esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 4 Experiments
### Comparison with Previous Work  
This section compares our results with related work that used either visual or audiovisual modalities, as shown in Table 10. The performance column in the table represents the classification accuracy achieved in each study. The findings demonstrate that the performance achieved in our work is comparable to that in prior studies that employed visual or audiovisual features.  
Prior work such as [7; 8; 10] used datasets recorded in lab-controlled environments. Work that used audiovisual features with the smile as the facial feature achieved accuracies of 84% using SVM and 93% using LR [7; 10]. However, their work was limited to Japanese people because they used a Japanese female model to extract the smile feature. Later work employing only visual features, including facial action units, eye gaze, and lip activity, achieved an accuracy of 82% using LR [8]. In contrast, our work is based on a dataset recorded in the wild, as described in Section 3.1, which introduces additional challenges such as variable lighting, background noise, and participant movement.  
Another key difference is the classification tasks. Prior studies primarily focused on detecting dementia from HCs regardless of the dementia type even though their dementia group includes several dementia types such as Alzheimer's disease (AD), MCI, normal pressure hydrocephalus, and dementia with Lewy bodies (DLB) [7; 8; 10]. Other work focused only on detecting MCI from HCs [11]. Our work focused on investigating different memory-related conditions (ND, MCI, FMD) both from each other and from HCs, across a range of classification tasks.  
A recent study employed neural networks (NNs) to extract facial features, achieving 87% accuracy in distinguishing MCI from HCs [11]. However, their dataset consisted of video-recorded, semi-structured interviews where participants were clearly visible, and lighting conditions were optimal. In contrast, we utilized hand-crafted features (HCFs) instead of NNs, as HCFs require less training data while still achieving comparable results with an accuracy of 89%.  
Finally, Table 10 includes results obtained using the IVA\({}_{18}\) dataset [12; 25] and the IVA\({}_{52}\) dataset (the row labeled 'Our'). The results show that the performance tends to decreasewhen using the IVA\({}_{52}\) dataset, which is probably due to the increased variability and complexity of the in-the-wild recordings.
## 4 Simulation Results and Discussion
### Bit Error Rate of the System
With higher values of \(n_{m}\), IRS could achieve higher precision, for example, in phase modulation, which in turn allows for an improvement in the quality of the reflected signal and a reduction in BER. However, it is also important to note that there are practical limits associated with increasing \(n_{m}\). Among these limits are the complexity of the implementation, the amount of resources required, and the ability to control the phases in real-time.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 4 Discussion  
### Better Quality Reference Image  
Usually, the real image as a reference image used in image registration can not be obtained in a real video system. So, the most important step in image registration is the selection of the reference image, as mentioned in the introduction, the mean or the sharpest frame of the image sequence is usually used as the reference image in the past. However, if the mean frame is so blurred that it causes feature loss in the image, while the sharpest frame has severe geometric distortion that cannot be ignored, the registration is seriously restricted. In order to solve the problem, [PERSON] selected the mean of an image sequence as the reference and blurred the sequence frames using a blur kernel estimated from the sequence. When the reference image and sequence are at the same blur level, the whole registration process is guided to the sharper region. However, in the [PERSON] method, the blurred frames tend to introduce unexpected local image quality deterioration. Some edges in sequence are blurred and shifted because of joined frame blurring, and the same regions in the mean are also ambiguous and warped. When the registration process of these blurred regions is directed to other regions, and the registration of the boundary is sacrificed. It should be noted that the loss and confusion of edge information are probably irreversible, and the distortion and misalignment may be aggravated with the increase of iterations.  
In the proposed method, reconstruction of the higher quality reference frame is performed at first. The reference frame is obtained through discarding severely distorted parts and enhancing the edge. Compared with the mean frame, the reconstructed reference frame can greatly avoid introducing artificial misalignment, and its pixel points are closer to their real positions. Then, the registration is guided to the sharper boundaries with the preserve and enhancement of edge. The comparison between the reconstructed reference image with the mean reference image used in other methods described above, is shown in Figure 4. It is clear that the reference processed by the proposed method has more distinct letters edges.  
Figure 3: The running time at each iteration of different methods.
# Design and Evaluation of Face Mask Filtration: Mechanisms, Formulas, and Fluid Dynamics Simulations
## 2 Modelling
### Brownian Diffusion  
Brownian diffusion is one of the mechanisms by which aerosols penetrate facemasks. Filtration diffusion is based on the random Brownian motion of small particles (diameter less than 0.2 \(\upmu\)m) from regions of high concentration to regions of lower concentration. This mechanism is predominant at low air flow velocities. For a face mask, the single-fiber efficiency (E\({}_{D}\)) is given by the equations below.  
\[E_{D}=2\cdot Pe^{-2/3} \tag{1}\]  
where:  
\[Pe=\frac{d_{f}\cdot\iota_{0}}{D} \tag{2}\]  
\[D=\frac{kTC_{C}}{3\pi\mu d_{p}} \tag{3}\]  
Pe is the Peclet number, d\({}_{i}\) is the fiber diameter, U\({}_{0}\) is the face velocity, D is the diffusion coefficient, k is the Boltzmann constant, T is the temperature of the fluid, \(\mu\) is the dynamic viscosity of the fluid, d\({}_{p}\) is the particle's diameter, and C\({}_{c}\) is the Cunningham correction factor [20; 21].
# Improving Moving Insect Detection with Difference of Features Maps in YOLO Architecture
###### Abstract  
Insect detection under real-field conditions remains a challenging task due to factors such as lighting variations and the small size of insects that often lack sufficient visual features for reliable identification by deep learning models. These limitations become especially pronounced in lightweight architectures, which, although efficient, struggle to capture fine-grained details under suboptimal conditions, such as variable lighting conditions, shadows, small object size and occlusion. To address this, we introduce the motion module, a lightweight component designed to enhance object detection by integrating motion information directly at the feature map level within the YOLOv8 backbone. Unlike methods that rely on frame differencing and require additional preprocessing steps, our approach operates on raw input and uses only two consecutive frames. Experimental evaluations demonstrate that incorporating the motion module leads to consistent performance improvements across key metrics. For instance, on the YOLOv8n model, the motion module yields gains of up to 5.11% in mAP50 and 7.83% in Recall, with only a small computational overhead. Moreover, under simulated illumination shifts using HSV transformations, our method exhibits robustness to these variations. These results highlight the potential of the motion module as a practical and effective tool for improving insect detection in dynamic and unpredictable field scenarios.  
object detection; deep learning; YOLOv8; motion enhancement Article
# The Key Role of Laser Ultrasonics in the Context of Sustainable Production in an I 4.0 Value Chain
## 3 Quality Control Using In-Situ Methods
### In-Situ Methods for R&D Purposes
#### 3.2.3 In-Situ Methods Based on Synchrotron Radiation  
The possibility to perform in-situ material characterization with High Energy X-rays opens up a great potential to investigate near-surface microstructural changes and to be able to investigate rapidly occurring processes in real time, due to high resolution capabilities at high measurement frequencies. One possibility involves implementing a TMTS, for example, in the form of a dilatometer that has windows mounted to transmit the High-Energy XRD (HEXRD) beam through the mounted specimen. The diffraction effects of the material can then be detected in complete Debye-Scherrer rings on a flat panel detector. Using such an arrangement, a variety of changes in microstructure (similar to HT-XRD) can be elicited simultaneously at high temperatures, and by using a deformation dilatometer, conclusions can also be made about the behavior during simultaneous deformation [85; 86; 87].  
The main advantage of this method is that the high-energy radiation does not limit the investigation to the surface, but larger bulk materials can be investigated.  
Another investigation method worth mentioning, which is often carried out with synchrotron radiation is Small-Angle X-ray Scattering. Investigations based on this principle provide information on the size distribution and precipitation or dissolution kinetics of precipitates. These analyses can be performed in-situ rapidly and quantitatively, and with time and spatial resolution [88].
# Deep-Feature-Based Approach to Marine Debris Classification
## 2 Materials and Methods
### Deep Convolutional Architectures  
Compared to traditional computer vision techniques, deep convolutional networks provide better accuracy in image classification tasks. Furthermore, they also provide superior flexibility since they can be retrained using custom datasets and also require less human expert analysis and fine-tuning [49]. This section describes six deep CNN architectures utilized in this work for marine debris classification and extraction of dense image representations.  
The main components repeated through the following architectures are convolutional and spatial pooling layers: max pooling, average pooling, and global average pooling. Convolutional layers convolve the input with the kernel shared across all input's spatial locations to obtain feature maps. The feature map value at position \((i,j)\) obtained with \(k^{th}\) kernel (filter) is calculated as \(z_{i,j,k}=w_{k}^{T}\mathbf{x}_{i,j}+b_{k}\), where \(\mathbf{w}_{k}\) and \(b_{k}\) denote \(k^{th}\) kernel's weight vector and bias term, while \(\mathbf{x}_{i,j}\) denotes the input patch centered at \((i,j)\). The nonlinear activation function \(g:\mathbb{R}\rightarrow\mathbb{R}\) is applied element-wise on obtained feature maps to obtain activations \(a_{i,j,k}=g(z_{i,j,k})\)[20]. The pooling layers aggregate information within feature map \(\mathbf{a}_{k}\) by replacing local pooling regions \(\mathcal{R}_{i,j}\) in \(\mathbf{a}_{k}\) with the maximum element from \(\mathcal{R}_{i,j}\) in the case of max pooling, and with arithmetic mean of elements in \(\mathcal{R}_{i,j}\) in the case of average pooling. On the other hand, global average pooling averages all values in \(\mathbf{a}_{k}\).
# Novel Semantic-Based Probabilistic Context Aware Approach for Situations Enrichment and Adaptation
## 3 Situation-Based Contextual Model: Definitions and Formalizations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Filtering Approaches**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{2}{c}{
\begin{tabular}{c} **Online** \\ **Learning** \\ \end{tabular} } & \multicolumn{1}{c}{**Dataset**} & \multicolumn{1}{c}{**Scalability**} & \multicolumn{1}{c}{**Adaptation**} & \multicolumn{1}{c}{**Flexibility**} \\ \hline \multirow{4}{*}{**Content-based**} & [6] & 63.00\% & ✗ & images & ✗ & ✗ & ✗ \\  & [7] & 70.50\% & ✗ & Movies & ✗ & ✗ & ✗ \\  & [8] & 72.30\% & ✗ & Sc. papers & ✗ & ✗ & ✗ \\ \hline \multirow{4}{*}{**Collaborative-based**} & [11] & 80.70\% & ✗ & Movies & ✓ & Partial & ✗ \\  & [12] & 84.60\% & ✗ & Netflix & ✓ & Partial & ✗ \\  & [13] & 84.60\% & ✗ & Gowalla & ✓ & Partial & ✗ \\  & & 97.30\% & & Book & ✓ & Partial & ✗ \\ \hline \multirow{4}{*}{**Collaborative-based**} & [21] & 83.50\% & ✗ & Movies & ✓ & Partial & ✗ \\  & [22] & 80.00\% & ✗ & Movies & ✓ & ✓ & Partial \\ \hline \multirow{4}{*}{**Flexibility**} & Trust-aware & [18] & 87.84\% & ✗ & Services & ✓ & ✓ & Partial \\  & Location-aware & [24] & 64.50\% & ✗ & Services & ✓ & ✗ & Partial \\  & Time-aware & [25] & 43.40\% & ✗ & Services & ✓ & ✗ & Partial \\  & Spatiotemporal-aware & [15] & 51.00\% & ✗ & Foursquare & ✓ & ✗ & Partial \\  & Role-based & [17] & 63.50\% & ✗ & Epinions & ✓ & ✗ & Partial \\  & Environment-aware & [28] & - & ✗ & Tourism & ✓ & ✓ & ✓ \\  & Mobile-aware & [26] & - & ✗ & Tourism & ✓ & ✓ & Partial \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recommendation approaches.
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 6 Discussion
### Limitations and Challenges  
Despite its strengths, our proposed approach faces some limitations such as data constraints, as the availability of diverse datasets remains limited, especially for rare tumor types. Hardware limitations also pose a challenge, as high computational resources are required for training deep learning models on large datasets. Additionally, there is room for improvement in terms of generalization to unseen data, which could be addressed through techniques such as domain adaptation or transfer learning. Future research should focus on overcoming these limitations by expanding the dataset diversity and optimizing model performance with reduced hardware demands.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 4 Experiments
### Performance Metrics  
This subsection introduces the main performance metrics which are used in the experiments below.  
The evaluation of MOTS performance employs several key metrics. False negatives (FNs) quantify missed detections where true targets are not identified, and false positives (FPs) measure spurious detections of non-existent targets. Lower values for both metrics indicate better detection accuracy. Identity switches (IDSs) count occurrences where target identities are incorrectly swapped, directly reflecting tracking consistency. The MOT accuracy (MOTA) integrates the three factors above as a comprehensive accuracy measure. However, since MOTA has limited sensitivity to identity preservation, researchers typically complement it with the identity F1-score (IDF1) to specifically evaluate ID maintenance capability, which is important in crowded scenarios.  
For segmentation-specific evaluation, MOTS accuracy (MOTSA) and MOTS precision (MOTSP) extend these metrics: MOTSA adapts the MOTA framework using mask areas for error calculation, and MOTSP focuses on geometric precision of segmentation boundaries through mask IoU. To further evaluate segmentation performance, the segmented MOT accuracy (sMOTSA) metric extends MOTSA by incorporating mask quality assessment. Specifically, sMOTSA computes accuracy based on correctly segmented pixels rather than entire masks, so it can provide finer-grained evaluation of segmentation quality.  
All metrics follow ingthe convention that higher values indicate better performance, though their complementary nature must be noted. Low MOTA with high IDF1 may suggest excellent long-term tracking despite short-term detection deficiencies. Thesemetrics collectively enable thorough evaluation across different aspects of tracking and segmentation quality.
# GIS-Based Cluster and Suitability Analysis of Crop Residues: A Case Study in Yangon Region, Myanmar
## 2 Materials and Method
### Research Flow and Data Collection  
This study used ArcGIS Desktop 10.8.1 (Product Version: 10.8.1.14362), developed by Environmental Systems Research Institute (Esri), a company based in California, United States, to perform a GIS cluster and suitability analysis, as shown in Figure 2.  
This study has two categories of data: nonspatial and spatial. The nonspatial data regarding the production number of crop output quantity, electricity access, and water access are collected from Myanmar Information Management Unit [33; 34]. The spatial data in land use and land cover maps, digital elevation model (DEM), road, protected areas, and administrative data are gathered from open satellite images of open data sources from the United States Geological Survey(USGS), Environmental Systems Research Institute (Esri) 2020 landcover and Geofabrik website and Myanmar Information Management Unit website, Table A1, Appendix A.  
Figure 1: Study Area of Yangon Region, Myanmar.
## 5 Evaluation  
### Simulation Setup  
The simulation experiment platform uses NS-2 [28]. In this paper, we verify the proposed mechanism by introducing a three-layer multicast tree structure, and the mechanism is applicable to multicast tree topology of any number of layers. The experimental topology and link bandwidth settings are shown in Figure 2. The propagation delay of the links is set to 10 ms. There are four reliable multicast groups in the network, namely GUMSID1, GUMSID2, GUMSID3, and GUMSID4. The multicast source of GUMSID1 and the multicast source of GUMSID2 are far away from the multicast receiver, and the multicast source of GUMSID3 and the multicast source of GUMSID4 are relatively close to the multicast receiver. Receiver1, receiver4, receiver7, and receiver10 join GUMSID1; receiver2, receiver5, receiver8, and receiver11 join GUMSID2; receiver3 and receiver6 join GUMSID3; and receiver9 and receiver12 join GUMSID4. The rates of the multicast sources of the four multicast groups are initialized to 100 Mbps. When the experiment runs to 0.9s, the rates of the multicast source of GUMSID3 and the multicast source of GUMSID4 become 70 Mbps. To accurately evaluate the performance of the proposed mechanism, all packet losses in the experiments are caused by network congestion.  
Table 1 shows the parameter settings of our simulation experiments. We compared DCAM with AARM [18] and Equal [19] with regard to loss recovery delay, cache hit ratio, transmission completion time, and overhead under different cache strategies (LCE [9],  
Figure 2: The simulation topology.  
CAPC [20], ProbCache [21], and Prob [22]). The parameter settings of CAPC, ProbCache, and Prob refer to [20].  
One of the most important performance indicators of reliable multicast is the loss recovery delay. In order to evaluate the impact of the setting of the weight coefficients \(\alpha\), \(\beta\), and \(\gamma\) on the loss recovery delay, we conducted a large number of experiments under different combinations of weight coefficients. In this experiment, DCAM is used with CAPC cache strategy. As shown in the Figure 3, when the values of \(\alpha\), \(\beta\), and \(\gamma\) are set to 0.3, 0.3, and 0.4, respectively, the DCAM-CAPC combination achieves the smallest normalized loss recovery delay, so all experiments in this paper adopt this setting.
# Pulsed Nanoelectrospray Ionization Boosts Ion Signal in Whole Protein Mass Spectrometry
## 3 Results  
### Pulsed Nanoelectrospray Ionization Increases Signal-to-Background Chemical Noise  
Nanoelectrospray ionization mass spectra were obtained for solutions containing Ubq, Cyt C, Myo, and CAII at concentrations of 3 to 5 \(\upmu\)M. 'Optimal' pulsed nESI mass spectra in terms of their ion abundance were obtained for each test analyte using frequencies and duty cycle values ranging from 10 to 350 kHz and 10 to 90%, respectively. Lower duty cycles were not investigated because no ion signal was readily detected below a duty cycle of 10%. In general, pulsed nESI resulted in the same type of ions being formed as that for direct current nESI as expected; i.e., protonated ions were readily formed in both pulsed and direct current nESI-MS in comparable charge states (Figure 2). However, at the optimal frequencies and duty cycles, pulsed nESI-MS resulted in an increase in ion signal and a decrease in background chemical noise (\(N_{\text{C}}\)) levels. For example, the average ion signal for Ubq, Cyt C, Myo, and CAII increased by 12, 62, 82, and 37% respectively using pulsed nESI-MS compared to when direct current nESI-MS was used (Figure 2). The \(N_{\text{C}}\) also decreased for all samples by up to 46%. Such an increase in the absolute ion abundance and decrease in \(N_{\text{C}}\) by pulsed nESI resulted a significant increase in \(S/N_{\text{C}}\) across all samples by up to 154% compared to when direct current nESI-MS was used (Figure 2; Table 1).
## 5 Results
### Results from Testing Predictive Models  
The results from the training process, specifically the graphs illustrating the degree of fit between the output values and targets during the training of selected predictive models (as specified in Table 2), are presented in Figures 3-7.  
The first tested model, Net1, aimed to predict target values for the indicators RMA1, RMA2, RMA3, and ADL.  
The training results for each of the models are presented as follows:  
* Net1:
* During training, a very high fit score of R = 0.99998 was obtained, indicating excellent adaptation of the model to the training data.
* During validation, the result was R = 0.8665, meaning the model performed slightly worse on unseen data but still maintained a high level of fit.
* A result of R = 83,381 for the test set suggests potential overfitting to the training data, possibly leading to poorer generalization to new data.  
* Net2: \(\odot\) The training result was 0.50367, indicating a moderate fit of the model to the training data. During validation, the result was R = 0.77451, meaning the model performed better than Net1 on new data, but there are still areas for improvement. A result of R = 0.50028 for the test set suggests that the model struggles on new data, indicating a generalization problem.
* Net3: \(\odot\) The model achieved a training result of 0.67851, suggesting moderate fit. \(\odot\) Validation result was 0.62192, indicating similar model performance on validation data as on training data. A test set result of R = 0.73999 is fairly good, suggesting the model has the ability to generalize to new data.
* Net4: \(\odot\) The model achieved a training result of 0.49892, indicating moderate fit. The validation result was 0.33994, suggesting that the model does not perform well on new data, possibly due to overfitting on training data. A test set result of R = 0.55212 suggests moderate model generalization capability.
* Net5: \(\odot\) The model achieved a very high training result of 0.87627, indicating a strong fit to the training data. The validation result was 0.95191, suggesting the model performs well on new data. However, a test set result of R = 0.0 is concerning and may indicate a problem with the model or the test data.  
None of the models tested yielded satisfactory results during the training process, which should be explicitly noted: these results relate to the so-called training process. Nevertheless, the assessment of simulation results, as part of the subsequent steps of the presented methodology (Section 4.1), provided interesting findings.  
Figure 3: Regression function for the learning (training, validation, and testing) process for model Net1.  
Figure 4: Regression function for the learning (training, validation, and testing) process for model Net2.  
Figure 5: Regression function for the learning (training, validation, and testing) process for model Net3.  
Figure 6: Regression function for the learning (training, validation, and testing) process for model Net4.  
Figure 7: Regression function for the learning (training, validation, and testing) process for model Net5.