esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experimental Procedures, Evaluation, and Discussion
#### 5.4.1 Type 1: Experiments of Regular Users
(2019), and the IOS by [PERSON] et al. (2020). These studies used the ML1 dataset and MAE as the performance metric. Accordingly, we measured the MAE value for the same dataset using UGSM. We compared our findings to the results obtained in another study (2019), where 3 to 150 neighbors were used. Table 10 shows the \(k\) neighbors tested in each algorithm, the number of neighbors, the k value at the Min MAE, and the best MAE value obtained by the indicated algorithms. Figure 7 shows the results. From Table 10, it is striking that UGSM gives the lowest MAE with a dynamic number of neighbors. From Figure 7, it is clear that the UGSM model has a better prediction accuracy than the compared algorithms in terms of all neighbor sizes. Since the UGSM does not directly depend on user ratings in selecting neighbors, its MAE is not affected much when changing the size. In the third part, we compared UGSM to the proposed algorithm in (2019), the UW algorithm, which has been compared to a variety of other approaches, including MSD, COS, Jaccard, PIP, and CJMSD (2019). It was applied in experiments to the ML-1M dataset. Thus, we used their dataset methodology with UGSM to compare them. Figure 8 demonstrates the MAE values for the ML-1M dataset for UGSM and several methods; \(k\) from 10 to 50, with increments of 5 per step. Figure 5: MAE for different k sizes on R2-yahoo music dataset by UGSM and other models. The \(x\)-axis is the K-NN size and the Y-axis is the MAE value. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**K-NN** & **W. NNf** & **W. [PERSON] Sim** & **MAE** & **K-NN** & **W. NNf** & **W. Min Sim** & **MAE** \\ \hline
20 & 8/20 = 0.4 & 0.5 & 0.63 & 65 & 28/65 = 0.43 & 0.52 & 0.56 \\
25 & 12/25 = 0.48 & 0.4 & 0.59 & 70 & 28/70 = 0.4 & 0.4 & 0.59 \\
30 & 12/30 = 0.4 & 0.4 & 0.56 & 75 & 32/75 = 0.42 & 0.32 & 0.582 \\
35 & 12/35 = 0.34 & 0.63 & 0.59 & 80 & 33/80 = 0.41 & 0.46 & 0.561 \\
40 & 14/40 = 0.35 & 0.4 & 0.61 & 85 & 33/85 = 0.39 & 0.38 & 0.546 \\
45 & 23/45 = 0.51 & 0.54 & 0.56 & 90 & 35/90 = 0.39 & 0.5 & 0.56 \\
50 & 23/50 = 0.46 & 0.451 & 0.56 & 95 & 37/95 = 0.39 & 0.4 & 0.558 \\
55 & 25/55 = 0.45 & 0.56 & 0.572 & 100 & 37/100 = 0.37 & 0.4 & 0.573 \\
60 & 27/60 = 0.45 & 0.541 & 0.58 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: MAE, NNf, and w min sim of adaptive R2-yahoo music dataset. These results reflect the ability of UGSM to choose the appropriate neighbors for the active user, based on the global user's taste for the item genre, thus increasing the power of the prediction model based on the local similarity among users.
## 3 ML Application in O-RAN
### Mobility Management  
Mobility management is a key function for cellular communication to maintain service continuity and ensure a good level of service quality for users moving across a network.  
\begin{table}
\begin{tabular}{c p{142.3 pt}} \hline \hline
**Year** & **Ref** & **Contribution** \\ \hline
2021 [4] & Proposed logistic regression for modular and flexible O-RAN in 6G networks, focusing on traffic steering and O-RAN xApps. \\ \hline
2022 [51] & Introduced federated meta-learning with DQN for privacy-preserving multi-RAT knowledge sharing. \\ \hline
2022 [52] & Proposed Q-learning-based algorithm for power and frequency allocation in O-RAN to minimize macro gNB interference and maximize device QoS. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of current efforts on ML applications for traffic steering in O-RAN.  
This requires efficient coordination of radio resources to achieve predictive, timely, and successful handovers for preventing communication disruptions in highly dynamic mobile environments. To support this, the O-RAN architecture offers various capabilities, including the collection of, maintenance of, and access to historical traffic and radio data. Additionally, real-time monitoring of traffic and radio conditions is achievable through the support of the Near-RT RIC framework, which enables the deployment of AI/ML-based applications for detecting and predicting handover anomalies at the user level.  
In [53], the authors proposed a new predictive handover method to predict target cells in advance, hence to reduce handover failures. Handover cases are simulated by random user movement within an environment with three eNBs and coverage holes. The handover prediction algorithm is implemented within a software developed by O-RAN Software Community (O-RAN SC), where an Anomaly Detection use case has been installed in the Near-RT RIC platform, which consists of three xApps: Anomaly Detection, Traffic Steering, and QoE Predictor. The process starts with the Anomaly Detection xApp, which analyzes UE data and sends notifications to the Traffic Steering xApp via the RMR protocol when anomalies are detected. The Traffic Steering xApp then requests a prediction of the target cell from the QoE Predictor xApp, which uses the Vector Autoregressive (VAR) algorithm to forecast time-series data based on past throughput data. As user mobility is not considered by the original QoE Predictor xApp, the paper has contributed by adding mobile users' RSRP measurements to the predict cells' throughput. The proposed intelligent prediction method achieves higher successful transmission rates than conventional handover algorithms and allows the traffic steering xApp to send commands to the RAN, such as a handover command using the REST API interface.
###### Abstract  
This study aimed to evaluate the effects of cluster thinning and severe shoot trimming on berry and wine composition and wine sensory characteristics of Merlot variety, in the context of climate change challenges related to grapevine ripening and the corresponding high alcohol content in wine. In two seasons, two different crop sizes were obtained via cluster thinning and combined in a two-factorial design with severe shoot trimming (SST) and its respective high canopy control (HC). In both seasons, cluster thinning (CT) resulted in higher Brix in grape juice and higher alcohol in wine than full crop size (FC), whereas SST obtained lower values than HC. Total anthocyanins and phenolics in wine were increased by CT, whereas SST had no any significant effect on wine's phenolic content. Several sensory characteristics of wine were positively affected by CT in both seasons, including aroma intensity, wine body and overall wine quality, whereas SST wines were in one season characterized by increased perception of vegetal aroma, acidity and bitterness, and decreased perception of body, persistency and taste balance. Our results demonstrate that practices which affect the leaf area to fruit ratio have a major impact on wine sensorial characteristics, concluding that their choice should be based on the desired wine style.  
grupevine; crop size; late source limitation; ripening; berry composition; wine; sensory characteristics +
Footnote †: journal: Applied Sciences
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 7 Evaluation of Web3602
### Performance Requirements Assessment
#### 7.2.2 Required Rendering Performance  
Web360\({}^{2}\) renders 360\({}^{\circ}\) videos in the viewing direction while drawing an audio visualization object. The number of triangular meshes for displaying the rendered video was 3968, and each audio visualization object consisted of approximately 550 triangular meshes. For the content played by Web360\({}^{2}\), it was necessary to render up to approximately 7500 triangular meshes. By default, A-Frame warns when the number of triangular meshes exceeds 1000. We believe that A-Frame set this criterion so that it would run comfortably on various devices, but Web360\({}^{2}\) exceeded this criterion by a large margin only for rendering video. In tests during the development phase, the slow-rendering behavior on a smartphone (iPhone SE 2020) was improved by omitting object rendering in the blind spot of the audio visualization object (Section 5.3.4). Nevertheless, it is a fact that even in the results of the online survey responses in Section 7.1, there was a tendency for tablet/smartphone evaluations to be worse than PC evaluations. When considered in conjunction with the above evaluation of the required performance of communication speed (Section 7.2.1), it can be said that the communication and rendering performance in the PC environment is satisfactory to some extent. However, the performance of tablets and smartphones is still insufficient.
## 2 Modeling  
### Focused IR Heating Condition  
Figure 1 presents a conceptual drawing of the focused IR heating method. As shown in Figure 1a, the lamp and target material are located at each focal point of the elliptical reflector; the reflector can gather a large portion of the emitted IR energy within a narrow area. \(\mathbf{q}_{\text{total}}^{*}(\mathbf{x})\) is the IR energy distribution in the heated area and \(l\) denotes the entrance length of the elliptical reflector. There have been some numerical studies on modeling \(\mathbf{q}_{\text{total}}^{*}(\mathbf{x})\)[19; 20; 21; 22]. The reported models differ slightly in detail but share the fundamental point of being based on the theory of ray optics; the models trace all paths of the IR rays emitted from the lamp in geometric ways, including reflector reflections. Figure 1b shows an example of the focused heating effect in a metal sheet; the heated example is from [22]. As the cross-sectional shapes of the lamp and elliptical reflector are uniform in the longitudinal direction, it is efficient to assume that the gradient of the heat flux distribution in the longitudinal direction is negligible, which allows in-plane modeling, as presented in Figure 1a. In the ray optics-based models in [19; 20; 21; 22], the equations are mathematically complex; calibration is not easy because the IR rays travel through highly complex paths caused by reflections. This study aimed to build a simpler and more accurate model in the aspect of energy, which is a scalar quantity.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 6 Discussion
### Research Questions
#### 6.1.5 RQ5--Future Developments  
It is a recurring issue from the reviewed papers that adding more constraints to the problem is desired, for example, adding due dates to consider lateness or adding resource breakdowns to consider a dynamic scenario. This further exacerbates the scalability issues, which require an urgent solution.  
GNN and RNN approaches cannot yet fully solve this problem. Graph-based approaches have trouble scaling past a certain point since increasing the number of nodes might exponentially increase the number of edges. Recurrent-based approaches need more and more strategies to solve problems, such as using attention weights or the latest model architectures, further increasing required computational power.  
It is important to use simpler architectures and problem encodings whenever feasible. However, more efficient agent-environment interactions, training frameworks or algorithms are needed to satisfy the desire to further constrain the already-complex problems tackled.  
Traditional optimization methods still outperform RL for combinatorial optimization problems. However, RL offers an alternative approach better suited for dynamic environments and changing problems without customized problem tuning.  
Various hybrid approaches where RL methods complement classic optimization methods have already been discussed in this document. However, there is still potential for RL to further save computational effort and increase efficiency by reducing the search space, providing metaheuristics parameters and warm-starting both solvers and metaheuristics, for example.
## 3 Wavelet Coefficient and Feature Extraction  
### Effect of Wears on Wavelet Coefficients of Radiate Sounds  
Discrete wavelet coefficient was calculated for specimens influenced by the adhesive wear, as shown in Figure 5a. As the wavelet number increased, the obtained coefficient increased gradually. A small coefficient value indicates a high frequency. The periodicity of the coefficient value changed in the second level, as shown in Figure 5b. Particularly, the frequency band corresponding to level 2 includes both the adhesive and abrasive friction noise ranges [11]. For minimal pressure applications, there was no significant change in periodicity in the second level. As the contact pressure increased, the sound level increased. The high-frequency components for scratching caused by contact pressure appeared in the detail coefficients, especially in level 2. Figure 6a,b show the wavelet coefficients for the abrasive wear. The effects of the wear appeared at the second level of the detail coefficients. The abrasive wear did not have a significant difference in the detail coefficient values compared to those of the adhesive wear. The cycle change of coefficient values was lower. The noise tended to a specific signal and can be compared using PSNR.  
Figure 4: Process of wavelet decomposition in the primary stage for original acoustic response.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
## 3 Results and Discussions
### Anticancer and Biocompatibility Performance  
Multiple studies have shown that the introduction of metal doping (e.g., Zn and Ag) into different metal oxide NPs exhibited increased cytotoxicity on human cells, as compared to pure metal oxide NPs. For example, [PERSON] et al. [52] observed that Zn-doped TiO\({}_{2}\) NPs induced cytotoxicity and oxidative stress in human breast (MCF-7) cancer cells, with the intensity of toxicity increasing following the increased concentration of Zn doping. In another instance, [PERSON] et al. [26] demonstrated that both SnO\({}_{2}\) NPs and Ag-doped SnO\({}_{2}\) NPs induced cytotoxicity and oxidative stress in tobacco cell cultures, with Ag doping playing a pivotal role in toxicity induction. Figure 7A illustrates the cell viability of MCF-7 cells following exposure to pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) for 24 h. At lower concentrations, prepared NPs did not affect the cell viability of MCF-7 cells. However, a significant decrease in cell viability was observed with increasing concentrations of NPs. These results reveal the potential dose-dependent cytotoxic effect of NPs on MCF-7 cells. Moreover, it was observed that Zn-doped SnO\({}_{2}\) NPs (1% and 5%) had the most significant anticancer efficacy against MCF-7 cancer cell lines. This enhanced activity could be attributable to the synergistic effects of Zn doping on SnO\({}_{2}\) NPs. In this context, Zn doping interacts with the SnO\({}_{2}\) NPs, creating a synergistic effect. This interaction is likely to enhance the ability of NPs to inhibit the growth of MCF-7 cancer cells. Table 2 demonstrates the IC\({}_{50}\) values of pure SnO\({}_{2}\) NPs and Zn-doped SnO\({}_{2}\) NPs (1% and 5%) of MCF-7 cancer cell lines.  
Biocompatibility is an important aspect of anticancer materials. Our other results found that both pure and Zn-doped SnO\({}_{2}\) NPs show good cytocompatibility when exposed to human umbilical vein endothelial cells (HUVECs) (Figure 7B).
## 1 Introduction
[24] proposed a trajectory optimization algorithm to formulate the lateral vehicle guidance task along a reference trajectory, which is with optimized efficiency. Ref. [25] proposed a two-layer model predictive control controller to optimize the required longitudinal force and yaw moment adjustments and to achieve the minimized error of the steady state tracking objective. A combined control algorithm was designed in [26] by taking the yaw rate and the centroid slip angle error as input variables and using the braking torque as the steering angle of the control objectives. In [27], a novel fusion feed-forward neural network controller for task decomposition was proposed to realize the lateral control of autonomous vehicles. In [28], a robust output feedback vehicle lateral motion control strategy considering network-induceddelay and tire force saturation was proposed to overcome the uncertainty of tire turning stiffness and external disturbance. In this paper, we introduce a QFD model and Kano-TRIZ industrial design method to design a novel four-wheeled rollator and adopt a model predictive control algorithm to achieve the lateral trajectory tracking control of the rollator. A simulation was conducted to validate its feasibility in terms of the trajectory tracking error of the proposed design and control methodology. The remainder of this paper is structured as follows: Section 2 describes the novel rollator design with Kano-Triz theory; Section 3 discusses the kinematic and dynamic model of the designed rollator and introduces lateral stability tracking control for its controller development; simulation and validation is performed and the corresponding results and discussions are drawn in Section 4. The conclusion and future work are presented in Section 5.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 4 Proposed Methodology  
This section outlines the architecture and core procedures of the proposed edge-assisted QoS routing approach. As the RPL is the standard Low-Power and Lossy Network (LLN) routing protocol, this work enhances its applicability under challenging QoS routing conditions through architectural and procedural extensions. A viable strategy to address the limitations of resource-constrained devices in Wireless Sensor Networks (WSNs) is to offload computationally intensive tasks to external edge resources. Such offloading extends the network's functional capacity and enables scalable support for multi-constrained QoS routing.
## 4 Sound Event Classification Systems
### Audio Spectrogram Transformer  
We have also tested the Audio Spectrogram Transformer (AST) architecture proposed in [23], a state of the art architecture that provides an independent baseline to compare with our proposed convolutional architectures. This system uses a transformer-based architecture to classify audio signals. Transformers require a great amount of training data, much more than the amount required by CNNs. That is why the AST uses pretrained models. Moreover, since spectrograms can be considered as images, the AST employs cross-modality transfer learning training an initial model using the Vision Transformer (ViT) [49] architecture trained with the ImageNet [50] database. This initial model was retrained with spectrograms from the AudioSet database. This results in the AST-P public  
Figure 3: Convolutional block.  
model [51] that we have used as pretrained initialization for the training of the network with our database.  
The preprocessing of the sound event recordings for the AST was similar to that of the convolutional systems (see Section 4.1.1): the audio files were cut into fixed-length analysis fragments of 1 s, with a 0.5 s hop size, but in this case, as the AST uses pretrained models, the audio files had to be downsampled to 16 kHz. Each fragment was converted into a log-Mel-spectrogram using a Mel filter bank of 128 filters, with a framerate of 10 ms and a window of 25 ms. The outcome was next divided into a set of 16 \(\times\) 16 patches, which were then linearly projected onto a set of 1D embeddings. Each patch embedding was added to a learnable positional embedding. An additional classification token was appended to the sequence. The resulting sequence was then used as an input to the transformer. Since the AST is designed for classification tasks, it only uses the encoder part of the original transformer architecture [52]. AST uses an encoder that has an embedding dimension of 768, 12 layers, and 12 heads. The output of the encoder of the transformer serves as the audio spectrogram representation. A linear layer with sigmoid activation maps the audio spectrogram representation to a label for classification.
## 2 Literature Review
### Early Engagement  
Early Warning System. Implement a data-driven early warning system that tracks student performance, attendance, and engagement in real-time. This system would flag at-risk students early in their academic journey, allowing advisors and faculty to intervene before students reach a point of disengagement. Peer Mentoring Program. Launch a peer mentoring program where senior students mentor first-year students in challenging subjects. This could help ease the transition into university-level courses and provide guidance on how to succeed in difficult subjects. Summer Bridge Program. Create a summer bridge program for incoming students who may lack the necessary foundational knowledge in key subjects like mathematics and physics. This pre-academic term program would offer intensive coursework to better prepare students before they begin their first-year subjects.  
Personalized Learning Support. Utilize adaptive learning technologies to provide personalized learning plans for students who are struggling. These plans could include tailored resources, such as video tutorials or targeted practice exercises, to help students catch up in specific areas where they are falling behind.  
Our proposed work touches on existing predictive models for student attrition but lacks a detailed critique of the limitations. Many current models, such as logistic regression or basic decision trees, are limited by their inability to capture non-linear relationships and interactions between variables like socioeconomic status, academic performance, and behavioral factors. These models often rely on a narrow set of features and cannot accommodate the complexity of student behavior, leading to oversimplified predictions and higher false positive rates. Additionally, they may struggle with handling large datasets or missing values, further reducing their predictive power. In contrast, the proposed model integrates machine learning with big data analytics, utilizing a random forest algorithm to improve accuracy and robustness. Unlike traditional models, the random forest approach can handle vast, heterogeneous data sources and implicitly perform feature selection, identifying critical factors influencing dropout. This model also provides real-time monitoring, enabling timely interventions, which is a significant improvement over the static model. Furthermore, the proposed model focuses on early-year subjects, allowing for targeted interventions in foundational courses. These innovations help to reduce overfitting and enhance the generalizability of the model across diverse educational settings.