esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 3 Methodology
### Light-Weight Runtime Moving Object Detection in the Video Compressed-Domain  
As mentioned in Section 2.1, the aim of this method is to analyze only MVs at the edge device, recognize the real moving objects, and exclude the noisy motions that are generated by environmental factors such as illumination changes and background movement, in the current video scene. For this purpose, we present a method that has the workflow shown in Figure 6 to analyze the video coding MVs. In order to extract MVs from the compressed bitstream, the input video stream is partially decoded, and the collected MVs include both real object MVs and noise MVs. Examples of extracted MVs are represented in Figure 7 in consecutive frames of two different video test sequences. To analyze these MVs, the following functionalities are involved.  
Figure 6: Workflow of the proposed detection-based tracking approach in the compressed-domain. MV, motion vector.
Sensors_**2010**, _10_, 5233-5262; doi:10.3390/s100505233  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**NEURON: Enabling Autonomicity in Wireless Sensor Networks**  
**[PERSON] 1,2,*, [PERSON] 1, [PERSON] 2,**  
**[PERSON] 1 and [PERSON] 1**  
1 National Technical University of Athens, Heroon Polytexneiou, 15773, Zografou, Greece;  
E-Mails: [EMAIL_ADDRESS] (P.G.); [EMAIL_ADDRESS] (G.M.); [EMAIL_ADDRESS] (N.M.)  
2 Greek Research and Technology Network, Av. Mesogion 56, 11527, Athens, Greece;  
E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +30-210-7474255; Fax: +30-210-7474490.  
_Received: 1 February 2010; in revised form: 1 April 2010 / Accepted: 14 May 2010 / Published: 25 May 2010_  
**Abstract:** Future Wireless Sensor Networks (WSNs) will be ubiquitous, large-scale networks interconnected with the existing IP infrastructure. Autonomic functionalities have to be designed in order to reduce the complexity of their operation and management, and support the dissemination of knowledge within a WSN. In this paper a novel protocol for energy efficient deployment, clustering and routing in WSNs is proposed that focuses on the incorporation of autonomic functionalities in the existing approaches. The design of the protocol facilitates the design of innovative applications and services that are based on overlay topologies created through cooperation among the sensor nodes.  
**Keywords:** wireless sensor network; autonomicity; energy efficiency; clustering; hierarchical routing; p2p; overlay; NEURON
## 3 Research Results and Discussion
### Maximum of the Criterion Function--General Analysis
In order for the inequalities \(\alpha<0\), \(\beta>0\), \(\gamma<0\) to be true, the following conditions must be defined:  
* The coefficient \(\alpha\) is defined with the formula: \[\alpha=P_{12}\ ET_{2}\ (z_{2}-z_{1})+P_{12}\ p_{23}\ ET_{3}\ (z_{3}-z_{1})-P_{12}\ ET_{4}\ (z_{4}-z_{1})\] (17) The inequality \(\alpha<0\) is equivalent to the inequality: \[P_{23}\ ET_{3}>ET_{2}\ (z_{2}-z_{1})/(z_{1}-z_{3})-ET_{4}\ (z_{4}-z_{1})/(z_{1}-z_{3})\] (18)
* The coefficient \(\beta\) is defined with the formula: \[\beta=ET_{4}\ (z_{1}-z_{4})\] (19) On the basis of the taken assumption Z1, it results that \(\beta>0\). * The coefficient \(\gamma\) is defined with the formula: \[\gamma=P_{12}\ ET_{2}\ (z_{2}-z_{4})\ ET_{4}+P_{12}\ p_{23}\ ET_{3}\ (z_{3}-z_{4})\ ET_{4}\] (20) The inequality \(\gamma<0\) is equivalent to the inequality: \[P_{23}\ ET_{3}>ET_{2}\ (z_{2}-z_{4})/(z_{4}-z_{3})\] (21)  
With reference to the coefficient \(\gamma\), two cases can be considered. In the first case, based on the assumption Z5: \(z_{3}>z_{4}\) and considering the additional assumption Z6: \(z_{2}>z_{4}\), the inequality \(\gamma<0\) is true. The relationship between the average values of the times ET\({}_{2}\) and ET\({}_{4}\) and between the unit costs z\({}_{2}\) and z\({}_{4}\) is unknown. This is because it is difficult to clearly describe the relationship between the status of minimal repair (repair by Technical Emergency Service) and the condition of preventive replacement. In the second case, when assumption Z6 cannot be taken into account, then inequality in Equation (21) should be considered in the same way as inequality in Equation (18) which is related to the coefficient \(\alpha\). Then, we denote the right sides of inequalities (18) and (21) as \(\delta 1\) and \(\delta 2\). If \(\delta=\max\{\delta 1,\delta 2\}\), then the condition p\({}_{23}\) ET3 \(>\delta\) and Formulae (15), (18), (20) and (21) cause inequalities \(\alpha<0\), \(\gamma<0\). Therefore, we can write that:  
**Conclusion 1**. : _If \(p_{23}>\delta/ET_{3}\), then the inequalities \(\alpha<0\), \(\gamma<0\) are true._
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 2 Materials and Methods
### Model Evaluation Metrics  
For the models' evaluation, the accuracy and F1 score metrics were used. As shown in Equation (1), accuracy is the ratio of the correct predictions over the sum of all predictions (correct and wrong). In the following equation, TN, TP, FN, and FP represent the true negative, true positive, false negative, and false positive values in the models' confusion matrix. F1 score, on the other hand (Equation (2)), is the harmonic mean of precision (TP over TP and FP) and recall (TP over TP and FN) and provides a balance between these two metrics.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
Figure 8: Architecture of the overall system design, comprising the DL models, the RPi4/UGV hardware, and the IoT output decision.  
\[F1\;score=\frac{2(Precision\cdot Recall)}{Precision+Recall} \tag{2}\]  
Accuracy was decided as the evaluation metric for this paper as it is a widely used metric. Accuracy, though, assumes a somewhat balanced distribution of the training examples throughout the different classes in order to give a representative evaluation on the models' performance. In this study, after the data augmentation that occurred, all six classes contained almost the same number of images (the deviation between the images of each class was, at maximum, 3%). The harmonic mean is used to compute the F1 score because it gives more weight to lower values. This means that, if either precision or recall is low, the F1 score will be significantly affected, encouraging a balance between the two metrics. This makes it robust in imbalanced class distribution. F1 score in this work is used in order to validate the accuracy results as both metrics are expected to have similar outcomes.  
Furthermore, the evaluation of the overlap between predicted segmentations was performed using the Intersection over Union (IoU), also referred to as the Jaccard index. This was computed to measure the degree of intersection between the predicted and actual segmentations, as described in Equation (3).  
\[IoU=\frac{TP}{TP+FN+FP} \tag{3}\]
# Enhancing Task-Oriented Dialogue Modeling through Coreference-Enhanced Contrastive Pre-Training
## 4 Experiment
### Main Results and Analysis
#### 4.4.3 Dialogue State Tracking  
Dialogue state tracking tasks commonly employ the joint accuracy and slot accuracy as evaluation metrics. Table 4 provides a comparison between the mainstream models, including GPT2, BERT, TOD-BERT, and our proposed model, on the MultiWOZ2.1 dataset, showcasing our model's superior performance. In contrast to TOD-BERT-jnt, our model CECPT achieves a 3.1% and 0.3% improvement in the joint accuracy and slot accuracy, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Joint Acc** & **Slot Acc** \\ \hline GPT2 * & 46.2\% & 96.6\% \\ DialoGPT * & 45.2\% & 96.5\% \\ BERT * & 45.6\% & 96.6\% \\ TOD-BERT-mlm * & 47.7\% & 96.8\% \\ TOD-BERT-jnt * & 48.0\% & 96.9\% \\ CECPT & **51.1\%** & **97.2\%** \\ CECPT w/o semantic & 48.4\% & 96.9\% \\ CECPT w/o structure & 49.2\% & 96.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiment results on the dialogue state tracking dataset, MultiWOZ2.1. We present joint accuracy and slot accuracy for the full data setting. Results marked with * are sourced from [10].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**MultiWOZ2.1**} & \multicolumn{2}{c}{**DSTC2**} & \multicolumn{2}{c}{**GSM**} \\ \cline{2-5}  & **Micro-** & **Macro-** & **Micro-** & **Macro-** & **Micro-** & **Macro-** \\  & **F1** & **F1** & **F1** & **F1** & **F1** & **F1** \\ \hline GPT2 * & 90.8\% & 79.8\% & 92.5\% & 39.4\% & 99.1\% & 45.6\% \\ DialoGPT * & 91.2\% & 79.7\% & 93.8\% & 42.1\% & 99.2\% & 45.6\% \\ BERT * & 91.4\% & 79.7\% & 92.3\% & 40.1\% & 98.7\% & 45.2\% \\ TOD-BERT-mlm * & 91.7\% & 79.9\% & 90.9\% & 39.9\% & 99.4\% & 45.8\% \\ TOD-BERT-jnt * & 91.7\% & 80.6\% & 93.8\% & 41.3\% & 99.5\% & 45.8\% \\ CECPT & **91.9\%** & **80.9\%** & **3.9\%** & **43.6\%** & **99.6\%** & **45.9\%** \\ CECPT w/o semantic & 91.5\% & 80.9\% & 93.8\% & 41.2\% & 99.5\% & 45.8\% \\ CECPT w/o structure & 91.8\% & 80.7\% & 93.4\% & 40.4\% & 99.0\% & 45.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test results on three Dialogue act prediction datasets. We choose the micro and macro F1 scores as the task metric. * denotes the experiment performance from [10].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Recall** & **Acc** & **Acc** & **Acc** \\  & **out** & **All** & **in** & **out** \\ \hline GPT2 * [35] & 32.0\% & 83.0\% & 94.1\% & 87.7\% \\ DialoGPT * [36] & 32.1\% & 83.9\% & 95.5\% & 87.6\% \\ BERT * [22] & 35.6\% & 84.9\% & 95.8\% & 88.1\% \\ TOD-BERT-mlm * [10] & 46.3\% & 85.9\% & 96.1\% & 89.5\% \\ TOD-BERT-jnt * [10] & 43.6\% & 86.6\% & 96.2\% & 89.9\% \\ CECPT & **50.1**\% & **87.8**\% & **96.2**\% & **90.6**\% \\ CECPT w/o semantic & 46.2\% & 86.7\% & 95.8\% & 90.0\% \\ CECPT w/o structure & 48.6\% & 86.7\% & 95.3\% & 90.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results on the OOS dataset, one of the largest intent recognition corpora. Models marked with * are sourced from [10].
## 5 Discussion
In our case, more complex statistical modeling was conducted to demonstrate this hypothesis, since statistical hypothesis testing as ANOVA or correlation does not directly imply that user choices can be predicted. A deep learning 2-class model was developed to recognize chosen and non-chosen stimuli based on the gaze time patterns in the final three seconds prior to the decision. We selected this time window based on the previous results of the Gaze Cascade Model (Figure 6). The architecture chosen was an LSTM since it can model the temporal dynamics of a signal. Recent studies used this architecture to recognize the patterns of eye-tracking in VR environments [35]. To evaluate the model, we used an independent test set of 287 decisions. The results demonstrated that the model achieved an overall accuracy of 90.59% when predicting the decision (specifically, the chosen stimuli were correctly identified 59.57% of the time, while non-chosen stimuli were accurately recognized 96.67% of the time). These findings suggest that gaze time can serve as a valuable indicator of decision-making, and the deep learning model has shown accuracy in predicting user choices using eye-tracking metrics. The high accuracy in recognizing non-chosen stimuli indicates that the model can effectively differentiate between selected and non-selected options. Overall, this research highlights the potential of using gaze time analysis and deep learning techniques for understanding and predicting user decisions. Further investigations and refinements to the model could lead to even more accurate predictions, making it a valuable tool for product evaluation and user research in various fields.
# The Impact of Impulsive Traffic on Cellular Internet of Things Network Performance Indicators
## 6 Numerical Assessment  
Let us consider an LTE network cell serving an area containing a large number of telemetry devices that send their measured values in the form of files. The file arrival model is a batch model, with its parameters determined as follows:  
* Set file QoS to \(\pi_{c}=0.05\).
* The transmitted file's size is distributed exponentially, with a mean value of F = 100 bytes.
* The information transmission rate \(u\) given by one virtual channel utilized to service the IoT device's application will be considered to be \(u=100\) kbps.
* The file transfer rate \(C\) that can be offered by the system can be calculated by the relation: \(C=v*u\).
* The time required to serve one file is exponentially distributed with a mean duration equal to \(\mu\), where: \(\mu=u/F\).
* The potential load of files is set at \(a=15\) Erl.
* The relationship \(\lambda=a\mu/b\) yields the parameter of the Poissonian distribution of the group arrival intensity. Consider the following three approaches to file distribution in an incoming group:  
1. First case: each incoming group can contain only one file with a probability equal to \(f_{1}=1\). In this case, \(b=1,\sigma^{2}=0\);
2. Second case: the incoming group can contain several files, which can be equal to \(1,\,2,\,\dots,\,15\), with the same probability for each one \(f_{i}=1/15;\,i=1,2,\dots,15\). In this case, \(b=8,\sigma^{2}=18.6667\);
3. Third case: incoming groups can contain only 1 or 15 files with the same probability for both \(f_{1}=f_{15}=0.5\). In that case, \(b=8,\sigma^{2}=49\).
## 2 Materials and Methods  
### GPP and Land Cover Types  
This study focused on the GPP changes of the Northern Hemisphere and in particular on vegetation. To determine how GPP varied with climate change, land cover types were first obtained from annual time series of the GLASS-GLC land cover dataset. The GLASS-GLC product has a spatial resolution of 5 km, and the data examined in this study covered the period from 1982 to 2015.  
The GLASS-GLC product identifies seven types of land cover [42], namely barren (barren land), snow and ice (snow/ice), all forest types (forest), forest/cropland mosaics and natural herbaceous/cropland mosaics (cropland), natural herbage and herbaceous cropland (grassland), shrubland (shrubland), and shrub/bush tundra and herbaceous tundra (tundra). GLASS-GLC data are characterized by high consistency, high reliability, and a long timespan compared to other land cover products [42]. The present study mainly focused on the changes in vegetation GPP, and therefore, the snow and ice (snow/ice) area was excluded from the analysis. Using land cover data from 1982 to 2015, this study clipped the annual GPP and precipitation data based on the specific grid locations of the vegetation for each year, which can mitigate the influence of vegetation dynamics.  
Global monthly GPP data were obtained from the Oak Ridge National Laboratory Distributed Active Archive Center [43] during the 1982-2016 period. These data were generated based on the widely known Monteith light use efficiency (LUE) equation and were improved with spatially and temporally explicit optimized LUE values derived from selected FLUXNET tower data. The optimized LUE values were extrapolated to a global grid with a consistent resolution of 8 km using multiple explanatory variables representing climatic, landscape, and vegetation factors influencing both LUE and GPP. All of the GPP data are greater than 0 after quality control in the products. The monthly average values for the seasons in each year were calculated for each vegetation type.
# Double-Borehole Superimposed Effect of a New Non-Explosive Directional Rock-Breaking Method
###### Abstract  
Due to the difficulty of creating directional fractures efficiently and accurately with existing non-explosive rock-breaking methods, a directional fracturing technique utilizing a coal-based solid waste expansive agent, termed the instantaneous expansion with a single fracture (IESF), has been developed. IESF can generate high-pressure gases within 0.05-0.5 s and utilize gas pressure to achieve directional rock fragmentation. The rock-breaking mechanisms under double-borehole conditions of conventional blasting (CB), shaped charge blasting (SCB), and IESF were studied by theoretical analysis, numerical simulation, and in situ test. The gas pressure distribution within directional fractures of IESF was determined, and the crack propagation criterion between double-borehole was established. Numerical simulation results indicated that the stress distribution in CB was random. SCB exhibited tensile stress of \(-\)10.89 MPa in the inter-borehole region and \(-\)8.33 MPa on the outer-borehole region, while IESF generated \(-\)14.47 MPa and \(-\)12.62 MPa in the corresponding regions, demonstrating that stresses generated between adjacent boreholes can be superimposed in the inter-hole region. In CB, strain was concentrated along main fractures. SCB exhibited strains of 7 mm and 8 mm in the shaped charge direction, while non-shaped charge directions showed a strain of 1.5 mm. For IESF, strain in the shaped charge direction measured 6 mm, compared to 1 mm in non-shaped charge directions, resulting in superior directional fracture control. In situ test results from Donglin Coal Mine demonstrated that IESF can form superior directional rock-breaking efficacy compared to both CB and SCB, with the average crack rates of 95.5% by IESF higher than 85.0% by SCB. This technique provides a non-explosive method that realizes precise control of the direction of cracks while avoiding the high-risk and high-disturbance problems of explosives blasting.  
**Keywords:** shaped charge blasting; crack propagation; rock damage; roof cutting; stress and strain fields
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Carrier Assignment** & **Carrier Power, Max (dBW)** & **Carrier Power, Min (dBW)** & **Sat. TX Gain** & **ES Type** & **ES Gain (dBi)** & **ES Sidelobe Formula** & **Noise Temp. (K)** \\ \hline
10 MOG7D\({}_{-}\) & 17 & 9 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
100 KG1W\({}_{-}\) & 17 & 9 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
100 KG1D\({}_{-}\) & 23 & 15 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
50 KG1W\({}_{-}\) & 14 & 6 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
50 KG1D\({}_{-}\) & 20 & 12 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
25 KG07W\({}_{-}\) & 11 & 3 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
25 KG07D\({}_{-}\) & 17 & 9 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
5K00G1W\({}_{-}\) & 11 & 3 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\
5K00G1D\({}_{-}\) & 17 & 9 & 34 & MES-1, MES-2 & 4, 12 & ND-EARTH & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The link characteristics of the Omnispace F2 satellite. Based on the information provided above, our study offers a comprehensive interference analysis that focuses on the impact of a 5G NTN consisting of 448 satellites on various satellite communication systems. Specifically, we investigate the interference caused by the 5G NTN on the voice and data communication terminals in the L-band of Immarsat aircraft, as well as on the voice, data, and Internet of Things (IoT) communication terminals of the Omnispace and Lyra satellite systems in the S-band. Based on the interference level results, we estimate the potential performance degradation of these incumbent satellite systems if a 5G NTN is deployed in the n255 or n256 bands. By examining these specific frequency bands, we aim to provide insights into the potential challenges that may arise when integrating 5G NTN into the existing satellite infrastructure.
# Indirect Assessment of Railway Infrastructure Anomalies Based on Passenger Comfort Criteria
## 3 Suspension Systems  
The interaction between the wheels and the railway track infrastructure, be it healthy or with defects, generates vibrations with varying amplitude and frequency. These vibrations can cause damage to both trains and railway track infrastructures; thus, they must be controlled. The train suspension system, constituted of primary and secondary suspensions, fulfils this function by enabling the filtering of the vibrations derived from the vehicle-track dynamic interaction (promoting ride comfort) and controlling the kinematic modes of the bogie (promoting stability). Suspension systems are natural low-pass filters that prevent the transmission of high frequencies from the rail track and wheel to the carbody and seats [39].  
The primary suspension's main goals are to secure stability and guidance, reduce track forces and wear, and improve curve performance. Regarding the secondary suspensions, it suppresses the vibration transmission from the bogie to the carbody and attenuates vehicle vibrations derived from railway track irregularities, improving ride comfort and controlling quasi-static motion [39; 40; 41].  
Although with the same goal, different trains have different suspension systems, which interfere with the filtered ranges of frequencies. The present research conducted experimental tests on Alfa Pendular (AP) and Intercity (IC) trains. Due to their model series and tilting mechanisms, these trains present different suspension systems. Therefore, characterising those trains' suspension systems is essential.
## 4 Results
### Binary Segmentation Results  
In this section, the change-points discussed are based on binary segmentation as shown in Figures 17-19 for the three separate geographical regions. We use the acronyms TSA and TSO to abbreviate Total Sum Aerosol and Total Sum Ozone in Figures 17-19.  
The binary segmentation method applied to the total sum ozone and total sum aerosol time series in Region 1 shows an interesting pattern, namely, that changes in aerosol can induce a delayed effect on ozone of between 10 and 25 days.  
This can be seen by comparing the dates on which change-points occur in the total sum aerosol time series against the total sum ozone time series. The first total sum aerosol change-point occurred on 7 November 2019, 11 days before the first total sum ozone change-point occurred; specifically on 18 November 2019 (Figure 17).  
This same pattern occurs at the second and third change-points of the total sum aerosol occurring on 4 December 2019 and 8 January 2020 with the perceived effect on ozone occurring 25 days later on 29 December 2019 for the second change-point and 10 days later on 18 January 2020 for the third change-point.  
This lag pattern also appears in Region 2, as the first total sum aerosol change-points occur after the corresponding, second and third total sum ozone change-points in the same region. However, the second and third total sum aerosol change-points occur after the last total sum ozone change-point in Region 2 (Figure 18).  
For Region 3, a slightly different situation arises, as the binary segmentation method only detected two change-points for the total sum ozone time series. The first occurring on 18 November 2019 and the second on the 2 January 2020 (Figure 19).  
The first total sum aerosol change-point occurring on 5 November 2019, has a similar lag pattern to those identified in Region 1, since the first total sum ozone change-point occurred 13 days later, on 18 November 2019. Overall, there is a lag between change-points in total sum aerosol levels and corresponding change-points in total sum ozone levels of 10 to 25 days.  
It is difficult, on the basis of this change-point analysis alone, to definitively detect a causal relationship between changes in aerosol levels and changes in ozone levels, though there is some evidence of a pattern. An alternative approach is applied in this study to statistically establish times at which the ozone states change, as determined by HMM models,