esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Ablation Studies
#### 4.7.3 Hyperparameters of \(\lambda\) and \(\gamma\)  
The hyperparameters \(\lambda\) and \(\gamma\), which govern the dynamic adjustment of transfer loss (see Section 3.5.2), play key roles in determining recall, precision, and overall inter-model cooperation. In this study, \(\alpha\) and \(\beta\) remain fixed at 0.5, while performance metrics are gathered for various \(\gamma-\lambda\) pairs (see Figure 6). As the \(IoU\) consistency threshold, \(\lambda\) measures  
Figure 5: Effect of transfer-loss weights (\(\alpha\) and \(\beta\)) on model performance.  
the degree of similarity in the models' predictions. A relatively small \(\lambda\) defines consistency more leniently, capturing a greater number of predictions and thereby boosting recall, yet at the risk of admitting low-quality knowledge flow that can erode precision. By contrast, a larger \(\lambda\) imposes tighter consistency requirements, fostering higher precision but limiting collaborative interactions between models and thereby diminishing recall. Experimental findings suggest that a suitably moderate \(\lambda\) strikes an effective compromise between shared knowledge transfer and independent optimization. Meanwhile, the dynamic factor \(\gamma\) dictates the degree of transfer-loss attenuation when models exhibit high consistency. A larger \(\gamma\) enables models to lean more on their internal supervision signals, thus bolstering precision. Conversely, a smaller \(\gamma\) strengthens cross-model knowledge sharing, likely improving recall at the expense of reduced independence in optimization. Under lenient consistency requirements (e.g., \(Rank@1,IoU=0.3\)), the influence of \(\lambda\) and \(\gamma\) on recall is minimal, producing relatively stable performance. This suggests that collaborative knowledge exchange reliably enhances the model's overall recall capability. Under more balanced consistency thresholds (e.g., \(Rank@1,IoU=0.5\)), the models grow increasingly sensitive to variations in \(\lambda\) and \(\gamma\), making prudent parameter choices pivotal for balancing recall and precision. Under stringent consistency metrics (e.g., \(Rank@1,IoU=0.7\)), the models display the highest sensitivity to \(\lambda\) and \(\gamma\), and moderate \(\lambda\) in combination with a mid-to-high \(\gamma\) yields peak performance during collaborative learning. In summary, \(\lambda\) and \(\gamma\) jointly manage the trade-off between recall and precision and, by dynamically calibrating the strength of knowledge transfer, serve as key levers for enhancing the overall efficacy of cooperative learning.
## 2 Formulation of the Problem  
The configuration of the electromagnetic problem is shown in Figure 1, where a circular disk of thickness \(d\) and radius \(a\) with electrical conductivity \(\sigma\) and relative permeability \(\mu_{\rm r}\) is reported. The disk is placed on the plane \(z=0\) of a cylindrical coordinate system \((\rho,\phi,z)\) with the center at the origin. The source of the electromagnetic field is a circular current loop of radius \(R\) and constant current \(I_{0}\) coaxial with the disk and placed at \(z=h\). The time-harmonic \(\,\mathrm{e}^{\mathrm{i}\omega t}\) dependence is assumed and suppressed throughout. It should be noted that thanks to the azimuthal invariance of the problem the considered source produces a \(\mathrm{TE}_{z}\) field (with \(E_{\phi}^{\rm inc}\), \(H_{\rho}^{\rm inc}\), and \(H_{z}^{\rm inc}\)).
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## Appendix A
### Experimental Results After Applying Electrical Interference Noise
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **SNR** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{4}{*}{LSTM} & 27 dB & 0.993 & 0.9932 & 0.9943 & 0.9937 \\ \cline{2-6}  & 17 dB & 0.977 & 0.9794 & 0.9774 & 0.9782 \\ \cline{2-6}  & 11 dB & 0.9496 & 0.953 & 0.951 & 0.9519 \\ \cline{2-6}  & 3 dB & 0.9315 & 0.9375 & 0.9354 & 0.9362 \\ \hline \multirow{4}{*}{GRU} & 27 dB & 0.9903 & 0.9912 & 0.9914 & 0.9911 \\ \cline{2-6}  & 17 dB & 0.9753 & 0.9765 & 0.9774 & 0.9769 \\ \cline{2-6}  & 11 dB & 0.9541 & 0.9563 & 0.9559 & 0.9561 \\ \cline{2-6}  & 3 dB & 0.925 & 0.9315 & 0.9319 & 0.9317 \\ \hline \multirow{4}{*}{FCN} & 27 dB & 0.9887 & 0.9897 & 0.9893 & 0.9893 \\ \cline{2-6}  & 17 dB & 0.9767 & 0.9777 & 0.9774 & 0.9774 \\ \cline{2-6}  & 11 dB & 0.9527 & 0.9546 & 0.9552 & 0.9549 \\ \cline{2-6}  & 3 dB & 0.9288 & 0.9354 & 0.934 & 0.9346 \\ \hline \multirow{4}{*}{ResNet} & 27 dB & 0.9922 & 0.9926 & 0.9933 & 0.9929 \\ \cline{2-6}  & 17 dB & 0.9802 & 0.9804 & 0.9809 & 0.9805 \\ \cline{2-6}  & 11 dB & 0.9444 & 0.9472 & 0.9472 & 0.9472 \\ \cline{2-6}  & 3 dB & 0.9302 & 0.9339 & 0.9357 & 0.9348 \\ \hline \multirow{4}{*}{InceptionTime} & 27 dB & 0.9903 & 0.9914 & 0.9909 & 0.991 \\ \cline{2-6}  & 17 dB & 0.9746 & 0.9759 & 0.9746 & 0.9753 \\ \cline{2-6}  & 11 dB & 0.9468 & 0.9495 & 0.9482 & 0.9487 \\ \cline{2-6}  & 3 dB & 0.9288 & 0.9346 & 0.9354 & 0.9349 \\ \hline \multirow{4}{*}{XceptionTime} & 27 dB & 0.9922 & 0.9924 & 0.9946 & 0.9934 \\ \cline{2-6}  & 17 dB & 0.9725 & 0.9737 & 0.9743 & 0.9737 \\ \cline{2-6}  & 11 dB & 0.9447 & 0.9497 & 0.9472 & 0.9484 \\ \cline{2-6}  & 3 dB & 0.9309 & 0.9398 & 0.9347 & 0.9372 \\ \hline \multirow{4}{*}{XCM} & 27 dB & 0.9909 & 0.9919 & 0.9917 & 0.9917 \\ \cline{2-6}  & 17 dB & 0.9791 & 0.9798 & 0.9805 & 0.9802 \\ \cline{2-6}  & 11 dB & 0.9486 & 0.9517 & 0.9506 & 0.9512 \\ \cline{2-6}  & 3 dB & 0.9343 & 0.9403 & 0.9378 & 0.9389 \\ \hline \multirow{4}{*}{LSTMAttention} & 27 dB & 0.9911 & 0.9926 & 0.9914 & 0.9919 \\ \cline{2-6}  & 17 dB & 0.9802 & 0.9805 & 0.9812 & 0.9809 \\ \cline{2-6}  & 11 dB & 0.9493 & 0.9518 & 0.9524 & 0.9521 \\ \cline{2-6}  & 3 dB & 0.9211 & 0.9282 & 0.9295 & 0.9288 \\ \hline \multirow{4}{*}{GRUAttention} & 27 dB & 0.9938 & 0.9939 & 0.9943 & 0.9941 \\ \cline{2-6}  & 17 dB & 0.9829 & 0.983 & 0.984 & 0.9835 \\ \cline{2-6}  & 11 dB & 0.9489 & 0.9515 & 0.951 & 0.9512 \\ \cline{2-6}  & 3 dB & 0.9319 & 0.9376 & 0.9364 & 0.937 \\ \hline \multirow{4}{*}{TSSequencer} & 27 dB & 0.9914 & 0.9917 & 0.9922 & 0.9918 \\ \cline{2-6}  & 17 dB & 0.9822 & 0.983 & 0.9829 & 0.9829 \\ \cline{2-6}  & 11 dB & 0.9468 & 0.9501 & 0.9489 & 0.9494 \\ \cline{2-6}  & 3 dB & 0.9309 & 0.9414 & 0.9333 & 0.9373 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experimental results on MFPT dataset with electrical interference noise.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 5 Discussion  
Summarizing the results for Sample \(A_{H}\), it can be stated that the trend of TE and \(M_{Dr}\) show the breakage equally well, with a relatively clean spectrum. The most dominant orders are harmonics or close to the harmonics of the rotation frequency. The torque spectrum shows more activity in the high-frequency range. The trends and colorplots of the accelerometers forecast the damage a bit later in time than TE and \(M_{Dr}\). The spectrum shows, in the case of acceleration, more variation due to the restarting of the test bed, but these signals could be used to stop testing before breakage occurs as well. Dominant orders are also harmonics of the rotation speed in the case of acceleration spectra. Using the trends of SBR_TE_Pinion and SBR_TE_Ring, based on \(S_{diff}^{rel}(i)\), it is possible to differentiate between the damaged gear, and it is apparent from these trends that the pinion gear is damaged.  
Figure 23: Alternative indicators calculated with TE 200 bands for Sample \(B_{H}\) ring orders.  
Recapping the results of Sample \(B_{H}\), it is apparent that stopping the test before the breakage of the tooth occurs is possible with the use of the algorithm, with any of the parameters considered. As for Sample \(A_{H}\), using the trend of TE and the torque, an earlier stop could have been achieved compared to using the acceleration signals. It is true for all of the parameters that the most dominant orders are harmonics or close to the harmonics of the rotation frequency. The spectrum of the torque also shows more activity for this sample in the higher-frequency regions than the TE spectrum.  
Considering the damage of Sample \(B_{H}\), two broken pinion teeth, it is not possible to find any indication to the event of the breakage of a single tooth using the above presented results. However, with more testing of the gears and the CI, it may be possible to relate the actual damage more effectively to the results. The determination of the damaged gear is possible for this sample as well, using the SBR, calculated from the \(S_{diff}^{rel}(i)\) of TE.  
Based on the key performance indicators of signal-to-noise ratio, sensitivity, differentiation of damaged sample and detection time within the sample lifetime, the CIs can be contrasted. The results of this comparison have been summarized in Table 4.  
To fully explore and assess the capabilities of the CI and the described method, it is necessary to extend the research work to more samples, different types of gears and also to other types of machine parts.
# Intrinsic and Extrinsic Factors Affecting the Color of Fresh Beef Meat--Comprehensive Review
## 3 Results and Discussion
### Intrinsic Factors
#### 3.1.5. Slaughter Weight  
Feed intake and production systems might considerably influence the final carcass weight and consequently affect the ultimate pH and color of beef [36, 63]. Meat industries worldwide have struggled to cool down beef carcasses at a constant rate due to higher animal-weight variation in the last few years. This inconsistency in heavy-carcass temperature and pH decline might increase the risk of heat shortening and myoglobin autoxidationprocesses to occur [64]. It was also evidenced that when animals were slaughtered at the same age, grain-based systems produced heavier animals than pasture-based, due to an average higher calorie diet from the former [49; 63]. Improved L*, a*, and b* measurements from heavier cattle were also a result of higher-energy diets that were offered to the animals before leaving for the abattoir, which promoted slower glycogen degradation until slaughtering [65]. Different authors reported either higher L* [64], higher L* and a* [41], or overall higher L*, a*, and b* values [37; 63] in the meat of heavier than lighter cattle. Other publications reported that heavier Charolais cattle showed improved a*, b*, and C* values but lower L* scores. This experiment concluded that the preference for heavier animals paradoxically created darker lean muscles [20]. Significantly lower L* values were also reported in bulls' meat compared to young bulls' meat, even if bulls weighed 90 kg more when slaughtered [66]. Heavier cattle tend to produce meat with increased L*, a*, and b* attributes than lighter cattle. This effect was associated with a high-caloric diet that promoted better fat deposition, and because slower glycogen decline could be achieved from heavier cattle, which also benefited the formation of lactic acid in the muscles of those animals. The meat industry needs to find the means to standardize the weight of the animals to prevent major differences in meat color.
###### Abstract
Third, the data resources obtained by users often cannot directly meet their data requirements; they need to use data-processing tools or write code to preprocess these data [26; 27]. However, for some data users, this requires a high learning cost. In summary, in the context of increasingly in-depth geographical comprehensive research, sharing data through data files is difficult to ensure data-sharing security on one hand, and cannot improve the current situation of inefficient use of data by users on the other hand. Therefore, this paper proposes a service-oriented data-sharing and computing framework, which aims to provide users with a whole-process data-access mechanism for data acquisition, processing, and application. The framework consists of three core components: (1) The \"Data service container\" can publish data resources as data services, so that users can obtain data by invoking data services without downloading data files. The UDX (the Universal Data eXchange) model proposed by the OpenGMS team [28] is used as the data interface of the data service, so that the heterogeneous geographic data can be structured by UDX, thus reducing the difficulty for users to understand the data [29; 30; 31; 32]. (2) The \"Workspace\" provides a place for users to customize complex data requirements. Users can customize data-processing logic in the workspace by aggregating data services in the network, without downloading data to the local machine for data processing. (3) The \"Data-computing engine\" provides the execution environment for data-processing methods. This allows users to focus only on the data-processing logic without having to manually configure the execution environment for the data-processing method. In short, based on the proposed data-sharing and computing framework, users only need to pay attention to their own data application requirements, and customize the corresponding data-processing logic, without paying attention to the details of data processing, which greatly reduces the difficulty of data use and greatly improves the efficiency of data sharing. The remainder of this article is structured as follows. The data-sharing and computing framework is designed in Section 2. Section 3 introduces the design and implementation of three core components of the proposed framework. In Section 4, based on the proposeddata-sharing and computation framework, a case for preparing input data for the SWAT model is designed. Finally, Section 5 summarizes the research work of this paper and discusses the future work of the study.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Radio Map Construction Aiding the Offline Workload
An objection to GraphSLAM is that the measured signal similarity is computed in a single space underlying the data collected of a user walking speed from 5 to 10 s. This refers to the measured signals from many different APs, which are practically unremarkable between time segments. A unique feature of SLAM is to build up an unknown environment and be able to estimate optimal landmark location and nonlinearity. This could find an unknown location existing in an unknown environment and it can continuously build a successful map resulting in accurate indoor tracking [206]. The SLAM algorithm is also considered for real-time in map-building application [207]. The positive aspect of SLAM is that it has the ability to tackle map management [208], from multipath-assisted to upgraded location accuracy and tracking. Based on radio frequency or acoustic signal, the multipath delay is considered in order to obtain a continuous connection between indoor localization and mapping infrastructure [209]. To implement WLAN infrastructure, SLAM based on Wi-Fi FP is capable of setting up an indoor topological map, by using a sensor-based platform [139,210,211]. In addition, SLAM based on the magnetic field, namely, magnetic SLAM, can be used to promote the indoor localization accuracy using a weighted particle filter.
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
## 3 Preliminaries  
### YOLOv8 for Face Detection  
Object detection models are essential for identifying objects in images and videos. One of the most popular object detection models is the YOLO (You Only Look Once) model, which is known for its remarkable speed and accuracy in object detection tasks. Over the years, YOLO has undergone multiple iterations, each aimed at improving the model's performance and efficiency.  
YOLOv8 [18] is one of the recent iterations of the YOLO model, which is optimized for both speed and accuracy. Building upon YOLOv5 [27], it incorporates enhancements to refine the original architecture.  
Figure 2 illustrates an overview of the YOLOv8 architecture, which comprises three primary components: the backbone, the neck, and the head. The backbone network extracts features from the input image, the neck network refines these features, and the head network performs a specific task, such as object detection, classification, segmentation, or pose estimation. The model is trained on extensive datasets to learn object features and accurately perform the designated task.  
Although YOLO is designed primarily for general object detection tasks, its adaptability facilitates specialized applications. For example, training YOLOv8 on a dataset containing facial images enables the model to accurately detect facial regions. This capability is essential for facial data encryption, as it allows systems to identify and encrypt facial regions while leaving other parts of the image unmodified.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Approach** & **Key Features** & **Relies on User’s Private Photos** & **File-Level Protection** \\ \hline [PERSON] et al. [25] & Photo-sharing consent system for privacy protection & Yes & No \\ [PERSON] et al. [26] & Automatic tagging framework for photo access control & Yes & No \\ Proposed System & Dynamic multi-user access control for facial privacy protection & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative overview of proposed approaches with some existing works in user consent and access control for facial privacy protection.  
In this work, we leverage the YOLOv8 architecture for face detection to identify facial regions in images. By training the model on facial datasets, we achieve accurate face detection and selectively encrypt these regions to protect sensitive facial data. This approach ensures that facial data remain secure while maintaining the visibility of non-sensitive content in shared images, thereby preserving the usability of the image. The details of the model's training process and configuration are discussed in Section 5.1.2.
# REGNet: Ray-Based Enhancement Grouping for 3D Object Detection Based on Point Cloud
## 4 Experiments and Discussions
### Comparisons with the State-of-the-Art Methods  
**Experiments with ScanNet V2.** To demonstrate the effectiveness of the proposed REGNet, we compared it with the most recent state-of-the-art techniques. The results are summarized in Table 1. As shown, our proposed REGNet outperforms its baseline VoteNet and achieves comparable results with the current state-of-the-art techniques. Furthermore, REGNet performs the best in 8 out of 18 categories, which is almost three times more than the second-ranked GFNet, which only achieves the best results in 3 categories. This indicates that the three proposed enhancement modules can effectively improve subsequent object localization and classification tasks.  
We also provide the qualitative comparisons in Figure 3. In the figure, we give the raw point cloud, the visualization results of VoteNet, the current GFNet, the visualization results of our REGNet, and the corresponding ground truth. To ensure a fair comparison, we only compare the results of methods using only 3D geometric information. The comparison results show that our approach can obtain more accurate location results for objects such as beds and sofas, especially the bounding boxes indicated by the red dashed lines in the figure.  
**Experiments with SUN RGB-D.** We also evaluate our REGNet on the SUN RGB-D dataset; the quantitative results are provided in Table 2. In it, we compare our method with many other state-of-the-art methods, such as VoteNet, H3 DNet, and many others on the SUN RGB-D benchmark. From the comparison results, we find that our method achieves the best performance in six classes, whereas almost all of the rest achieve the second-best performance. We also illustrate the qualitative comparisons of SUN RGB-D in Figure 4. In the figure, from left to right, are the image, raw point cloud, the prediction results of VoteNet and GFNet, our results, and the corresponding ground truth.
# A Graph-Based Approach to Recognizing Complex Human Object Interactions in Sequential Data
## 3 Materials and Methods
### Feature Extraction
#### 3.4.5 Scene Feature: SPM  
Spatial pyramid matching (SPM) [49] is a solution to the bag-of-words (BOW) model which represents an image as an order-less collection of local features but discards the spatial relationships of local descriptors, which severely limits its descriptive power. In SPM, an image is partitioned into increasingly finer regions, and features are evaluated in the local regions. This is done in three levels in which the image is divided into 1, 4, and 16 regions respectively. Then local SIFT descriptors are extracted from the small image regions, and they are coded using a dictionary (learned using features from several training images). The code vectors in each spatial region are then pooled together by building histograms. Finally, the histograms of the different spatial regions are concatenated to give the complete SPM feature vector of an image frame. Each step of this process is shown in Figure 9.
## 4 Discussion  
### Quantitative Results  
The results of this study demonstrate the effectiveness of the optimized ABSA model, particularly in terms of its performance metrics. The quantitative evaluation focused on accuracy, F1-score, precision, and recall. Table 12 shows that the model achieved an accuracy of 97.3%, an F1-score of 95.2%, a precision of 96.8%, and a recall of 94.5%. These metrics highlight the robustness of the model in accurately detecting sentiment associated with specific aspects of Indonesian student reviews.  
In addition, the aspect extraction process achieved an accuracy of 97.3%, supported by a fine-tuned BERT model with optimized hyperparameters, such as a batch size of 64 and a learning rate of 2 \(\times\) 10\({}^{-5}\). Similarly, sentiment classification results indicate that the model performs consistently well across multiple test scenarios, achieving reliable precision and  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Dataset Size** & **Aspect Accuracy** & **Sentiment F1-Score** & **Remarks** \\ \hline SVM-Based Model (A) & 5000 & 0.73 & 0.75 & Manual feature engineering, limited dataset \\ \hline LSTM with Word Embeddings (B) & 8000 & 0.81 & 0.80 & Struggles with complex aspects in longer reviews \\ \hline mBERT Fine-Tuned (C) & 10,000 & 0.85 & 0.82 & Multilingual pre-trained model \\ \hline Proposed IndoBERT ABSA & 10,000 & 0.97 & 0.95 & Fine-tuned specifically for the Indonesian language \\ \hline \hline \end{tabular}
\end{table}
Table 14: The comparison performance model.  
recall scores. These findings underscore the efficacy of the IndoBERT-based ABSA model in addressing the nuances of sentiment analysis in Indonesian educational contexts.