esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Model
#### 2.2.1 Word Embedding Layer
Positional vectors, on the other hand, are utilized to distinguish the semantic information differences carried by characters at different positions. When handling English vocabulary, BERT employs the WordPiece method, which involves a finer granularity of tokenization to better capture the semantic units. For Chinese, the current BERT models do not tokenize the input text but treat individual characters directly as the basic units forming the text. These design choices and strategies collectively form the foundation of BERT's deep understanding of natural language. Roberta adopts a Transformer architecture, similar to BERT, but with adjustments to the details. One of the most notable changes is the elimination of sentence-level segment embeddings, opting for a simpler approach without gaps. This modification enables [PERSON] to better capture the long-distance dependency relationships in the text, enhancing the model's expressive capacity. In terms of the training data, a larger dataset is utilized by [PERSON] compared to BERT, providing the model with more extensive and richer language knowledge. By learning from a greater variety of language phenomena, [PERSON] demonstrates superior performance in downstream tasks. Regarding sequence length handling, Roberta allows for the processing of longer sequences, which is crucial for tasks that involve more contextual information in a single input. The ability to handle longer sequences contributes to enhancing the model's understanding of complex contexts. Furthermore, [PERSON] introduces a dynamic masking approach, enhancing the model's generalization by dynamically selecting masked words in each iteration. This flexible masking strategy aids the model in better adapting to various textual inputs. In summary, these advantages enable [PERSON] to achieve superior performance across various natural language processing tasks, providing robust support for the model research in this paper. In the original [PERSON] and [PERSON] models, as they were trained on English language corpora, they adopted the \"WordPiece\" tokenization method for tokenization. This method involves a finer level of tokenization than words. For instance, the word \"emotion\" is tokenized into three tokens: \"e\", \"#mo\", \"##tion\". This tokenization method effectively reduces the size of the pretraining vocabulary (represented as |V|). It mitigates the issue of out-of-vocabulary words by combining word roots, allowing for a more effective representation of a broader vocabulary. However, for Chinese corpora, the concept of word roots, as seen in English, does not exist in the same manner. To address this issue, the model introduces a technique called \"Whole Word Masking\" (wwm) [19]. Taking the word \"emotion\" as an example, using the whole word masking method, it is tokenized into \"e\", \"##mo\", and \"##tion\". Then, all three tokens are masked. The handling of Chinese  
Figure 3: Input of BERT. tokenization issues is improved by this approach, enhancing the model's performance on Chinese tasks.
## 4 Results and Discussion
### Traffic CO Prediction at Different Times of Day  
Regression equations were created, based on the results from the CFS-MLP model; the coefficients of vehicular emissions predictors are calculated to formulate regression models to easily predict vehicular emission in the study area using a set of predictors that can be gathered from the field and existing databases in order to facilitate connection with the GIS-based model by applying parameter coefficients in the spatial model. The decision makers can notice the effect of causative parameters on vehicular emissions occurrence, which was assessed by the corresponding coefficient that appears in the regression function [47]. Regression equations were simulated at different times during the day (morning, afternoon, evening and night) to predict traffic CO at these times. The highest RMSE was 2.9817 ppm during evening observations, while the lowest RMSE was 0.387 ppm at night. Table 6 shows the results of the regression analysis at different times.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 5 Evaluations
### Comparisons with Existing Works  
As depicted in Figure 5, our method effectively removes haze from the image while preserving the integrity of the sky region. Furthermore, as demonstrated in Table 1, our proposed approach outperforms the second-highest scorer, the method of [PERSON] [3], by approximately 3.03 in DHQI.  
A comparison between our method and that of [PERSON] et al. [4] reveals significant disparities. [PERSON] et al.'s approach leads to color distortion within the sky region and excessive removal of haze around objects, characterized by visible white bands, as depicted in Figure 6. In contrast, our method exhibits no such distortions in the sky and avoids the formation of band-like haze around objects, as detailed in Table 1.  
While deep-learning-based approaches have shown success in various applications, including image dehazing [11] and restoration [12], they do not demonstrate a clear advantage in realistic hazy image datasets such as the RTTS and image dehazing metric DHQI compared to our method. Deep-learning-based methods often rely on the training dataset, leading to a performance influenced by the data-driven approach.  
Figure 4: The images illustrate the transmission map and result in images corresponding to variations in \(\gamma\). With an increase in \(\gamma\), structures become clearer, yet overly high values lead to excessive darkness. With a decrease in \(\gamma\), the persistence of haze is evident.  
In contrast, our proposed method directly performs adaptive dehazing from the original image, yielding consistent results under various conditions. This method also possesses the capability to analyze the image's state in real time and execute optimal dehazing without depending on specific training data.
###### Abstract
Additionally, longitudinal data may be censored, meaning measurements below a certain threshold cannot be accurately quantified. For viral load data, the detection limit is generally between 50 copies/mL and 400 copies/mL. A linear model based on the observed data might not be suitable for the unobserved data. Alternatively, if a mechanical or scientific model is available for the longitudinal data, it can provide better predictions for the unobserved data and improve change point estimation. Such a mechanical model often takes a nonlinear form. Accounting for a random change point within a likelihood framework poses a major challenge due to the lack of closed-form expressions [7; 8; 9]. This challenge is compounded by the non-Gaussian distribution of the change point, nonlinear models, and data censoring. In this paper, we propose a zero-inflated exponential (ZIE) distribution-based random change point model with segmented NLME sub-models to analyze left-censored data. To facilitate full likelihood-based inference, we extend the Stochastic Expectation-Maximization (StEM) algorithm, which was initially introduced by [10]. Our extension of the StEM involves a Gibbs sampler coupled with Metropolis-Hasting sampling for mixed-type random effects structure in the random change point model. The remainder of this paper is structured as follows. Section 2 introduces the ZIE-based nonlinear random change point model. Section 3 presents the general model and details of the extended StEM algorithm. Section 4 analyzes an HIV cohort dataset, and Section 5 evaluates the proposed method through simulations. Finally, Section 6 concludes the article with a discussion.
# Power Modeling and Experiment Study of Large Flapping-Wing Flying Robot during Forward Flight
###### Abstract
[18] presented a flapping-wing design for Mars surface research inspired by bumblebees and constructed an efficient power aerodynamic model to investigate the vehicle's flying efficiency and power on Mars. A vortex ring aerodynamic model [19] was developed by [PERSON] to forecast the power required for flapping-wing flying as a function of air velocity. Flapping-wing air vehicles can be particularly beneficial in disaster relief and environmental detection circumstances that require long-term monitoring. One challenge of developing FWAVs is to determine how to achieve a longer endurance. To more effectively design FWAVs with long endurance flight capability, it is necessary to establish a relationship between the geometrical parameters, design parameters, and power consumption of robots. Thus, inspired by the large scale of birds, this work investigates the power consumption of a large flapping-wing fly robot in forward flight, which serves as the foundation for developing a bionic flying robot with longer endurance. However, most investigations on the power consumption of flapping flight have been conducted on birds in nature. The power of the flapping flight during horizontal forward flight is modeled and experimentally confirmed in this work to map bird features to the flapping flight.
## 6 Discussion  
Many studies have used clustering techniques to analyze users' sentiments, whether they are positive or negative [64; 65; 66; 67]. However, to the best of our knowledge, there is no Arabic study addressing this method, which makes this work innovative. Although the clustering results were intriguing, they did not achieve their intended function of classifying reviews as negative or positive. This may be due to the difficulty of the Arabic language, containing many dialects and grammatical rules, which makes the analysis process difficult compared to other languages. Likewise, the effect of using the type of lemmatization or stemming may have an impact, as some studies have proven its effects on the model result [68; 69]. Nevertheless, this study succeeded in extracting the most important topics that affect users' experiences in the Mrsool and Careem super applications, supported by the level of user satisfaction or dissatisfaction for every extracted topic. The results of this study show that such applications face some difficulties and obstacles in satisfying their users. Users believe that these apps' delivery and payment, customer support and updates, and prices are unsatisfactory. Therefore, we recommend that developers constantly test updates before releasing them to avoid errors. Customer service must respond to customer inquiries and complaints as quickly as possible. Likewise, prices must remain constant and appropriate for the services provided.
# A Robust Conformal Framework for IoT-Based Predictive Maintenance
## 7 Discussion  
The extensive experimentation described in Section 6 illustrates the practical value of the Robust Conformal Prognostic framework when applied to the CMAPSS dataset. Models such as CNN-GRU and BiLSTM consistently demonstrate superior performance, particularly in the FD4-FD2 datasets (see Section 4), suggesting that their capacity to capture both temporal dependencies and localized signal variations is instrumental in handling complex operational regimes. However, NBEATS' present performance plateaus at a higher error level, capturing abrupt shifts in sensor readings may require additional architectural modifications or domain-specific regularizations, especially in more complex scenarios (FD2-FD4). In particular, the weighted margin approach, discussed in Section 3.2, demonstrates the most stable coverage performance across a range of scenarios; in fact, by emphasizing recent residuals, it adeptly adjusts interval widths whenever sensor data exhibit sudden changes, presenting sufficiently tight CI while also remaining flexible enough to encompass unexpected degradation trajectories. Generally, By coupling deep-learning prognostics with distribution-free, clamp-regulated Conformal Prediction, the proposed framework does more than improve accuracy; it operationalizes machine trustworthiness  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Metric** & **FD1** & **FD2** & **FD3** & **FD4** \\ \hline \hline Model Metrics & & & & \\ \hline Model & NBEATS & CNN-GRU & NBEATS & BiLSTM \\ MSE & 347.67 & 300.32 & 226.29 & 505.239090 \\ MAE & 14.34 & 13.06 & 10.610778 & 17.281365 \\ RMSE & 18.64 & 17.73 & 15.04 & 22.477524 \\ R\({}^{2}\) & 0.86 & 0.82 & 0.911 & 0.800323 \\ S-score & 24069 & 75307 & 22870 & 273456 \\ \hline \hline \multicolumn{5}{l}{Conformal Metrics} \\ \hline Method & Weighted & Bootstrap & Weighted & Weighted \\ Coverage & 0.505 & 0.513 & 0.423 & 0.488 \\ Margin & 40.171 & 41.63 & 36.06 & 64.52 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Combined Metrics Evaluation of CMAPSS datasets: predictive and UQ algorithms.  
in streaming environments. The resulting pair--RUL point estimate and validated lower-bound--constitutes an intelligence signal upstream that IoT platforms can readily consume. In a fully AI-enabled stack, this signal becomes a catalyst for closed-loop autonomy: edge gateways can down- or up-sample sensors according to risk, cloud-based optimizers can resequence production or logistics, and digital-twin layers can run \"what-if\" scenarios driven by calibrated failure horizons. Because the interval is guaranteed valid irrespective of data drift or regime shifts, it provides a robust decision substrate across heterogeneous devices, locations, and update cycles, extending its value beyond the single-asset case.
## 5 Analysis and Discussion  
When light is scattered from the targets, its amplitude is changed. These changes depend on the sample material and shape, as well as the illumination polarization. As mentioned in Section 3.1, the structural features of nano-pit, nano-dot, and nano-bump were centrosymmetric. According to the analysis in Section 2, the function of their effective refractive index is also centrosymmetric, that is, in'the polar coordinate  
\[\mathrm{n}(\rho,\theta)=\mathrm{n}(\rho).\]  
Therefore, the effective refractive indices of these three targets are constant with respect to the two illumination polarizations. For the silicon or Au lines, the function of the equivalent refractive index along the horizontal (\(X\)) axis is inconsistent but periodic. From the simulation and experimental results, the TSOM image and its OIR of the centrosymmetric targets is similar regardless of the different illumination polarizations, but an apparent difference can be observed for center asymmetric targets, which supports our hypothesis.  
From the data of the last three rows of Table 2 and the data of Table 3 (double silicon lines and dense line array), the relative differences between OIRs of TSOM and DTSOM increase as the number of lines increases or the pitch of the array decreases. This phenomenon can be explained through the effect of form birefringence, which leads to two effective refractive indices for two orthogonal polarizations. The form birefringence strongly depends on the sample structure even if the sample material is non-birefringent. As the number of lines increases or the pitch of the array decreases, the scattered light intensity of the perpendicular polarization varies more significantly across the line and space than that of the parallel polarization, which is eventually illustrated through the relative difference between OIRs with the two illumination polarizations.
###### Abstract  
The scarcity of labeled training samples poses a significant challenge in hyperspectral image classification. Cross-scene classification has been shown to be an effective approach to tackle the problem of limited sample learning. This paper investigates the usage of generative adversarial networks (GANs) to enable collaborative artificial intelligence learning on hyperspectral datasets. We propose and design a specialized architecture, termed Hyper-CycleGAN, for heterogeneous transfer learning across source and target scenes. This architecture enables the establishment of bidirectional mappings through efficient adversarial training and merges both source-to-target and target-to-source generators. The proposed Hyper-CycleGAN architecture harnesses the strengths of GANs, along with custom modifications like the integration of multi-scale attention mechanisms to enhance feature learning capabilities specifically tailored for hyperspectral data. To address training instability, the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) loss discriminator is utilized. Additionally, a label smoothing technique is introduced to enhance the generalization capability of the generator, particularly in handling unlabeled samples, thus improving model robustness. Experimental results are performed to validate and confirm the effectiveness of the cross-domain Hyper-CycleGAN approach by demonstrating its applicability to two real-world cross-scene hyperspectral image datasets. Addressing the challenge of limited labeled samples in hyperspectral image classification, this research makes significant contributions and gives valuable insights for remote sensing, environmental monitoring, and medical imaging applications.  
hyperspectral data; data analytics; adversarial neural network; generative adversarial network +
Footnote †: journal: Applied sciences
# Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
## 2 System Model
### Channel Model  
A given with the LS-MIMO model, it can be easily expected that there would be some correlation at the TX side but little correlation at the RX side, since the TX side has a large number of TX antennas in a limited space, while the RX side has one antenna in each. The channel can be modeled as follows [10; 11]:  
\[\mathbf{H} = [\mathbf{g}_{1}^{T},\ \cdots,\ \mathbf{g}_{k}^{T},\ \cdots,\ \mathbf{g}_{k}^{T}]^{T}\circ[\mathbf{A}_{1}^{T},\ \cdots,\ \mathbf{A}_{k}^{T},\ \cdots,\ \mathbf{A}_{K}^{T}]^{T}, \tag{2}\] \[= [\mathbf{h}_{1}^{T},\ \cdots,\ \mathbf{h}_{k}^{T},\ \cdots,\ \mathbf{h}_{K}^{T}]^{T}, \tag{3}\]  
where \(\mathbf{g}_{k}\) is the \(1\times D_{k}\) complex Gaussian vector with mean zero and unit variance (\(\mathbf{g}_{k}\sim CN(0,\ \mathbf{I}_{D_{k}})\)), \(D_{k}\) is the the number of resolvable directional path from a transmitter antenna to a RX antenna, \(\mathbf{A}_{k}\in\mathbb{C}^{D_{k}\times N_{t}}\) is the transmit steering matrix containing \(D_{k}\) steering vectors of the transmit antenna array, and \(\circ\) is the Hadamard product operation. Then, \(1\times N_{t}\) channel vector for k-th single antenna user can be expressed as  
\[\mathbf{h}_{k}=\mathbf{g}_{k}\mathbf{A}_{k}. \tag{4}\]  
We simply disregard the mutual coupling since it is beyond the scope of this paper. Assuming a uniform linear array at the TX, the steering matrix for the \(k^{th}\) UE can be expressed as follows:  
\[\mathbf{A}_{k}=\frac{1}{\sqrt{D_{k}}}\left[\mathbf{a}^{T}(\theta_{k,1}),\ \cdots,\ \mathbf{a}^{T}(\theta_{k,D_{k}})\ \right]^{T}, \tag{5}\]  
where \(\mathbf{a}^{T}(\theta_{k,i})\) is given as  
\[\mathbf{a}(\theta_{k,i})=\left[1,\ e^{j2\pi d\sin\theta_{k,i}},\cdots,\ e^{j2 \pi(N_{t}-1)d\sin\theta_{k,i}}\right], \tag{6}\]  
where \(d\) is the normalized antenna spacing by wavelength. By adjusting the angular spread and the antenna spacing, we can make various channel conditions. In this paper, we assume little angular spread and \(0.5\lambda\) antenna spacing.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 2 Influencing Factors of City Gas Daily Load
### Solar Radiation  
Solar radiation is another major cause of daily gas load variation. After sorting out the solar radiation data of a city for one consecutive year, the yearly change trend of daily gas load and solar radiation is shown in Figure 2. The analysis shows that when the solar radiation was high, the daily load of gas was small, and when the solar radiation was low, the daily load was at a high level. It shows that the variation of solar radiation will affect the fluctuation of daily gas load, so it is necessary to analyze the correlation between solar radiation and daily load.  
Figure 1: Gas daily load and average temperature curve.  
Figure 2: Gas daily load and solar radiation curve.
## 5 Results and Discussion
### Performance Units
#### 5.2.8 Packet Inter-Reception Time (PIRT)  
From the results obtained and shown in Figure 13, it is noticed that PIRT increases for all of the protocols with increasing node density. Additionally, PIRT for MAMC-MAC is less when compared with other protocols. This improvement is because of the adoption of  
Figure 11: Reliability comparison.  
Figure 12: Message travel time comparison.  
mobility-based clustering scenarios that the proposed protocol accomplishes well under dense vehicular environment.
## 1 Introduction  
### Background  
Ballast tamping using heavy machines is the most important and effective maintenance operation used by railway track managers in various countries to restore the geometry conditions of ballasted tracks, but it is expensive [1]. The traditional preventive maintenance strategy based on fixed time intervals can lead to \"over-maintenance\" and \"under-maintenance\", which not only affect the safety and availability of railway tracks but also increase maintenance costs [2]. The pressure on maintenance operations also continues to increase with railway network expansion. Taking China's railways as an example, from 2011 to 2021, the annual trapping kilometers increased from 103,138 km to 190,890 km, accounting for nearly 80% of the total extension length of the ballasted track [3]. Therefore, to ensure safety and reasonably control maintenance costs, the maintenance strategy for railway infrastructures in many countries has gradually shifted to condition-based, proactive maintenance [4]. Furthermore, a tamping operation requires heavy machines and dozens of workers. Therefore, it is necessary to plan tamping maintenance operations in advance because of limited maintenance resources and budgets [5]. To perform reasonable planning of track tamping operations under a condition-based maintenance strategy, it is necessary to estimate the tamping maintenance cycle (i.e., the time interval between two adjacent tamping interventions) based on the deterioration of the track geometry condition to predict the tamping maintenance needs.
# A Perspective on Terahertz Next-Generation Wireless Communications
###### Abstract  
In the past year, fifth-generation (5G) wireless technology has seen dramatic growth, spurred on by the continuing demand for faster data communications with lower latency. At the same time, many researchers argue that 5G will be inadequate in a short time, given the explosive growth of machine connectivity, such as the Internet-of-Things (IoT). This has prompted many to question what comes after 5G. The obvious answer is sixth-generation (6G), however, the substance of 6G is still very much undefined, leaving much to the imagination in terms of real-world implementation. What is clear, however, is that the next generation will likely involve the use of terahertz frequency (0.1-10 THz) electromagnetic waves. Here, we review recent research in terahertz wireless communications and technology, focusing on three broad topic classes: the terahertz channel, terahertz devices, and space-based terahertz system considerations. In all of these, we describe the nature of the research, the specific challenges involved, and current research findings. We conclude by providing a brief perspective on the path forward.  
t pacs: 2019; 7.43; doi:10.3390/technologies7020043