esa-sceva/satcom-chunk-collection huggingface dataset:
# Progressive Multi-Scale Fusion Network for Light Field Super-Resolution
## 2 Related Work
### Light Field Super-Resolution  
Learning-based methods have greatly improved the performance of LFSR compared to traditional methods. [PERSON] et al. [22] proposed the LFCNN, which was the first application of deep learning to the LFSR. Inspired by the recurrent convolutional neural network, [PERSON] et al. [11] proposed the LFNet and stacked generalisation technique to synthesise the final SAIs. In this structure, only the structure of horizontal and vertical directional views was used to improve LFSR. Inspired by residual networks, [PERSON] et al. [12] proposed a residual network (resLF) to extract complementary details from four directions of auxiliary views. The sub-pixel information from auxiliary views was stacked to extract the internal geometric structure relations. In order to maximise the number of auxiliary views, [PERSON] et al. [13] proposed an all-to-one model (ATO) to generate SR LF images via combinatorial geometry embedding. In this structure, the complementary information of each SAIs can be used. Moreover, some effective methods directly calculate angular and spatial dimensions to extract cross-view information. [PERSON] et al. [14] proposed an LF-InterNet to extract and incorporate spatial and angular information. Furthermore, [PERSON] et al. [15] proposed a dense dual-attention network (DDAN) containing a view and channel attention to fuse hierarchical features and distillate valid information. The above methods do not fully deal with the disparity issue among different SAIs. [PERSON] et al. [16]proposed a deformable convolution to incorporate angular information due to disparities among LF images. To fully utilise all angular information, [PERSON] et al. [23] proposed a multiple epipolar geometry network (MEG-Net), which used multi-direction epipolar images to reconstruct all views images. With the development of Transformer, [PERSON] et al. [24] proposed a detail-preserving Transformer (DPT) to recover the details of LF images by leveraging gradient maps of light field to guide the sequence learning. However, the structures of these methods are all-to-all models, whose complementary information is not well utilised for further performance improvement.
## 7 Conclusions  
The results of the experimental studies conducted in the current research show that program vector representations that are based on first-order Markov chains constructed from ASTs are most suitable for practical applications when compared to higher-order Markov chain models. Simple embeddings described by Algorithms 2-4 have the potential to outperform complex neural network-based models and can be easily ported to other programming languages. Future research could cover the applicability of Markov-chain-based embeddings to the discovery of code sharing similar concepts in large software projects, with the aim to eliminate duplicate implementations of the same algorithm from the code base. Another possible application of Markov-chain-based embeddings might include the search of code fragments suitable for hardware acceleration based on the given database of examples using the KNN algorithm. Specialized processors such as the field programmable gate array (FPGA) [54; 55] can be configured to implement a domain-specific algorithm in hardware, offering significant performance benefits. As shown in Table 2, different algorithms can be used to solve the same task, and in real world scenarios it is often hard to obtain a large dataset containing problem-specific code solving different tasks. This means that an ideal classifier should be able to detect a task using only one example of every known algorithm that can solve the task. Markov chains are simple models that can be constructed for graphs other than AST (see Algorithm 2), so future work could cover quality assessment of embeddings that are based on Markov chains built for either control flow graphs or data flow graphs for either high-level or low-level programming languages. Regarding educational data mining, future research could focus on anomaly detection [56] in source codes of programs submitted by students [19].  
**Author Contributions:** Conceptualization, methodology L.A.D., P.N.S. and A.V.G.; software, resources, visualization, testing A.V.G.; investigation, formal analysis, L.A.D., P.N.S. and A.V.G.; validation, supervision, project administration L.A.D. and P.N.S.; writing--original draft preparation, A.V.G.; writing--review and editing, L.A.D. and P.N.S.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data analyzed in this study are openly available in Zenodo at [[https://doi.org/10.5281/zenodo.7799971](https://doi.org/10.5281/zenodo.7799971)]([https://doi.org/10.5281/zenodo.7799971](https://doi.org/10.5281/zenodo.7799971)) (accessed on 8 July 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
Identifying key process variables (KPVs) plays a crucial role in reducing production costs and enhancing product quality. The current work focused on investigating three primary process parameters in the context of micro-milling titanium grade 9. Although there is an abundance of literature on the application and investigation of titanium grade 5, there is a notable gap in the research regarding the optimization of micro-machining parameters for titanium grade 9. Given that titanium grade 9 exhibits advantages over grade 5 in certain applications, this research aimed to uncover the critical process parameters specific to this grade. Titanium grade 9 exhibited improved surface finish below the tool edge radius. The following are the conclusions based on the research findings.  
* The surface integrity, which describes the product quality, was found to be dependent on all of the selected machining variables as identified in the analysis of variance. The feed rate was recognized to be the most significant member, with a contribution ratio of 62.69%.
* Burr width and burr length were determined to be mainly dependent on the feed rate, with contribution ratios of 55.20% and 77.70%, respectively. The reason for this occurrence was the shift of cutting mechanism from ploughing to shearing as the feed rate was increased, efficiently removing material and leading to minimized burr formation.
* The collective contribution of cutting speed and feed rate, in the case of tool wear, was determined to be 87.20%, proving their relevance. Cutting speed determines the cutting zone temperature and thereby dictates the tool wear mechanisms, whereas feed rate controls the rate of heat dissipation, which is another vital aspect concerning tool wear.
* In comparison with initial experimentation (at 0.45 \(\upmu\)m/tooth fz, 25 m/min Vc, 60 \(\upmu\)m ap), a 3.4% improvement in burr width was recorded while machining at 0.65 \(\upmu\)m/tooth fz, 75 m/min Vc, and 30 \(\upmu\)m ap, as underlined in confirmatory tests.
* Similarly, a surface roughness of 32 \(\upmu\)m was acquired under specific machining conditions under the best confirmatory conditions of a feed rate of 0.25 \(\upmu\)m/tooth, a cutting velocity of 25 m/min, and an axial depth of cut set at 90 \(\upmu\)m, which was 8.5% less than initial best surface roughness value obtained at 0.45 \(\upmu\)m/tooth fz, 25 m/min m/min Vc, and 60 \(\upmu\)m ap.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 6 Experiments Simulation and Analysis
### Global Path Planning Experiment
The convergence curve shows that PAGWO converges quickly within 200 iterations and significantly outperforms other algorithms in terms of both convergence speed and stability. The other algorithms exhibit slower convergence speeds and higher final stabilization values, further indicating that PAGWO excels in global optimization. Figure 7: Path Planning Comparison of Different Algorithms in Simulation Map 2. (a) GA; (b) SCA; (c) PSO; (d) GWO; (e) PAGWO; (f) convergence curve comparison. In summary, across three simulation environments with varying obstacle distributions and map sizes, PAGWO outperforms other algorithms in terms of path length, number of turns, and other metrics. To verify the robustness of PAGWO, the proposed algorithm was run 30 times in three different environments and was compared with other algorithms. The evaluation metrics are path length, number of turns, and running time, as shown in Table 4. In the three simulation map environments, the PAGWO algorithm shows significant advantages in path optimization. In terms of path length, the average shortest path lengths for PAGWO in the three maps are 28.21m, 44.30m, and 46.82m, respectively, outperforming other algorithms. In terms of the number of turns, PAGWO also performs better. In map 3, which is the most complex in terms of obstacles, the average number of turns is reduced by 34.62%, 34.97%, 34.15%, and 38.53% compared to GWO, GA, SCA, and PSO, respectively. In terms of running time, PAGWO performs exceptionally well in maps 2 and 3, demonstrating its higher computational efficiency in complex environments. In summary, the PAGWO algorithm not only improves the global optimization performance of the paths but also achieves a good balance between path smoothness and computational efficiency, making it an effective method for solving path planning problems in complex environments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ENV. Model** & **Index** & **Algorithm** & **GA** & **SCA** & **PSO** & **GWO** & **PAGWO** \\ \hline \multirow{3}{*}{ENV. 1} & Path Length (m) & 30.65 & 30.82 & 30.75 & 31.13 & 28.21 \\  & Number of turns & 6.50 & 6.40 & 6.60 & 7.10 & 6.10 \\  & Running time (s) & 6.39 & 5.20 & 4.06 & 2.39 & 2.06 \\ \hline \multirow{3}{*}{ENV. 2} & Path Length (m) & 50.18 & 51.56 & 50.49 & 49.56 & 44.30 \\  & Number of turns & 14.90 & 14.73 & 15.83 & 15.30 & 7.53 \\ \cline{1-1}  & Running time (s) & 14.96 & 3.10 & 6.69 & 4.98 & 3.06 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of algorithm performance metrics in path planning. Figure 8: Path Planning Comparison of Different Algorithms in Simulation Map 3. (a) GA; (b) SCA; (c) PSO; (d) GWO; (e) PAGWO; (f) convergence curve comparison.
# A Method for Underwater Acoustic Target Recognition Based on the Delay-Doppler Joint Feature
###### Abstract  
With the aim of solving the problem of identifying complex underwater acoustic targets using a single signal feature in the Time-Frequency (TF) feature, this paper designs a method that recognizes the underwater targets based on the Delay-Doppler joint feature. First, this method uses symplectic finite Fourier transform (SFFT) to extract the Delay-Doppler features of underwater acoustic signals, analyzes the Time-Frequency features at the same time, and combines the Delay-Doppler (DD) feature and Time-Frequency feature to form a joint feature (TF-DD). This paper uses three types of convolutional neural networks to verify that TF-DD can effectively improve the accuracy of target recognition. Secondly, this paper designs an object recognition model (TF-DD-CNN) based on joint features as input, which simplifies the neural network's overall structure and improves the model's training efficiency. This research employs ship-radiated noise to validate the efficacy of TF-DD-CNN for target identification. The results demonstrate that the combined characteristic and the TF-DD-CNN model introduced in this study can proficiently detect ships, and the model notably enhances the precision of detection.  
underwater acoustic target recognition; feature extraction; Delay-Doppler domain; joint characteristics; neural network 2024162005 1
## 4 Experiments and Results
### Ablation Analysis  
In order to analyze the effectiveness of each component of the proposed method on answer selection, we removed one component of our model and evaluated the rest of the model on knowledge-based QA datasets.  
Table 7 shows the performance of our model and its ablation, where \"Ours without Bi-LSTM\" means the answer selection model uses the attention mechanism to process the data. \"Ours without attention\" means the answer selection model uses self-attention to extract the global features, then uses the Bi-LSTM neural network to circulate the data. \"Ours without self-attention\" means the answer selection model uses an attentive Bi-LSTM to process the data.  
Figure 8: Accuracy performance of datasets.  
Bi-LSTM can better capture the semantic dependencies in both directions. When adding an attention mechanism, attention can ignore the noise information to improve the performance of Bi-LSTM. Self-attention is used as a feature extraction tool before data circulating, and the outputs include global information for each word of the vector. The proposed model uses self-attention to weigh each word before circulating. The faster the convergence speed is, the more accurate the extraction of location information. We can see from Table 7 that each component in our model could make a good performance.
## 3 Formation Methods and Simulation
### 11 \(\times\) 11 Matlab Simulations of UAVs Magic Square Formation
#### 3.3.2 Design of UAV Controller  
Through an algorithm based on the 3 \(\times\) 3 magic square grid, which was illustrated in Section 3, the expected position \(p_{di}\) and expected speed of every \(UAV_{i}\) could be calculated. Thus, the controller form of individual UAVs can be expressed as Equation (15).  
\[u_{i}=k_{p}(p_{di}-p_{i})+k_{d}(q_{di}-q_{i}) \tag{15}\]  
where \(k_{p}>0\) and \(k_{d}>0\) are parameters of UAV PID controllers.  
The values of each item in simulations are as shown in Table 1.
## 2 System Model
### IRC Receiver to Suppress ICI in Small-Cells
If the ideal IRC case is considered, then composite channels, i.e., from serving cell and all interferers, are known at the receiver, so the ideal IRC weight matrix at the UE-\(m\) served by \(b\)th SBS will be given by using (34) as:  
\[W_{ideal\ IRC}=\underbrace{G_{bm}^{H^{\prime}}\sqrt{P_{bm}}}_{\text{Serving SBS part}}\left(\underbrace{N_{q=1}^{N_{Cell}}G_{q}G_{q}^{H^{\prime}}\sqrt{P_{q}}}_{ \text{interferes part}}+\underbrace{n}_{\text{noise}}\right)^{-1}, \tag{36}\]  
where, \(G_{q}^{H^{\prime}}\) is the Hermitian transpose of the composite channel. Using the ideal IRC weight matrix, i.e., \(W_{ideal\ IRC}\), instead of the receiver weight matrix \(W_{Rx,bm}\) supresses the interference caused by other cells. However, considering that the composite channels from other cells are not known at the receiver, the covariance matrix that includes ICI information needs to be estimated; for this reason, 3 GPP has introduced a demodulation reference signal (DM-RS) approach [35]. Based on this model, IRC suppresses interference intelligently and limited backhaul communication is required, resulting in low-latency communication; nevertheless, this is not included in the scope of this work.
###### Abstract  
Computer-based neuropsychological assessments have many advantages over traditional neuropsychological assessments. However, limited data are available on the validity and reliability of computer-based assessments. The purpose of this study was to examine the reliability and validity of computer-based dementia assessment contents (Co-Wis). This study recruited 113 participants from Yeungnam University Medical Center in Daegu from June 2019 to December 2019 and received ethical approval. Participants were evaluated using standard and objective dementia cognitive test tools such as the Korean version of the Mini-Mental State Examination (K-MMSE), the Clinical Dementia Rating Scale (CDR), and the Standardized Seoul Neuropsychological Screening Battery-II (SNSB-II). To verify the effectiveness of Co-Wis, the concurrent validity, test-retest reliability ([PERSON]'s correlation coefficients), construct validity (Factor analysis), and signal detection analysis (ROC curve) were used. In most of the Co-Wis subtests, the concurrent validity and test-retest reliability showed statistically significant correlations (\(p<0.05\), \(p<0.01\)). The factor analysis showed that Co-Wis assessed the most major cognitive areas (Tucker-Lewis Index (TLI) = 0.876, Comparative Fit Index (CFI) = 0.897, RMSEA = 0.88). Thus, Co-Wis appears clinically applicable and with high reliability and validity. In the future, we should develop tests to evaluate both standard data and big data-based machine learning.  
computer-based assessment; dementia; reliability; validity +
Footnote †: journal: Applied Sciences
# Future Sustainable Internet Energy-Defined Networking
## 2 Related Work and the State of the Art
More recent research has focused on energy-saving techniques for edge computing resources that are expected to facilitate latency-sensitive applications, process offloading from small IoT devices with limited capabilities, and execute AI algorithms close to the location of data sources [21]. Solutions in this domain include workload orchestration to warrant the efficient use of compute resources, e.g., [22] energy-aware scheduling for executing different types of Artificial Intelligence (AI) tasks considering heterogeneous resources and latency boundaries [23; 24] and the placement as well as the configuration of virtualized network components to reduce power consumption, e.g., [4; 25]. Prior research mainly concentrated on centralized offline approaches to optimize energy consumption by turning part of the infrastructure off [2; 4; 26]. These approaches rely on demand prediction, which may only sometimes be precise. They can also not react to unexpected events because of user behavior or changes in the infrastructure itself. In addition, connectivity and computation resources have been treated in isolation, leading to sub-optimal energy-efficient configurations due to their intrinsic coupling.
## 4 Results and Discussion
The experimental values demonstrate that the FHEOKG-SGC method gains effectual performance with minimal DEL values. For example, in Test-1, the FHEOKG-SGC technique attained a reduced DEL of 0.096 ms, while the LEACH, AES, FGECDH, and EESRA approaches obtained increased DEL values of 0.504 ms, 0.478 ms, 0.401 ms, and 0.245 ms, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{5}{c}{**Packet Loss Rate (\%)**} \\ \hline
**No. of Tests** & **LEACH** & **AES Protocol** & **FGECDH Protocol** & **EESRA Protocol** & **FHEOKG-SGC** \\ \hline Test-1 & 26.75 & 27.35 & 24.59 & 22.72 & 19.45 \\ Test-2 & 25.23 & 26.32 & 22.37 & 21.67 & 19.1 \\ Test-3 & 23.89 & 24.68 & 22.12 & 20.97 & 19.48 \\ Test-4 & 24.16 & 25.74 & 23.87 & 19.51 & 18.98 \\ Test-5 & 26.09 & 23.65 & 21.47 & 18.34 & 17.69 \\ \hline \end{tabular}
\end{table} 
Table 2: PLR outcome of the FHEOKG-SGC approach with other protocols under varying counts of tests. Figure 3: EFS outcome of the FHEOKG-SGC approach under varying file sizes. Moreover, in Test-5, the FHEOKG-SGC technique attained a reduced DEL of 2.387 ms, while the LEACH, AES, FGECDH, and EESRA approaches gained increased DEL values of 5.968 ms, 5.632 ms, 4.512 ms, and 3.326 ms, respectively. These results highlighted the supremacy of the FHEOKG-SGC method over the other approaches.
## 3 The ALIEN Architecture
### Main Idea  
Our notation is summarized in Table 1. In the ALIEN model, there are three layers:  
1. The attributes-driven latent information extraction layer (USER \(\times\) CANDIDATE);
2. The user behavior-driven latent information extraction layer (HISTORY);
3. The latent information digestion and prediction layer (PREDICTION). Our model is illustrated in Figure 1. ALIEN performs two tasks in the macro and micro perspectives, and then merges those in the PREDICTION layer:  
Attributes-driven latent information extraction layer (USER \(\times\) CANDIDATE): This layer performs a macro task of learning the latent information from modeling the low and high-order feature interactions, thereby establishing the contribution of each feature interaction to the user \(u\)'s interest. Modeling low and high-order feature interactions from user and item attributes is overlooked by many recommendation models [7; 10]. In addition, in their models, user and item embeddings \(\mathbf{u}\) and \(\mathbf{v}\) are initialized only with indices of \(u\) and \(v\), which have vague meanings and converge slowly. However, user and item attributes are very important and provide key features which describe \(u\)'s and \(v\)'s basic characteristics. In our model, user and item attributes are used to initialize \(\mathbf{u}\) and \(\mathbf{v}\) via an embedding method, in order to render a clearer meaning with the side information and make the model converge faster. The embedding method is discussed in Section 3.3. Moreover, taking into consideration the exhaustive volume of calculations for generating high-order feature interactions, which grows exponentially when the order of interactions increases, we propose a novel architecture to reduce the computational complexity and parameter costs. User behavior-driven latent information extraction layer (HISTORY): This layer finishes the micro task and investigates the different impact which each history interaction has on the candidate item. Obviously it is not enough to build user profile with only stationary attributes. User behavior not only