Some examples of wsdw2v captured from Jupyter notebook which disambiguated “bank” and “plant” in different contexts.  
be tested by v(\"Bangkok\") + v(\"China\") - v(\"Thailand\") \(\approx\) v(\"Beijing\"). Unfortunately, pre-trained word-embedding vectors from Google did not provide their dataset. We could not compute the inverse document frequency. Therefore, we decided to train our word-embedding vectors from the UMBC (University of Maryland, Baltimore County) WebBase Corpus [37; 38], containing over three billion words which provided around five million word-embedding vectors. The training was done using Gensim with the same parameters as Google word-embedding vectors, which achieved 69% accuracy on word analogy test examples. UMBC word-embedding vectors were used to evaluate three configurations of constructing a sentence vector. Training time for UMBC word-embedding vectors was 460 min and using overall memory 10,538 MB. We also trained an LSA model from the UMBC WebBase Corpus about four million documents with 100,000 most shared features and reduced dimensions to 200. We used this LSA model to compare with our method, wsdw2v. Training time for the LSA model was 174 min and using overall memory 101,220 MB. Notice that the LSA model took much more memory than the Word2 vec model. In order to train a model from a large corpus, physical memory may be not sufficient to hold large data structures. Thus, many operating systems provide a memory management technique called virtual memory that moves some data from physical memory to a paging file on disk storages. We used this virtual memory technique in case of training the LSA model because overall memory could not fit with our physical memory (a RAM of 16 GB).
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 3 Methodology
### Squeeze-and-Excitation (SE) Attention Mechanism  
The SE attention mechanism explicitly recalibrates the importance of each feature channel dynamically, thus enabling the network to enhance focus on the most informative features while suppressing less informative ones [15]. The detailed implementation of the SE mechanism is presented in Algorithm 1. The SE block achieves channel-wise attention through two critical operations:  
```
1:Input: Feature map \(X\in\mathbb{R}^{C\times H\times W}\)
2:Output: Attention-weighted feature map \(\widehat{X}\)
3:\(z_{c}\leftarrow\frac{1}{H\cdot W}\sum_{i=1}^{H}\sum_{j=1}^{W}X_{c}(i,j)\)\(\triangleright\) Global Average Pooling (GAP)
4:\(z\leftarrow[z_{1},z_{2},...,z_{c}]^{\top}\)\(\triangleright\) Channel descriptor vector
5:\(s\leftarrow\sigma(W_{2}\cdot\delta(W_{1}\cdot z))\)\(\triangleright\) Two FC layers with ReLU and sigmoid activation
6:\(\widehat{X}_{c}\gets s_{c}\cdot X_{c}\) for all \(c=1,2,...,C\)\(\triangleright\) Element-wise channel scaling
7:return\(\widehat{X}\)
```  
**Algorithm 1** Forward Pass of SELayer.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Learning Rate** & **Batch Size** & **Epochs** \\ \hline ResNet-50 & 0.0001 & 32 & 10 \\ VGGNet-16 & 0.0001 & 32 & 10 \\ Hybrid (SE-ResNet50) & 0.0001 & 32 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training hyperparameters for baseline and hybrid models.  
Legend:
* \(X\): Input feature map of shape \(C\times H\times W\).
* C: Number of channels.
* \(H,W\): Height and width of the feature map.
* \(z_{c}\): Average-pooled scalar for channel \(c\).
* \(z\): Channel descriptor vector (\(\mathbb{R}^{C\times 1}\)).
* \(W_{1},W_{2}\): Fully connected (FC) layer weights.
* \(\delta\): ReLU activation function.
* \(\sigma\): Sigmoid activation function.
* \(s_{c}\): Channel-wise attention weight.
* \(\widehat{X}_{c}\): Attention-weighted feature map for channel \(c\).
* \(\widehat{X}\): Output feature map after SE attention.
# HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
## 7 Potential Real-World Applications
### Cognitive Radio Networks  
The proposed modulation classification model can improve dynamic spectral access, enabling the efficient use of available frequencies [49]. As noted by [PERSON] et al., in [65], AMR methods significantly improve the spectral utilization efficiency by allowing for cognitive radios to share licensed bands between licensed and unlicensed users. Moreover, the work of [PERSON] et al. [66] highlights the importance of robust AMC methods in cognitive radio networks, which are essential for meeting the spectral demands of emerging technologies.
## 4 Case Study
### Simulation Cases  
Faults in time-series data are categorized into point, contextual, and collective anomalies [23]. The point anomaly is a temporary fault that deviates significantly from a normal time-series distribution. Contextual and collective anomalies are continuous faults that occur over a certain period within a normal time-series distribution. This study divided simulation cases according to the type of fault to simulate situations that may occur in the SCWT sensor, as listed in Table 4. In the point-offset cases (Cases A-1 and A-2), faults in the SCWT sensor occurred temporarily once a week for one hour. In the fixed-offset cases (Cases B-1 to B-4), faults with a constant offset occurred continuously for a certain period. In the random-offset case (Case C), faults with a random offset occurred for a certain period. In these cases, a random offset was created using the random number generation function in Excel. In the drift-offset cases (Cases D-1 and D-2), faults with offset gradually worsened over time. Fixed, random, and drift offsets occurred on 16 August and continued until  
Figure 6: Schematics of the simulation model: (**a**) envelope of building; and (**b**) simplified cooling system.  
31 August. In the fixed- and random-offset cases, the offset at the previous time did not affect the offset at the current time. Therefore, fault detection was performed during the operating hours of the chiller, from 7 AM to 6 PM. However, fault detection was performed continuously in the drift-offset cases because the drift offset at the current time was affected by the offset at the previous time.
## 2 Materials and Methods
### Reference Data  
The basic cartographic maps used to detect changes in the provision of ES in Italy are the national land cover map and the national land consumption map. These maps are the common data source for all the ES analyzed in this study.  
The national land cover map is based on the integration of the Copernicus Land Monitoring Services (CLMS) datasets (namely the Corine Land Cover, High Resolution Layers, Urban Atlas, Riparian Zones, and Natura 2000 datasets). The datasets show differences in the classification systems, resolutions, and updating frequencies adopted. Therefore, a harmonization procedure had to be adopted in order to make the datasets comparable. [PERSON] et al. [45] followed the EIONET EAGLE (Action Group on Land monitoring in Europe) group framework, which provides a methodology for describing land cover and land use data in a consistent manner. The CLMS data were integrated in an EAGLE compliant land cover map for the year 2012 (10 m resolution), which includes 16 classes of land cover (Figure 1).  
The national land consumption map is, on the other hand, a product generated by the processing of Sentinel-1 (based on the SAR backscatter of VH polarization threshold) and Sentinel-2 (based on Normalized Difference Vegetation Index extraction and threshold value) satellite images, and photointerpretation of national orthophotos. The land consumption map is updated every year with a spatial resolution of 10 m [6,44]. National land consumption maps for the years 2012 and 2020 (Figure 2) were used for the purpose of assessing ES changes.  
Land consumption is mapped both in its permanent and temporary forms following the definition given by [6,7]. By permanent consumed land, artificial surfaces composed of impervious material (e.g., concrete, asphalt or tarmacdam), both 3D (buildings) and 2D (roads), were taken into consideration (6.6% of the Italian national territory). This component is assumed to be irreversible, since extremely long amounts of time are required for restoring soil to its the natural state. Reversible consumed land is the result of compaction in construction sites, excavations in quarries, temporary coverage with artificial surfaces and other forms of soil alteration that do not imply permanent consumption, but that adversely affect the soil's natural state. However, this component is a minor (albeit dynamic) part of the total consumed land (0.5% of the Italian national territory). In light of the limited extent thereof and taking into account that for some portions of Italy's territory the distinction between permanent and temporary forms of land consumption is lacking, both components are considered as consumed land, without any exception whatsoever (Figure 2). Figure 3 shows some examples of land consumption in urban, agricultural, and natural environments in the period under investigation.
###### Abstract  
In this review article, a comprehensive study is provided regarding the latest achievements in simulation techniques and platforms for fifth generation (5G) wireless cellular networks. In this context, the calculation of a set of diverse performance metrics, such as achievable throughput in uplink and downlink, the mean Bit Error Rate, the number of active users, outage probability, the handover rate, delay, latency, etc., can be a computationally demanding task due to the various parameters that should be incorporated in system and link level simulations. For example, potential solutions for 5G interfaces include, among others, millimeter Wave (mmWave) transmission, massive multiple input multiple output (MIMO) architectures and non-orthogonal multiple access (NOMA). Therefore, a more accurate and realistic representation of channel coefficients and overall interference is required compared to other cellular interfaces. In addition, the increased number of highly directional beams will unavoidably lead to increased signaling burden and handovers. Moreover, until a full transition to 5G networks takes place, coexistence with currently deployed fourth generation (4G) networks will be a challenging issue for radio network planning. Finally, the potential exploitation of 5G infrastructures in future electrical smart grids in order to support high bandwidth and zero latency applications (e.g., semi or full autonomous driving) dictates the need for the development of simulation environments able to incorporate the various and diverse aspects of 5G wireless cellular networks.  
5G; mmWave transmission; massive MIMO systems; system level simulators; link level simulators; radio network planning +
Footnote †: journal: Electronics
## 5 Conclusions  
In this study, a cylinder-in-ball valve, in which the opening hole can start at the center of the flow path, was proposed. To quantitatively evaluate the flow characteristics and the cavitation phenomenon, computational fluid dynamic analysis was carried out. The results were reviewed via comparisons with a V-port ball valve. In addition, an experimental \(CV\) measurement was performed, and the results were compared with those obtained from numerical analysis for the proposed cylinder-in-ball valve. The results are as follows.  
1. Through a structural conceptual design, a cylinder-in-ball valve was proposed, in which the ball and cylinder were rotated in opposite directions using a combination of gears. This created a coaxial counter-rotating motion of the ball and cylinder, and the valve opening started at the center of the flow path.
2. The estimates for the \(CV\) of the proposed cylinder-in-ball valve ranged from 1.05 to 109.87 as the opening rate changed from 20 to 100%, and the change in the \(CV\) for the opening rates followed an even distribution of exponential increments and provided an equal-percentage of inherent flow characteristics for a flow-control valve.
3. The proposed cylinder-in-ball valve provided an insensitive \(CV\) change at the range of opening rates between 40 and 80% compared with that of the V-port ball valve, so the proposed cylinder-in-ball valve could be considered to offer a more sophisticated level of flow control.
4. The experimentally obtained \(CV\) values for the fully opened proposed cylinder-in-ball valve showed good agreement with estimates via the CFD analysis.
5. Multi-phase CFD analysis based on the Rayleigh-Plesset equation provided no meaningful \(VVF\) for the proposed cylinder-in-ball valve at an opening rate of 20%. Therefore, cavitation did not occur in the proposed cylinder-in-ball valve even at a small opening rate, and the irregular flow characteristics were avoided.  
All authors contributed to this work by collaboration. Conceptualization, H.-L.K. and S.-H.H.; software and experiment, H.-L.K.; validation, H.-J.P., H.-L.K. and S.-H.H.; formal analysis, H.-J.P., H.-L.K.; writing--original draft preparation, H.-L.K.; writing--review and editing, S.-H.H.; visualization, H.-J.P.; supervision, S.-H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education of the Republic of Korea (2021R1I1A3042151).  
Not applicable.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) (2021R1I1A3042151).  
The authors declare no conflict of interest.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
###### Abstract  
The integration of Artificial Intelligence into industrial maintenance remains challenging due to the scarcity of high-quality data representing faulty conditions. Machine Learning models trained on laboratory testbed data often fail to generalize effectively in real workshop environments. This study evaluated the effectiveness of Transfer Learning models in handling this domain shift challenge compared with Machine Learning models. Their potential to address the generalization gap was assessed by analyzing the model adaptability from lab-recorded data to data from emulated workshop conditions, where real-world variability was replicated by embedding synthetic noise into the lab-recorded data. The case study focuses on detecting rotor unbalance through bearing vibration signals at varying speeds. A Support Vector Classifier was trained on the transformed features for both models for binary classification. Model performance was assessed under varying data availability and noise conditions to evaluate the impact of these factors on classification accuracy, sensitivity, and specificity. The results show that Transfer Learning outperforms Machine Learning, achieving up to 30% higher accuracy under high-noise conditions. Although the Machine Learning model exhibits greater sensitivity, it misclassifies balanced cases and reduces specificity. In contrast, the Transfer Learning model maintains high specificity but has difficulty detecting mild unbalance levels, particularly when data availability is limited.  
Keywords:rotor unbalance detection; domain adaptation; bearing failure identification; model generalization; transfer component analysis; transfer learning
## 5 Discussion and Conclusions
### Practical Contributions  
LEO satellite operators/manufacturers could use this method during early phases of mission design to predict their own footprint during production phase of satellites based on the mission design. This could pave way for focusing on specific midpoint indicators they are interested in and change the mission design if they want to focus on implementing more sustainable practices during production phase. Apart from LEO manufacturers, any user of this method can predict the footprint of any LEO satellite provided orbital data filed by the commercial company/space agency, even without knowing the mass budget data required to perform LCA.
# High-Precision Ranging Method of 5G NR Co-Band PRS in Industrial Internet Scenarios
## 4 Simulation Results And Errors Analysis
### Simulation and Analysis
As a result, it enables a more accurate differentiation of the first arriving path of the positioning signals. To evaluate the performance of the iterative DLL, we inject some fractional timing into the OFDM symbols before modulation at the transmitter. The transmitted signal can given by  
\[X_{i,n}=X_{i,n}e^{-2\pi(n-1)\operatorname{Fra}\{e^{\prime}\}/N} \tag{26}\]  
where the fractional timing error is defined as  
\[\operatorname{Fra}\{\sigma\}=-0.3+0.08\sin\biggl{(}\frac{2\pi nT}{50N}\biggr{)}. \tag{27}\]  
For the AWGN-only channel, the experimental results show that the RMSE is \(3.42\times 10^{-3}\) samples and the standard deviation of the code phase samples is \(3.31\times 10^{-2}\) samples (Figure 9). For the Rice plus Gaussian noise channel, the results show that the code phase RMSE is \(1.81\times 10^{-2}\) samples, and the code phase sample standard deviation is  
Figure 7: Estimation of the channel impluse response by treating the communication signal as noise. (**a**) The positioning signal transmitting power is 40 dB lower than the communication signal transmitting power (\(\beta_{PRS}=0.01\)). (**b**) The positioning signal transmitting power is 16 dB lower than the communication signal transmitting power (\(\beta_{PRS}=0.15\)).
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 2 VNE Problem Decomposition and Modeling
### Problem Description
#### 2.1.1 Modeling of the SN and VNR  
As described by the majority of the VNE research community, the SN and the VN of a VNR can be modeled either as directed or undirected graphs. The physical network \(\text{SN}=(N_{s},L_{s})\) consists of the sets of substrate nodes and links and denoted by \(N_{s}\) and \(L_{s}\), respectively. In the same sense, the set \(\text{VN}^{i}=(N^{i},L^{i})\) contains \(i=1,\ldots,n\) VNRs. \(L^{i}\) and \(N^{i}\) are the sets of virtual links and virtual nodes belonging to \(\text{VN}^{i}\).
## 4 Discussion  
In this study, the mechanical and displacement characteristics of the mine workings under different stope sizes were effectively verified through numerical simulation, and the relationships between them were established. A second-order nonlinear model was used to construct the relationships between the structural parameters and the mechanical responses, which clearly shows the complex interactive effects of the stope span, pillar span, and roof pillar thickness in terms of compressive stress and tensile stress. These interactive effects indicate that the mechanical response is not only influenced by a single parameter, but by the combined effects of multiple parameters.  
Compared with traditional optimization methods, the Pareto SA algorithm can balance the weight of multiple parameters in multi-objective optimization problems and select the optimal mining structure parameters. The advantage of this method is that it can quickly find a set of non-dominated solutions, which can provide designers with diverse references in actual engineering. However, although the simulation results show strong relevance, the complexity and uncertainty of the model still need to be paid attention to. For example, in actual engineering, uncontrollable factors such as geological conditions and construction techniques may affect the accuracy of the model.  
Future studies can further improve the reliability of the model by introducing more environmental factors or carrying out experimental verification. In addition, the combination of other advanced optimization algorithms such as genetic algorithms or deep learning models may also further optimize stope design schemes.
# Performance Analysis of a New Non-Orthogonal Multiple Access Design for Mitigating Information Loss
## 3 Proposed Scheme  
### Proposed Transmitter Design  
The characteristic of NOMA is a high transmission rate, but it is susceptible to noise because two or more users' data are transmitted on one frequency or time resource block. To address this, our approach can reduce the impact of noise by not dividing the power of the user's actual data and adding redundancy data to help decode the data. Redundancy data can be obtained by performing an XOR bit operation on the data of two users.  
Figure 2 shows the block diagram of the proposed transmitter design. When \(b_{k}\) denotes the bit stream of user \(k\)'s data, the XOR logic bit operation value of \(b_{1}\) and \(b_{2}\) is defined as \(b_{\text{XOR}}\):  
\[b_{\text{XOR}}=b_{1}\oplus b_{2}. \tag{8}\]  
Next, the partial bits obtained by splitting \(b_{\text{XOR}}\) into \(N\) are defined as \(b_{\text{XOR},n}\), \(n\in\{1,2,\cdots,N\}\). Then, the parts are combined using the SC technique. The redundancy data are as follows:  
\[s_{\text{c}}=\sum_{n=1}^{N}\alpha_{n}s_{\text{c},n}, \tag{9}\]where \(\alpha\) denotes the power allocation coefficient, \(\sum_{n=1}^{N}\alpha_{n}=1\), and \(s_{\mathrm{c},n}\) denotes the modulated signal of \(b_{\mathrm{XOR},k}\). Finally, the transmitter transmits \(s_{2}\) and \(s_{\mathrm{c}}\) serially to the receiver.  
The reason for sending the signal \(s_{2}\) instead of \(s_{1}\) is to increase the system's user fairness. If \(s_{1}\) is sent, user 2 should obtain \(b_{\mathrm{XOR}}\) to decode \(b_{2}\). Since \(s_{\mathrm{c}}\) is a signal combined with SC, the required SNR to decode it is high. Therefore, assigning the task of converting \(s_{\mathrm{c}}\) to \(b_{\mathrm{XOR}}\) to user \(1\), who has a better channel condition, helps increase the overall system's bit error probability.
## 3. SOH Prediction Method Based on the PSO-GWO-LSSVM Model
### PSO-GWO Combined Optimization Algorithm  
#### 3.2.1 Particle Swarm Optimization Algorithm  
PSO algorithm seeks the optimal solution by iteratively adjusting the positions and velocities of particles within the search space. In PSO, the initial population is randomly generated within the search domain, with each solution represented as a \"particle\" [35]. Each particle within the search space is characterized by a position and velocity. During the optimization process, particles update their position and velocity based on two sources of information: their own best-known position (local best) and the best-known position found by the entire swarm (global best) [36]. The strength of PSO lies in its strong local search ability, although it is prone to getting stuck in local optima.  
All particles in the swarm update their positions in each iteration using the following equation:  
\[\left\{\begin{array}{l}u_{i}^{t+1}=u_{i}^{t}+f_{1}n_{1}(\mathit{pbest}_{i}- x_{i}^{t})+f_{2}n_{2}(\mathit{gbest}-x_{i}^{t})\\ x_{i}^{t+1}=x_{i}^{t}+u_{i}^{t+1}\end{array}\right. \tag{19}\]  
where \(u_{i}^{t+1}\) represents the velocity of particle i at iteration t, \(x_{i}^{t}\) denotes the position of particle i at iteration t, \(\mathit{pbest}_{i}\) is the individual best position of particle, \(\mathit{gbest}\) is the global best position, \(f_{1}\) and \(f_{2}\) are the learning factors, and \(n_{1}\) and \(n_{2}\) are random numbers.