esa-sceva/satcom-chunk-collection huggingface dataset:
# Seeing the Unseen: Real-Time Micro-Expression Recognition with Action Units and GPT-Based Reasoning
## 4 Results  
### Performance Metrics  
The system's performance was evaluated using the following metrics: accuracy--proportion of correct predictions, precision, recall, and F1-score--for each emotional class, and confusion matrix--for detailed analysis of the classification errors.  
Our system demonstrates a solid overall performance, with an accuracy of 93% across all emotion categories. The weighted average metrics (precision: 0.96, recall: 0.93, F1 score: 0.94) indicate a robust performance, taking into account the non-uniform distribution of the sample data. However, the macro average (precision: 0.85, recall: 0.94, F1 score: 0.86) revealed some variability in performance across different emotion categories, suggesting that certain emotions are detected more reliably than others.  
The detailed performance for each emotional class existing in the CASME II dataset is presented in Table 4.  
The system particularly excelled in detecting fear, with almost perfect metrics (precision: 0.99, recall: 1.00, F1 score: 0.99) for 86 samples, making it the most reliably detected emotion. Disgust and repression also showed an impressive performance, with F1-scores of 0.93 and 0.98, respectively. Happiness detection was very precise, with perfect precision (1.00) and high recall (0.94).  
The high recall values for most categories (five out of seven categories had a recall \(\geq\)0.94) indicate that the system rarely missed instances of these emotions when present, the most important aspect for real-time applications that require high sensitivity.  
The most significant challenge lies in detecting sadness, which presents a perfect recall but an extremely low precision (0.30). This imbalance suggests that the system frequently classified other emotions as sadness, leading to many false-positive results. However, the very small sample size (only three instances) makes these metrics statistically unreliable and requires larger validation datasets.  
The detection of surprise showed moderate precision (0.68) despite a perfect recall, indicating some confusion with other emotions. The \"Others\" category maintained perfect precision but a lower recall (0.75), suggesting that some non-standard expressions are incorrectly classified into specific emotion categories.
# Big Data Integration in Remote Sensing across a Distributed Metadata-Based Spatial Infrastructure
## 6 Conclusions  
In view of the current issues of remote sensing data integration and management, this paper proposed an OODT and SolrCloud-based data integration and management framework. Specifically, aiming at heterogeneous features of multi-source remote sensing data, we proposed an ISO 19115-2:2009-based metadata transform method to achieve the unity of metadata format in the distributed sub-centers. In order to achieve efficient, stable, secure and usable remote sensing data integration across a distributed data center spatial infrastructure, we adopted the OODT framework based on its stable, efficient, and easy-to-expand features, to implement remote sensing data polling, thumbnail generation, file transfer, thumbnail archiving, metadata storage, etc. Finally, for efficient retrieval problems of integrated massive data, we proposed the LSI model-based data organization approach, and took SolrCloud to realize the distributed index and quick retrieval of metadata. In addition, in order to verify the availability of our proposed program, a series of distributed data integration, retrieval, and comparative experiments were carried out. The results showed that our proposed distributed data integration and management program was effective and provided superior capabilities. In particular, the LSI model-based data organization and the SolrCloud-based distributed indexing schema could effectively improve the efficiency of massive data retrieval.  
However, in this paper, the unified metadata conversion rule was pre-configured, and the metadata transformation was done manually. This was convenient and easy to operate, but less efficient. In particular, with an increase of data types, a great burden would be brought to data integration. Future studies based on deep learning algorithms using semantic matching and unified format conversion of remote sensing metadata will be performed. In addition, in this paper, the GeoSOT code length of each remote sensing image was calculated according to the image swath. This calculation is easy, and the obtained GeoSOT code is not very long. These relatively short GeoSOT codes could not bring a heavy query burden. However, despite this disadvantage, these relatively short GeoSOT codes, to a certain degree, have reduced query accuracy. Thus, future work will be focused on exploring a suitable GeoSOT code length calculation method, such as introducing the feedback control theory [48; 49; 50; 51; 52] to calculate GeoSOT code length of each type remote sensing image, so that neither the query efficiency nor accuracy will be affected.  
This work is supported by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUG170689) and the National Natural Science Foundation of China (No. 41471368, No. 41571413). All co-authors of this manuscript significantly contributed to all phases of the investigation. They contributed equally to the preparation, analysis, review and editing of this manuscript.  
No potential conflict of interest was reported by the authors.
## 3 System Functional Modules
### Implementation of Software and Hardware Watchdog  
The 2.4G wireless network is severely influenced by the outside environment, and may be unstable. To ensure the system work normally, software and hardware watchdogs are designed.  
(1) Software watchdog: when the CPU timer works normally, but the program works abnormally, the software watchdog converts the operation into restart mode by the timer, so that the system can restart automatically.  
(2) Hardware watchdog: as an individual component, the timer works in a way of interruption, when the operation of main program goes wrong, the interrupted program can still operate normally to guarantee that the timer can work normally (Figure 14).  
**Figure 14.** Flow chart of software watch-dog system, and circuit design of hardware watch-dog.  
The processing flow chart  
Electric circuit design drawing
## 2 Materials and Methods
### Cost Function
#### 2.2.1 Entry Region and Target Point  
Every trajectory has a defined starting point and an endpoint. For this particular application, the neurosurgeon specifies a fixed target point to be reached and an anticipated entry region. This region is subdivided into smaller sub-regions, from which the potential insertion point will be evaluated. In order to obtain the entry points, the information from the T1w images and atlas-based segmentation is used to avoid the selection of placed points occurring near sulci, blood vessels, or other anatomical structures. Based on the calculated entry angle (\(\alpha\)) at each point in the region, a weight is assigned, prioritizing points where  
Figure 2: (**a**) Paths generated by the RRT algorithm in 2D. After being evaluated with the cost function, the shortest path (distance) (dashed red line) and the path with the lowest cost (blue line) are highlighted. (**b**) Spatial configuration constraints for RRT path-planning, where \(i\) corresponds to the number of obstacles in the space, \(j\) is the number of elements in the trajectory, \(rc\) is the radius around each obstacle, \(\alpha\) is the entry angle measured from the tangent line on the skull, and \(d\) is the distance to each element, \(j\).  
an optimum angle (\(\alpha_{m}\) or 90\({}^{\circ}\)) is obtained. \(t_{A}\) is the term that evaluates the convenience of the entry angle, where the minimum cost for this term would be 1 when the entry angle and the optimum angle are equal, penalizing inclined angles greater than (\(\alpha\)):  
\[t_{A}=\alpha/\alpha_{m} \tag{1}\]
# Machine Learning-Based Detection of Cognitive Impairment from Eye-Tracking in Smooth Pursuit Tasks
## 2 Materials and Methods
### Assessment Procedures
#### 2.2.3 Eye-Tracking  
Eye-tracking recordings were carried out using a Tobii 4C eye-tracker (produced by Tobii AB, Stockholm, Sweden) operating at 90 Hz, alongside proprietary software developed by NEUS Diagnostics, d.o.o. (Ljubljana, Slovenia), and supporting hardware, including an examiner's laptop and a 23.6-inch monitor (1920 \(\times\) 1080 resolution) for participant viewing. A trained technician oversaw the procedure. The participants were seated approximately 70 cm from the screen and interacted with the tasks solely through eye movements, without physical contact with the equipment. The NEUS software presented visual tasks and guided participants through the session, simultaneously capturing and storing gaze data for subsequent analysis. All data were anonymised and made accessible only to authorised researchers.  
The smooth pursuit examination included six different tasks. In each task, the patient was asked to follow a computer-based moving dot. The dot moved in a sinusoidal pattern, either vertically or horizontally in three different speeds: slow (period of 4800 ms, frequency of \(\approx\)0.21 Hz), medium (period 2400 ms, frequency \(\approx\) 0.42 Hz), and fast (period 1600 ms, frequency \(\approx\) 0.625 Hz). Each task was 10 s long. Figure 1 shows our experimental flowchart as seen from the participant's point of view. There were no other graphical elements on the participant's screen during the test.  
Before the test begins, participants listen to simple instructions explaining that they should carefully follow the movement of the dot using only their eyes. Each task starts with the dot appearing at the centre of the screen. It then begins to move across the screen (either vertically or horizontally), and when the dot stops moving, a blank black screen is shown. The first task is always at a slow speed, followed by medium, and finally the fast version. The entire set of tasks is repeated three times. Figure 2 shows an example of eye movements from a healthy control while performing the medium-speed smooth pursuit task.
# One-Class LSTM Network for Anomalous Network Traffic Detection
## 5 Experimental Results and Discussion
### Discussion  
In this study, we conducted experiments on three public datasets, and the proposed OC-LSTM algorithm achieved excellent results compared with other methods. As mentioned, on the NSL-KDD dataset, based on the total of all types of attacks, the method proposed in this paper achieved excellent performance, reaching 96.86% \(\pm\) 0.58% and 96.72% \(\pm\) 0.34% on the AUROC and AUPRC metrics, respectively. However, in the single-category classification, the IF algorithm obtained 99.20% \(\pm\) 0.13% and 96.08% \(\pm\) 0.74% on the AUROC and AUPRC metrics, respectively, when the data type was Probe. This shows that this shallow network model was not accurate in identifying all the data types. However, it achieved better results than our algorithm for the Probe data type. When the data type was R2L, the soft-bound deep SVDD algorithm achieved 92.92% \(\pm\) 2.22% on AUROC, and the one-class deep SVDD achieves 66.82% \(\pm\) 8.01% on AUPRC.  
The OC-LSTM algorithm proposed in this paper achieved excellent results in relation to all data types on the other two data sets. However, there were also deficiencies in terms of specific data types. This shows that the algorithm still has room for improvement in identifying specific features. Furthermore, in designing a network anomaly detection system, to avoid this from happening, we adopted the form of an ensemble network. Some of the comparison methods and the OC-LSTM employed in this study were integrated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**MAWILab-20200102**} & \multicolumn{2}{c}{**MAWILab-20201203**} \\ \cline{2-5}  & **AUROC** & **AUPRC** & **AUROC** & **AUPRC** \\ \hline OC-SVM/SVDD & 90.66 \(\pm\) 0.23 & 83.59 \(\pm\) 0.41 & 87.58 \(\pm\) 0.30 & 80.19 \(\pm\) 0.51 \\ IF & 94.58 \(\pm\) 0.42 & 90.43 \(\pm\) 0.86 & 89.35 \(\pm\) 1.04 & 86.51 \(\pm\) 1.29 \\ KDE & 92.17 \(\pm\) 0.21 & 86.81 \(\pm\) 0.55 & 89.81 \(\pm\) 0.24 & 85.17 \(\pm\) 0.31 \\ DCAE & 94.78 \(\pm\) 1.04 & 92.49 \(\pm\) 1.40 & 92.50 \(\pm\) 1.31 & 90.45 \(\pm\) 1.53 \\ OC-NN & 96.42 \(\pm\) 0.30 & 94.90 \(\pm\) 0.48 & 93.96 \(\pm\) 0.21 & 92.71 \(\pm\) 0.51 \\ Soft-Bound Deep SVDD & 94.89 \(\pm\) 0.73 & 91.49 \(\pm\) 1.46 & 90.96 \(\pm\) 2.90 & 88.53 \(\pm\) 1.60 \\ One-Class Deep SVDD & 95.00 \(\pm\) 0.73 & 91.70 \(\pm\) 1.26 & 91.79 \(\pm\) 2.30 & 90.04 \(\pm\) 1.80 \\ OC-LSTM & **97.36 \(\pm\) 0.49** & **96.70 \(\pm\) 0.72** & **94.58 \(\pm\) 0.47** & **93.23 \(\pm\) 0.64** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average AUROC and AUPRC values as percentages with StdDevs per method on MAWILab.  
in the server, and different weights were assigned to them for joint training. In this way, the advantages of various methods could be integrated, thereby greatly improving the accuracy and usability of anomaly detection of the system.
## 4 Results and Discussion
### Influence of the Geometrical Parameters  
This section investigates the impact of geometrical parameters on BC/BC\({}_{\rm NV}\), utilizing a \(c\)-\(\phi\) soil with \(c=10\) kPa and \(\phi=30\) degrees for analysis. Figure 3 presents the influence of footing width \(B\) on BC/BC\({}_{\rm NV}\), revealing a decreasing trend along the distance \(r/D\) as the footing width changes from 1 m to 2 m. The most significant difference is noted at \(r/D=0\), gradually diminishing at distant locations. Figure 3 illustrates the difference in BC/BC\({}_{\rm NV}\) resulting from cavity shape, with a notably higher value observed for a circular cavity. The smooth stress distribution around the circular cavity leads to a less pronounced impact on the bearing capacity compared to cases with square cavities. Kuwano and Ohara [26] highlighted a substantial decrease in the bearing capacity of a footing above square or rectangular cavities. Additionally, Figure 3 emphasizes the substantial difference in BC/BC\({}_{\rm NV}\) values when the cavity is directly beneath the footing (\(r/D=0\)) and as it progressively shifts to normalized horizontal distances \(r/D\).  
Figure 2: Comparison of the variation of the bearing capacity with the depth to cavity obtained by RPFEM and the study by [PERSON] and [PERSON] [8]. (**a**) \(D/B=1\); (**b**) \(D/B=2\).  
In Figure 3c, the influence of cavity diameter \(D\) on BC/BC\({}_{\rm NV}\) is explored, revealing noticeable variations with increased \(D\). Figure 3d depicts the effects of cavity depth normalized to footing width \(B\) as \((h\)-\(D/2)/B\) on BC/BC\({}_{\rm NV}\). Within the range \((h\)-\(D/2)/B=2\) to \((h\)-\(D/2)/B=4\), significant BC/BC\({}_{\rm NV}\) variations are evident for various normalized horizontal distance values \((r/D)\). Smaller \(r/D\) values correspond to steeper slopes between \((h\)-\(D/2)/B=2\)-4. In summary, the considered parameters collectively exert a noticeable impact on Bearing Capacity behavior. Notably, when the cavity is sufficiently distant from the footing, BC/BC\({}_{\rm NV}\) tends toward 1, indicating the presence of a critical line that elucidates an influence zone.
## 3 Application of Functionality Loss Evaluation Model for Railway Network Components  
### Construction and Visualization of the Railway Network Model  
To assess the applicability of the functionality loss evaluation model for railway network components, a hypothetical railway network system was constructed. The hypothetical railway network system was assumed to be a branching system rather than a single line, referencing various railway systems in Korea and abroad. The proposed network system for evaluation was designed with three lines, allowing different routes to remain operational depending on the damaged sections, thereby increasing the diversity of network impact scenarios based on functionality loss. Line 1 spans 29 km and includes 10 railway components (three railway bridges, four embankment sections, and three tunnels); Line 2 spans 11 km and includes four railway components (one railway bridge, two embankment  
Figure 9: Annual exceedance probability for railway structures. (**a**) Annual exceedance probability for railway bridge; (**b**) annual exceedance probability for railway embankment (Soft ground); (**c**) annual exceedance probability for railway embankment (stiff ground); (**d**) annual exceedance probability for railway tunnel.  
sections, and one tunnel); and Line 3 spans 15 km and includes six railway components (one railway bridge, four embankment sections, and one tunnel). To define fragility functions for embankment and tunnel sections, parameters such as embankment height, subsoil stiffness, and tunnel depth were assumed to ensure diversity in functionality loss levels. The assumed input conditions are shown in Table 5.  
The hypothetical railway network proposed in this study for applying the functionality loss evaluation model for railway network components is visualized in Figure 10. Additionally, although actual railway networks are much more diverse and complex than the railway network proposed in this study, a simplified design was adopted to facilitate a more convenient evaluation of the functionality assessment process for structures. Railway bridge sections are displayed in gray, while tunnel sections are displayed in black. Additionally, embankment sections on soft ground are marked in light yellow, and those on stiff ground are marked in orange.  
Figure 10: Seismic resilience value for an earthquake scenario.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Height (Embankment)/Depth (Tunnel)** & **Ground Stiffness** \\ \hline Embankment 1 & 2 & Stiff ground \\ Embankment 2 & 4 & Soft ground \\ Embankment 3 & 6 & Stiff ground \\ Embankment 4 & 2 & Soft ground \\ Embankment 5 & 4 & Stiff ground \\ Embankment 6 & 6 & Soft ground \\ Embankment 7 & 2 & Stiff ground \\ Embankment 8 & 4 & Soft ground \\ Embankment 9 & 6 & Stiff ground \\ Embankment 10 & 4 & Soft ground \\ Tunnel 1 & shallow & \\ Tunnel 2 & deep & \\ Tunnel 3 & shallow & \\ Tunnel 4 & deep & \\ Tunnel 5 & deep & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Structure condition for embankment and tunnel.  
Application of Functionality Loss Evaluation Model for Structures Under Earthquake Scenarios: Case 1
## 3 Results  
### Apigenin Showed No Significant Effects on Cell Viability and Morphology of hODs  
The exposition to increasing concentrations of APG (5, 10 and 20 \(\mu\)M) for 24 h resulted in no cytotoxicity for osteoblasts (Figure 1).  
APG at 5 \(\mu\)M produced a lower level of LDH release compared to untreated cells (CTRL) and compared to the other concentrations. However, the result for 5 \(\mu\)M was not statistically significant. The results of LDH assay were confirmed from the analysis of morphology, which was investigated only for the concentration of 5 \(\mu\)M (5-APG) at 24 h, because LDH release showed no differences at higher concentrations 10 and 20 \(\mu\)M. Microscopically no morphological differences (Figure 2B) were seen in comparison to the control cells (Figure 2A). As illustrated in Figure 2, cells appeared spindle shaped and well spread, better seen at higher magnification 40\(\times\) (Figure 2C,D).  
\begin{table}
\begin{tabular}{c c c} \hline
**Gene** & **Forward Primer (5\({}^{\prime}\)-3\({}^{\prime}\))** & **Reverse Primer (5\({}^{\prime}\)-3\({}^{\prime}\))** \\ \hline ALP & AATAGGTGACCATCATGG & GCACCCCAAGACCTGCTTAT \\ OCN & TCAGCCAACTGCTCCACAGTC & GGCGCGTACCTGATCAATG \\ COL1 & AGTCAGAGTGAGGACAGTGAATTG & CACATCACACAGGAAAGTGC \\ \(\beta\)ACT & CCAGAGGCGTACACAGGATAG & GAGAAGAGACCAGGACTCTC \\ \hline \end{tabular}
\end{table}
Table 1: Primer sequences used in RT-qPCR.  
Figure 1: Effects of APG treatment (24 h) on LDH release. Values represent mean \(\pm\) SD expressed in percentages compared to control (CTRL).
## 1 Introduction
They will likely be used as sensors themselves, and as data relays for other nearby devices with more limited connectivity, for example health sensors in a personal area network [15] or domotic sensors and actuators in a home automation environment [12]. The use of smartphones for transmission of sensing data, their own or relayed, can lead to faster battery depletion undesirable for the users, as they use their smartphones mainly for other purposes, like phone calls, SMS, or Web browsing. This article focuses on mobile M2M communications, considering their impact on devices with limited capabilities and constraints. Other M2M communications' surveys focus mainly in home networks [16], 3 GPP Long-Term Evolution (LTE) networks [17], service platforms [18], or data mining techniques [19]. Although wireless and cellular networks are commonly addressed in surveys and literature, we center the theme of this manuscript on mobile communications, and we emphasize the importance of resource usage efficiency, which can be further enhanced by the use of gateway devices. Additionally, we combine a concise review of standards and protocols related to M2M communications, which can serve as an initial starting point for researchers new to the field, or anyone that wants to have a general overview of the state of the art. This document makes the following contributions:  
* we survey the literature and structure current open challenges;
* we review standardization activities, architecture and common application-layer protocols, namely Constrained Application Protocol (CoAP) and Message Queuing Telemetry Transport (MQTT);
* we perform a preliminary study on the impact of using smartphones as entities that collect and aggregate information from sensors on their battery consumption. This document is organized as follows: An extensive survey of the literature work and challenges are presented in Section 2. The reference architectures for M2M are presented in Section 3, and ETSI architecture is detailed in Section 4. In Section 5 we discuss M2M communication models and paradigms, and in Section 6 we detail two application protocols, CoAP and MQTT. Section 7 studies the feasibility of smartphones as gateways, and we conclude the document in Section 8.