esa-sceva/satcom-chunk-collection huggingface dataset:
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 2 Related Work
### Multimodal Fusion  
For multimodal recommendation systems, finding a multimodal fusion method that excludes modality similarity and can achieve information complementarity between modalities is crucial for enhancing recommendation performance. Multimodal fusion methods mainly include early fusion, hybrid fusion, and late fusion [37]. Early fusion refers to the process of merging the modal features extracted at the beginning and then inputting this combined feature representation into the model for processing. On the contrary, late fusion involves merging after the model has made decisions for each modality. Hybrid fusion combines these two methods. For example, UVCAN [38] uses an early fusion method based on attention mechanisms. In the late fusion approach, SLMRec [39] presents an innovative self-supervised learning framework for multimedia recommendation, leveraging graph neural networks alongside three novel self-supervised tasks: feature dropping, feature masking, and fine- and coarse-grained feature learning. By implementing these tasks, SLMRec generates multiple views that facilitate contrastive learning, thereby uncovering implicit relationships across different modalities and performing late fusion on the generated item views. CELFT [40] has developed a hybrid fusion method where fusion is performed both before and after modal features are encoded, combining early and late fusion together. However, these fusion methods only concatenate or add the item representations obtained from different modalities, failing to effectively distinguish the similarities among modal features and fully leverage their complementarity. Therefore, we propose a two-step enhanced fusion method that takes a strategy of first enhancing and then fusingitem representations to effectively distinguish the similarity between multimodal features and fully utilize their complementarity.
# A Wearable Sonification System to Improve Movement Awareness: A Feasibility Study
###### Abstract  
This paper presents the design, development, and feasibility testing of a wearable sonification system for real-time posture monitoring and feedback. The system utilizes inexpensive motion sensors integrated into a compact, wearable package to measure body movements and standing balance continuously. The sensor data is processed through sonification algorithms to generate real-time auditory feedback cues indicating the user's balance and posture. The system aims to improve movement awareness and physical conditioning, with potential applications in balance rehabilitation and physical therapy. Initial feasibility testing was conducted with a small group of healthy participants performing standing balance tasks with eyes open and closed. Results indicate that the real-time audio feedback improved participants' ability to maintain balance, especially in the case of closed eyes. This preliminary study demonstrates the potential for wearable sonification systems to provide intuitive real-time feedback on posture and movement to improve motor skills and balance.  
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Footnote †: journal: Medical  
clinical evaluation [12], and provide information on the quality of movement via realistic pictures of patient functioning during rehabilitation [13].  
An increasing number of commercially available low-cost, off-the-shelf devices and open-source software have created opportunities for manufacturers, designers, engineers, technologists, and researchers to design and develop devices that can be worn and enhance interactions between them and their users. When deployed in motion-aware applications, these wearables can improve awareness of the limb's position, movement, and gait activities [5; 14; 15; 16; 17]. Wearable technologies for accurate gait event detection have come in many different forms, such as inertial measurement units (IMUs) [5; 17; 18; 19; 20] with embedded sensors, including accelerometers [21; 22; 23; 24; 25] and gyroscopes [26; 27; 28].  
Sonification is a process of using non-speech audio to convey information to users [29]. The application of sonification in gait and rehabilitation is a growing area of interest [17; 30; 31; 32; 33]. It involves translating gait characteristics captured by wearable technology into auditory feedback, aiding in establishing rhythmic and harmonious gait or movement patterns.  
The integration of IoT, wearable technology, and sonification offers a promising avenue for personalized healthcare interventions [10; 11; 33]. The integration of IoT and wearable technology in this study offers a transformative approach to enhancing movement awareness, especially through the innovative use of sonification. By leveraging affordable, compact motion sensors, the research focused on developing a user-centric feedback system and applying sonification techniques for continuous body movement measurement. This facilitated real-time, data-driven insights, crucial for improving physical awareness and balance, particularly for individuals with movement disorders.
# Reputation-Based Trust Approaches in Named Data Networking
## 1 Introduction  
Trust is a concept that is tricky to define. One of its definitions is \"the extent to which one party is willing to participate in a given action with a given partner, considering the risks and incentives involved\" [1]. A trust decision is based on the balance between trust and risk, and it has some sort of effect on the trustee.  
Named Data Networking (NDN) [2], the most prominent ICN architecture, currently utilizes trust mechanisms that are based on cryptographic signatures and certificates. This approach is called credential-based [3] or policy-based [4] trust. It be considered as a \"strong and crisp\" approach, where decisions are founded on logical rules and verifiable properties encoded in digital credentials.  
However, there exists another major approach to trust management, a \"soft and social approach\", based on reputation measures gathered and shared by a distributed community. This model is called \"reputation-based trust\". In this approach, when agents decide whether to trust another agent or not, they rely on evidence of past behavior supplied by trusted sources, as opposed to credentials.  
In this paper, we provide an overview of reputation-based schemes designed for the NDN architecture and argue that reputation-based trust should also be considered as a viable, complementary solution for achieving trust in NDN, or other related architectures (NDN-based such as UMOBILE (A Universal, Mobile-Centric, and Opportunistic Communications Architecture) [5], or similar ones such as CCNx [6]).  
Integrating reputation-based trust in NDN can also assist in securing environments that suffer from such issues, such as the Internet of Things (IoT). With the advent of the IoT and the new challenges that have arisen, many researchers have found in ICN an ideal match. The authors of Reference [7] argue that the IP model brings many constraints in such environments regardingsecurity and integration of local communications, introducing unnecessary complexity. On the other hand, retrieving named data in ICN naturally matches application semantics--simplifying their operation--while its main features can intrinsically support requirements such as energy efficiency, security, robustness, network scalability and mobility [8].  
In the following sections, we first provide a background on reputation-based trust, as well as the NDN trust and security mechanisms currently in use, in Section 2. Then, we provide incentives for further investigating the potential of reputation-based trust in NDN and we overview the existing reputation-based mechanisms, in Section 3. Aiming to provide a generalized framework for designing reputation-based trust mechanisms in NDN, we detail the design options and their trade-offs in Section 4. Finally, we highlight the existing and potential use cases of reputation-based schemes in NDN and discuss related issues and challenges in Section 5.
# Design of a Binocular Pupil and Gaze Point Detection System Utilizing High Definition Images
## 4 Conclusions  
In this study, we proposed a new binocular full HD resolution eye gaze tracking system using LabVIEW (LabVIEW 2015, National Instruments Corporation, Austin, TX, USA) image processing algorithms. The experimental results show that average error of the gaze estimation system is 0.83 degree on a 1280 \(\times\) 1024 pixel resolution user monitor. The system working frequency is 40 Hz. According to the systems utilizing 640 \(\times\) 480 size images, our result corresponds to 280 fps approximately. We preferred fast binary image processing algorithms to achieve this frequency. After binarization, pupil area is made clearer with morphological methods, and center coordinates of pupil and corneal reflections are found with the weighted average technique which is implemented faster than elliptic and circular shape detection methods. Therefore, the reactions of pupil depending on the stimulus can be observed at small time slices. This kind of fast eye tracker system finds its use in many practical applications such as human computer interaction, aviation, medicine, robot control, production tests and augmented reality.  
The system works under a camera viewing angle. Head movement area width is 38.42 cm and height is 29.37 cm. Eye pupil and corneal reflections can be detected in this area. This is a limitation of the system. The design was tested with 11 participants. One of the participants was a contact lens user. The user's eye pupil and corneal reflections were detected without problems. Reflections caused by the NIR illuminator and environment illumination can occur on the glass depending on eyeglass materials. To test the system for eyeglass users, each set of eyeglasses requires testing. The design has the ability to adjust threshold value and the size of reflections in the software. Therefore, the system could handle eyeglasses.  
In the future, we will study another method to increase the accuracy and we will try to implement image processing algorithms into our FPGA frame grabber card to overcome operating system delays and tracking frequency.  
This research was supported by coordinatorship of scientific research projects of Ankara University (13B4343008). All authors contributed to this collaborative work. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] performed the research and discussed the results. [PERSON] wrote the paper and [PERSON] contributed to the writing and revision of the paper. All authors approved the manuscript.  
The authors declare no conflict of interest.  
Figure 13: (**a**) Right eye estimated gaze points tracking the stimulus signal; (**b**) Left eye estimated gaze points tracking the stimulus signal. Note that blue circle represents cycle of the moving target.
# Investigation of the EWT-PSO-SVM Model for Runoff Forecasting in the Karst Area
## 2 Methodology
### Developed EWT-PSO-SVM Model  
The EWT method inherits the advantages of EMD and wavelet analysis. The PSO algorithm is an intelligent optimization algorithm, which is one of evolutionary algorithms to further optimize the SVM parameters. SVM has a simple structure, good robustness, and good classification ability. Combining the advantages of the above three methods for predicting runoff series, a hybrid EWT-PSO-SVM forecasting model was constructed. First, the EWT is applied to resolve the original monthly runoff series into IMF (IMF\({}_{1}\), IMF\({}_{2}\), \(\dots\), IMF\({}_{n}\)) components with certain regularity. Second, the PSO method is used to optimize the SVM parameters \(c\) and \(g\). Third, using the PSO-SVM model as a forecasting tool, the extracted individual IMF components are substituted into the PSO-SVM model to get the corresponding forecasting results for every IMF component. Finally, the forecasting results of all IMF components are combined to get the final runoff forecasting results. The developed method mainly consists of the decomposition stage and the reconstruction stage. In the decomposition stage, the subseries obtained by EWT decomposition are more suitable for models to find periodicity and regularity than the original series. In the reconstruction stage, the information in the forecasting results of all subseries is combined to obtain the final results. Through both decomposition and reconstruction stages, the developed model is more likely to have better generalization ability.
# Image-Captioning Model Compression
## 6 Conclusions  
In this work we proposed the use of different neural network compression techniques for models trained to solve image-captioning tasks. Our extensive experiments showed that all of the proposed methods help to reduce model size with an insignificant loss in quality. Thus, the best obtained model reaches 127.4 CIDEr and 21.4 SPICE, with a weighting of only 34.8 MB. This sets up a strong baseline for future studies on this topic.  
In future works, other compression methods such as knowledge distillation could be investigated in order to reduce the models' sizes even more. Also, some more complex pruning or quantization techniques could be used. The other direction for further research could be encoder compression, using not only different architectures, but also the other methods, for example, pruning and quantization. Reducing the size of more complex and bigger models such as [35,36] is also a promising direction of study.  
**Author Contributions:** Conceptualization, methodology, software, writing--original draft preparation, visualization, investigation, editing V.A.; writing--review, supervision, project administration, funding acquisition D.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **B@1** & **B@4** & **M** & **R** & **C** & **S** & **Size** \\ \hline \hline  & \multicolumn{6}{c}{Up-Down} \\ \hline Without quantization & 79.2 & 35.1 & 26.9 & 56.3 & 118.4 & 20.8 & 62 MB \\
**With quantization** & **79.2** & **35.3** & **26.9** & **56.3** & **118.4** & **20.8** & **43.4 MB** \\ \hline \hline  & \multicolumn{6}{c}{AoANet} \\ \hline Without quantization & 79.5 & 38.2 & 29.1 & 58.3 & 127.6 & 21.5 & 50.4 MB \\
**With quantization** & **79.4** & **38.3** & **29.0** & **58.3** & **127.4** & **21.4** & **19.7 MB** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results of all of tested models decoders with and without quantization. “Size” column refers to decoder.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **B@1** & **B@4** & **M** & **R** & **C** & **S** & **Size** & **NNZ** \\ \hline \multicolumn{8}{c}{Up-Down} \\ \hline Original model & 79.8 & 36.3 & 27.7 & 56.9 & 120.1 & 21.4 & 661.4 MB & 172.7 M \\ Compressed model & **79.2** & **35.3** & **26.9** & **56.3** & **118.4** & **20.8** & **58.5 MB** & **20.1 M** \\ \hline \multicolumn{8}{c}{AoANet} \\ \hline Original model & 80.2 & 38.9 & 29.2 & 58.8 & 129.8 & 22.4 & 791.8 MB & 207.4 M \\ Compressed model & **79.4** & **38.3** & **29.0** & **58.3** & **127.4** & **21.4** & **34.8 MB** & **17.1 M** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation results of all of tested models both original and compressed using methods from this paper. Both “Size” and “NNZ” columns refer to the whole model.
# Effect of Heat Treatment on Microstructure and Creep Behavior of Fe-40 Ni-24 Cr Alloy
## 3 Result and Discussion
### Creep Characteristic
This is followed by 0.064% strain for the specimen heat-treated at 1200 \({}^{\circ}\)C, 0.096% strain for the specimen heat-treated at 1250 \({}^{\circ}\)C. Figure 4d shows the plot of the strain rate versus time for heat-treated Fe-40 Ni-24 Cr alloy. The graphs clearly showed that the creep rate decreased to a minimum steady-state value and later increased at the end of the creep test. Figure 4e shows the minimum strain rate value at the secondary creep stage versus grain size. The minimum strain rate value for the specimen heat-treated at 1050 \({}^{\circ}\)C (62 \(\upmu\)m) was 1.9914 \(\times\) 10\({}^{-3}\)% strain/s, followed by the specimen heat-treated at 1200 \({}^{\circ}\)C (158 \(\upmu\)m), which had 1.1890 \(\times\) 10\({}^{-3}\)% strain; the specimen heat-treated at 1225 \({}^{\circ}\)C (183 \(\upmu\)m) had 7.1875 \(\times\) 10\({}^{-4}\)% strain and the specimen heat-treated at 1250 \({}^{\circ}\)C (312 \(\upmu\)m) had strain rate of 8.2249 \(\times\) 10\({}^{-4}\)%. The minimum strain rate value decreased from grain size 62 \(\upmu\)m to 183 \(\upmu\)m and slightly increased from 183 \(\upmu\)m to 312 \(\upmu\)m which showed the same high temperature creep mechanism result at temperature 800 \({}^{\circ}\)C. Figure 4f shows the creep rupture time of heat-treated Fe-40 Ni-24 Cr alloy crept at 900 \({}^{\circ}\)C and 100 MPa. The resulting trend shows the creep rupture time increase with grain size. The creep rupture time for the specimen heat-treated at 1050 \({}^{\circ}\)C (62 \(\upmu\)m) is 3.2 h; for the specimen heat-treated at 1200 \({}^{\circ}\)C (158 \(\upmu\)m) it is 4.9 h, followed by the specimen heat-treated at 1225 \({}^{\circ}\)C (183 \(\upmu\)m), which is 5.5 h, and the specimen heat-treated at 1250 \({}^{\circ}\)C (312 \(\upmu\)m), which is 6.4 h. The creep rupture time was affected by the steady-state creep rate results. As the steady-state creep rate decreases with grain size, the creep rupture time increases with the increase in grain size. Figure 4: Creep curve of heat-treated Fe-40 Ni-24 Cr at temperature 900 \({}^{\circ}\)C and initial stress of 100 MPa; (**a**) total strain versus time; (**b**) instantaneous creep strain; (**c**) instantaneous strain versus grain size; (**d**) strain rate versus time; (**e**) minimum creep rate versus grain size (**f**); creep rupture time versus grain size of the heat-treated alloy.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 2 Materials and Methods
### Training  
Models were built and trained using the Pytorch and Pytorch Lightning neural network libraries [61, 62]. The U-Net weights that generate \(\mathbf{G}\) and the weights of the linear layer \(\mathbf{F}\) are learned from the data itself. With random initialization of all weights, training did not converge to an optimal result. By initializing \(\mathbf{F}\) using principal component analysis through singular value decomposition [44, 46], the network converges to a good solution. This was inspired by [28, 29], which use an identical initialization process. Unlike [29], which fixes \(\mathbf{F}\) to the initial PCA solution, and [28], which optimizes \(\mathbf{F}\) using a trust region strategy on a Stiefel-manifold to guarantee orthogonality, continued orthogonality is not enforced during training. Proper initialization of the U-Net is also imperative for training stability. The learnable PReLU parameter \(a\) is chosen as 0.75, and Kaiming uniform initialization is used to initialize the other weights of the U-Net [52].  
The captured image is standardized to a zero mean and standard deviation of one and used as training data. Training is fully unsupervised, and any available ground truths are not used during training, so the image is not split into training or test patches. Since there is only a single training image during each training session, using minibatches is unnecessary. In the following experiments, identical hyperparameters were used for all images. The learning rate was set at \(8\times 10^{-3}\) and \(\lambda=0.0025\). Each model was trained for 5000 steps in 25 epochs of 200 steps each for this experiment. Other training parameters, such as those of NAdam, are kept at default values. While these hyperparameters can be tuned, the default values gave good results for varied images. Adjusting the regularization penalty, \(\lambda\), for individual images might provide further improvements.
# A Review of Power System Fault Diagnosis with Spiking Neural P Systems
## 1 Introduction
Furthermore, a methodology for fault diagnosis in transformers was presented. There has been only a few investigations regarding incorporating the idea of machine learning in SNPS. It is also difficult to introduce machine learning mechanisms in SNPS because of its formal language theory framework. Recently, a new method based on learning Spiking Neural P System with Belief AdaBoost [91] is introduced for fault diagnosis in transformers. Moreover, SNPS models and their variants have been introduced for identification of faults in power transmission networks [125], traction power supply systems of high-speed railways [88], metro traction power systems [82], electric locomotive systems [87]. Moreover, these models have been used for fault location estimation of power systems [126] and fault line detection [85]. In this paper, we have summarized all the SNPS models and their corresponding methodologies for fault diagnosis in different types of power systems. More specifically, we discuss the structure and working of different variants of SNPS and studied the important aspects of the reasoning algorithms based on these models. Moreover, we discuss the advantages and disadvantages of different fault diagnosis methods. At the end, we discussed an automatic implementation method for fault diagnosis [127]. The main motivations for preparing this paper are as follows:  
(1) Until now there has been no comprehensive survey collecting all the methodologies of fault diagnosis of power systems based on the membrane computing models;  
(2) Fault diagnosis of power systems with spiking neural P systems is an important application of membrane computing. This review aims to highlight and advance this direction. (3) One more motivation of this review is to advance a newly emerging research direction, i.e., learning spiking neural P systems, by discussing a supervised learning algorithm in the framework of SNPS and using this model for identification of faults in transformers. Furthermore, the contributions of this paper can be summarized in the following manner:  
(1) This paper provides a comprehensive survey of SNPS models and a software tool which are used for fault diagnosis in power systems;  
(2) The comparisons of different fault diagnosis methods and reasoning algorithms are studied in this paper. (3) This paper discusses future research lines with respect to SNPS for power system fault diagnosis and learning SNPS. This paper is organized in the following manner: Section 2 discusses fault diagnosis methods in power systems in the framework of SNPS and its variants. Section 3 discusses an automatic implementation method for fault diagnosis in complex power systems and finally in Section 4, we conclude the paper and discuss some future research lines.
## 3 The Software of TLE Observation System
### TLE Detection Algorithm
**(b)** Comparison of two consecutive samples of the filtered ADC values by the temporal and energy discriminators confirms that the radiant power is originated from TLE. **(c)** The information from the spatial discriminator is used to set the region of interest (ROI) for the image acquisition purpose. determines the region of interest (ROI) so that the onboard CMOS camera only captures images over the ROI, which allows efficient memory usage of the onboard TLE observation system.