esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conceptual Design of BESPECIAL
### Second Development Stage
#### 4.2.1 Psychometric Tests and Their Delivery via VR
In addition, tests performing time will be reduced and the obtained results will be collected and stored more easily and in a convenient way for their use in the AI module. Two main approaches can be followed to deliver contents via VR, depending on the type of devices used, namely, immersive virtual reality (IVR) and desktop virtual reality (DVR). IVR is capable of offering a complete immersion of the user in the virtual environments by using specific hardware known as a VR headset, which consists of a helmet provided with two head-mounteddisplays and headphones. The displays allow for 3D vision and isolate the user's sight from the real world, as well as headphones for the user's hearing. Coherence between the user's and virtual images movements are ensured by inertial sensors equipped on the helmet. Hand controllers are also provided often, in order to improve the interaction with the virtual world. The result is the projection of multiple images that configure a room-size venue for experiencing VR and creating a flow through the scenario, thus generating the full immersion experience. Unfortunately, IVR suffer a major limitation due to the high costs and the low accessibility of the needed devices. Conversely, DVR does not need complex and expensive hardware to be played, since it runs on common devices, like personal computers, tablets and smartphones, which are more sustainable for higher education institutions [85]. As a counterpart, it can provide only a static and less interactive experience, making the sense of complete immersion be lost. Some cheap helmets, into which a smartphone can be inserted, however, can partially render it affordable. The majority of the previous studies are based on IVR [86]. Nevertheless, some novel works are focused on DVR environments [85]. Inspired by the second ones, BESPECIAL VR psychometric tests will be created by relying on DVR. They will thus, be suitable for ubiquitous devices, like smartphones and tablets, allowing for a wider and quicker spread. JSON and PhoneGap framework will be used to develop the VR module, including PHP and HTML, combined with MySQL, CSS, JavaScript and JQuery, as complementary base languages. The created VR environment will present the tests in a calming background, made of soft colors and arcade game elements. Cheap cardboard helmets for common smartphones to be inserted into will be used to reduce the field of view of the user, in order to enhance the degree of immersion perceived. The results of psychometric tests will then be passed to the AI assessment module and used jointly with the results of the questionnaire and the information extracted from the clinical reports, first to train the AI and, then, to predict which is the best supporting material for each user.
## 3 Results and Discussions
### Displacement Responses  
Displacement response is the total displacement of the storey at the corresponding degree-of-freedom. Representative displacement time history for Chi-Chi earthquake excitations is shown in Figure 6. It is clear from the figure that the passively isolated structure subjected to earthquake loading time history vibrates at a lower frequency and higher amplitude, whereas the fixed base structure vibrates at a much higher frequency. It can be observed from the time history plots that the MR elastomer-based isolated structure does not vibrate at any single value of frequency; rather, it responds by vibrating at a range of frequencies. Higher amplitudes of both the isolated structures are due to larger displacements at the isolation level.  
Storey-wise line plots for peak displacements are presented in Figure 8. It can be seen in the figures that for the isolated structures, a major portion of total displacement (96-99%) in the structure is absorbed at the isolation level due to the relatively soft isolation layer, and very minimal displacement is being transferred to the superstructure. Whereas for the fixed base model, all the displacement is transferred rather uniformly to the superstructure. Higher base drifts for the near-fault earthquake time histories compared to the far-fault earthquake time histories can be observed. A considerably lower response for the Kocaeli earthquake far-fault earthquake time histories can also be seen as compared to the other cases.  
A comparison of peak displacement response for all the adopted earthquake time histories is presented in Figure 9. A greater response for the passively IS and greater subsequent reduction in response can be observed for the near-field earthquakes compared to the far-fault ones. It can also be observed that the response of the passively isolated structure against Kocaeli FF station is very low compared to that of other responses, thus its response reduction of MRE BIS compared to passively IS is also lower.  
Figure 6: Displacement time history at storey 5 for the Chi-Chi earthquake.  
Figure 7: Peak displacements relative to base, Imperial Valley earthquake.  
The average displacement response improvement of MRE BIS compared to the passively IS is presented in Figure 10. It is evident that response improvement is greater for the near-fault earthquake records compared to the far-fault ones.  
Figure 8: Storey-wise total displacements.  
Figure 10: Average displacement response reduction of MRE base-isolated structure (MRE BIS) compared to the passively IS.  
Figure 9: Peak displacement response at top storey against earthquake cases.
###### Abstract  
Early detection of earthquakes is essential for minimizing potential damage and ensuring public safety. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), provide a promising alternative for analyzing seismic waves. In contrast, traditional methods such as the short-term average/long-term average (STA/LTA) algorithm and the Akaike information criterion (AIC) have limitations in detecting primary (P) waves in high-noise conditions, caused by industrial and anthropogenic disturbances. This study presents a CNN-based automatic P-wave detection model tailored for the Almaty city region. The seismic dataset used in this research was obtained from the IRIS database and includes data collected from seven stations within a 333 km radius of Almaty, Kazakhstan. The proposed model achieves a recall rate of 89.1% and an accuracy of 94.1% compared to other deep learning-based models. Experimental results demonstrate that this method enhances the reliability of automatic early earthquake warning systems and improves the accuracy of P-wave detection. The research outputs presented for the local region are unique. Applying CNNs in seismic monitoring facilitates the development of efficient automated systems that minimize risks and improve response measures for natural disasters.  
Article  
P-wave detection; deep learning; convolutional neural networks; earthquake early warning; seismic monitoring
# Extracellular Matrix Tunes the Regenerative Potential of Fetal Stem Cells
## 3 Results
### Chondrogenic Capacity of Fetal Stem Cells after Growth on dECMs  
To characterize the chondrogenic potential of fetal MSCs following expansion on dECMs, P5 fSDSCs and fNPCs grown on SECM and NECM were incubated in a pellet culture system to move toward chondrogenesis. After a 21-day chondrogenic induction, the qPCR data showed that fNPCs exhibited a greater response to chondrogenic induction than fSDSCs. fNPCs  
Figure 2: Assessment of fetal MSC surface marker expression following expansion on dECMs. Flow cytometry was used to measure the expression of the MSC surface markers CD73, CD90, CD105, CD146, and SSEA4, as well as relative EdU incorporation into fNPCs and fSDSCs following expansion on dECM deposited by fetal NPCs (NE) or fetal SDSCs (SE), with tissue culture plastic (PL) as a control.  
yielded higher expression levels of _ACAN_, _COL2A1_, and _PRG4_ but lower expression levels of _COL1A1_ and _COL10A1_ after expansion on NECM; fSDSCs yielded lower expression levels of _ACAN_ and _COL1A1_ and higher expression levels of _COL2A1_ and _COL10A1_ after expansion on SECM (Figure 3A). Compared to Plastic expansion, interestingly, dECM-expanded fNPCs exhibited a decrease in _FBLNI_ (a marker for human articular cartilage) and an increase in _FOXF1_ (a marker for human NP tissue) [24], particularly with NECM; dECM-expanded fSDSCs displayed no significant change in _FBLNI_ and _FOXF1_ (Figure 3B).  
Compared to Plastic expansion, dECM-expanded fNPCs yielded significantly larger pellets, while dECM-expanded fSDSCs did not show much change (Figure 4). dECM-expanded fNPCs yielded pellets with more intense staining of sulfated GAGs and type II collagen and less intense staining of type I collagen than the Plastic group; dECM-expanded fSDSCs yielded pellets with similar staining intensity to the Plastic group (Figure 4). Interestingly, there were no detectable differences in the intensity of type X collagen staining among all groups (Figure 4).  
Figure 3: Chondrogenic gene evaluation of fetal MSCs following expansion on dECMs. fNPCs (N) and fSDSCs (S), following expansion \"on\" dECMs deposited by fNPCs (NE), fSDSCs (SE), or tissue culture plastic (PL), were incubated in a pellet culture system with chondrogenic medium for 21 days. TaqMan® real-time PCR was used to evaluate the chondrogenic marker genes _SOX9_, _ACAN_, _COL2A1_, _PRG4_, _COL1A1_, and _COL10A1_ (**A**), and _FBLNI_ and the NPC marker gene _FOXF1_ (**B**) in the pellets collected at days 0, 10, and 21 after chondrogenic induction. _GAPDH_ served as the internal control. Data are shown as a bar chart. * indicates a statistically significant difference from the corresponding PL group (\(p<0.05\)). * indicates a statistically significant difference from the corresponding fNPC group. * indicates a statistically significant difference from the corresponding PL group of day 0 pellets (\(p<0.05\)).
## 5 Results and Discussion  
Three datasets, acquired on the NeVoCGPS network, were analysed with different techniques. A long dataset, ranging from 6 to 11 years, was used to study the long-term temporal evolution of parameters of interest in GNSS meteorology, i.e., ZTD, ZHD, ZWD and PWV to investigate climate trends in the region. In addition, a more focused analysis was conducted on two short data sets, 8-day and 5-day samples, respectively, encompassing the periods when three severe thunderstorms hit the Campania region on 23 and 26 September 2019, and 16 November 2020 (Table 2).  
For these analyses we mainly focused on the Vesuvius sector of the network because it enables us to study the topographic effects on the characteristics of the estimated parameters. GPS stations in the Vesuvius area are, in fact, distributed at different altitudes, ranging from 70 m (AGR1) to about 900 (BKE1) metres above sea level.  
In our Gamit data-processing for the thunderstorm study (short-term analysis), we used only real observations collected at a local weather station, co-located with the GPS site, MAFE, and on a remote site, MATE (Matera). In order to obtain the greatest accuracy for our weather studies, for the remaining stations and for the long-term analysis, the VMF1 mapping functions [27] were used, derived at 6-h intervals from numerical weather  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Thunderstorms** & \\ \hline
**Start** & **End** & **Total Rain (mm)** \\ \hline
23 September 2019, 01:00 & 23 September 2019, 04:00 & 21.6 \\
26 September 2019, 01:00 & 26 September 2019, 06:00 & 28.2 \\
16 November 2020, 22:30 & 17 November 2020, 02:30 & 70.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thunderstorms analysed in this study; rain amounts measured at MAFE station.  
models and updated daily on GAMIT/GLOBK servers. Then, the variation in the zenith delay during the observation span can be assessed at a higher sampling rate (1 h in our study) by specifying a piecewise-linear model with stochastic constraints.
## 2 Background
### Deployment in Wireless Sensor Network  
The factor that has an important effect on all the WSN performance metrics is deployment. The optimal deployment of the sensor nodes indicates that the whole required area is covered, and the network nodes have the best communication with each other with a minimal number of nodes [23]. Deployment can be either static or dynamic; static deployment is divided into deterministic and random deployment, depending on the environment and application required [24].
## 3 Methodology
### In Situ Measurements of Subsidence  
The ALT measurements at the CALM grid are complemented by subsidence observations since 2007 using various installations. Measurements have been made for all three years with Sentinel-1 coverage (29 August 2016), 3 September 2017 and 27 August 2018). The principle used for the measurements is frost-heaving of a plate. This method has been for example used by [PERSON] et al. [12] (metal or fiberglass rods and fiber glass plates). Thaw tubes are also commonly used in comparison to InSAR observations in permafrost areas, (e.g., [20; 59]). Alternatively, Differential Global Positioning Systems (DGPS) measurements have been shown to be applicable for monitoring seasonal subsidence [60; 61] as well as long-term isotropic thaw subsidence [29; 62].  
A metal rod is inserted as deep as the permafrost table through a hollow in a plywood plate (Figure 3). The plate subsides with the surface but the rod, being frozen to the active-layer base till late summer, stays above the subsiding surface. The increment between the plate on the surface and the mark on the rod referring to the initial position on the surface before freezing is accepted equal to subsidence. Measurements are performed annually in late fall along with active-layer measurements at the CALM grid. The seven sites represent different settings including drying parts with cryptogam crust, grasses and mosses as well as low and high shrubs (see Figure 3). They vary in ALT, ranging between 70 and 130 cm. Several of the sites are comparably close to each other (distance less than satellite product pixel spacing). All have been nevertheless included to the assessment to represent heterogeneity and due to data gaps at two of the sites (no data at number 3 for 2016 and 2017 and at number 7 for 2018). The subsidence monitoring program has been extended in 2018. 20 sites have been added and measurements made on 11 July 2018 and 27 August 2018.
# An Efficient BDS-3 Long-Range Undifferenced Network RTK Positioning Algorithm
## 3 Experimental Results Analysis
### Positioning Accuracy
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Combined Model** & **Rover Station \(U_{1}\)** & **Rover Station \(U_{2}\)** \\ \hline B1I/B1C/B2a/B3I & 124 & 164 \\ B1C/B2a/B3I & 122 & 113 \\ B1I/B2a/B3I & 128 & 171 \\ B1C/B2a & 178 & 117 \\ B1I/B3I & 359 & 400 \\ GPS & 286 & 338 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical results of the first convergence time(s). respectively. Additionally, B1C/B2a/B3I was superior to B1I/B2a/B3I, with average increases of 14.14%, 20.53%, and 10.89% in the E, N, and U directions, respectively. Thus, the B1I/B1C/B2a/B3I positioning results were the best among all combinations. Compared with the dual-frequency combination, the average increases for B1I/B1C/B2a/B3I in the E, N, and U directions were 27.37%, 31.91%, and 26.56%, respectively. In addition, compared with the triple-frequency combination, the average increases for B1I/B1C/B2a/B3I in the E, N, and U directions were 9.38%, 20.75%, and 18.72  
In order to test the positioning results, the residual values of the satellite positioning results of two sets from rover station \(U_{1}\) and rover station \(U_{2}\), corresponding to different frequencies, are shown in Figures 16 and 17. Theoretically, the residual value is close to zero, and in this experiment, it was obtained via substituting the final positioning result and estimated error parameters obtained in the intermediate process into the rover observation equation. The blank segments in Figures 16 and 17 are due to a failure to observe the satellite during this period. As shown in Figures 15 and 16, the residual values of the BDS-3 and GPS were generally near zero. Although the residual values were different because of the different observation times of the satellites, they could be maintained within 1 cm, which conformed to the variation law of residual values and coincided well with the final positioning results. From the perspective of frequency, the B2a residual value of the BDS-3 was
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 2 System Model
### Network Throughput and Delay  
In this scenario, the total network throughput (\(r\)) is the sum of throughput from each CM to the CC. Given \(d_{mn}\) represents the link distance between \(m\) and \(n\), then the throughput is defined as:  
\[r_{mn}=B\log_{2}\biggl{(}1+\frac{P_{t}}{N_{o}PL(d_{mn})}\biggr{)}. \tag{2}\]  
The CHs in the network can cooperate with each other to forward their data to the UAV or CC. Since a CM in cluster \(i\) (\(s_{i}\in N_{i}\)) takes multiple hops to reach CC, we define a routing matrix for each CM, denoted by \(H_{i,s_{i}}\). It is assumed that the routing matrix remains the same for some time horizon and also remains the same for all the nodes in a cluster. Therefore, the routing matrix is defined as \(H_{i,s_{i}}=(h_{mn})\in\mathbb{R}^{(K+1)\times(K+2)}\), where \(h_{mn}\) denotes the status of the connection between \(m\) and \(n\), \(m\in\{s_{i},\text{CH}_{1},\dots,\text{CH}_{\text{I}},\text{UAV},\text{CC}\}\) and \(n\in\{\text{CH}_{1},\dots,\text{CH}_{\text{I}},\text{UAV},\text{CC}\}\), and \(h_{mn}=1\) (\(h_{mn}=0\)) indicates the presence (absence) of path between the nodes \(m\) and \(n\). For example, if the 2 nd node in the 1 st cluster forward their data to CC through \(\text{CH}_{3}\) and UAV, then the routing matrix for such a configuration can be written as:  
\[H_{1,2}=\begin{bmatrix}1&0&0&0&0\\ 0&0&1&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{bmatrix}. \tag{3}\]  
The first entry \(h_{11}=1\) indicates the link between the 2 nd node and 1 st CH, \(h_{23}=1\) indicates the link condition between \(\text{CH}_{1}\) and \(\text{CH}_{3}\), \(h_{34}=1\) connects \(\text{CH}_{3}\) to the UAV, and \(h_{45}=1\) successfully terminates the information at CC.  
Let \(\xi_{i,s_{i}}\) defines the throughput for all the possible paths from the node in \(s_{i}\), then the throughput on the paths defined by the routing matrix \(H_{i,s_{i}}\) can be written as \(\xi_{i,s_{i}}=(r_{mn}h_{mn})\in\mathbb{R}^{(K+1)\times(K+2)}\). Since a node takes multiple hops to reach CC, we assume that the minimum throughput in all the multi-hop links is the throughput of the node. Let \(\lambda_{i,s_{i}}\) be the minimum value of \(\xi_{i,s_{i}}\), then the sum throughput of all the nodes can be written as:  
\[\zeta=\sum_{i\in I}\sum_{s_{i}}\lambda_{i,s_{i}}. \tag{4}\]  
The number of hops can be directly computed from the rank of \(H_{i,s_{i}}\) denoted by \(\wp_{i,s_{i}}\). Using \(\lambda_{i,s_{i}}\), packet size \(L\), and transmission rate, the end-to-end delay (ignoring propagation delay) of a node indexed by \(s_{i}\) in cluster \(i\) can be calculated as:  
\[\delta_{i,s_{i}}=\sum_{m}\sum_{n}\frac{Lh_{mn}}{r_{mn}}, \tag{5}\]  
Figure 3: Two-tier graph.  
whereas, the sum end-to-end delay of all nodes in the network are given as:  
\[\Delta=\sum\limits_{i\in I}\sum\limits_{s_{i}}\delta_{i_{s}i}. \tag{6}\]
## Materials and Methods
### Data Source
#### 2.2.3 Gross Primary Productivity Dataset  
We selected the MODIS dataset of GPP products (MOD17A2H006) from the National Aeronautics and Space Administration (NASA) Earth Observation System Data and Information System ([[https://www.earthdata.nasa.gov](https://www.earthdata.nasa.gov)]([https://www.earthdata.nasa.gov](https://www.earthdata.nasa.gov)), accessed on 10 June 2021). The data period is from 2001 to 2019, with a spatial resolution of 500 m and a temporal resolution of 8 d. The MODIS GPP dataset was developed based on the Light Use Efficiency model, and its reliability has been validated in various studies [47; 48; 49] and has been used in studies in the QPT [45]. Seasonal GPP for spring (March to May), summer (June to August), autumn (September to November) and winter (December to February) were synthesized based on 8-day GPP data, respectively. The maximum daily GPP in each year was extracted by the Maximum Value Composite method as GPP\({}_{\max}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station** & **Lon (\({}^{\circ}\))** & **Lat (\({}^{\circ}\))** & **Elevation (m)** \\ \hline Aba & 101.70 & 32.90 & 3275.1 \\ Zeku & 101.47 & 35.03 & 3662.8 \\ Hongyuan & 102.55 & 32.80 & 3491.6 \\ Ruoergai & 102.97 & 33.58 & 3441.4 \\ Maduo & 98.22 & 34.92 & 4272.3 \\ Dari & 99.65 & 33.75 & 3967.5 \\ Xinghai & 99.98 & 35.58 & 3323.2 \\ Henan & 101.60 & 34.73 & 3500.0 \\ Tongde & 100.58 & 35.25 & 3148.2 \\ Maekang & 102.23 & 31.90 & 2664.4 \\ Shiqu & 98.10 & 32.98 & 4200.0 \\ Tongren & 102.02 & 35.52 & 2491.4 \\ Jiuzhi & 101.48 & 33.43 & 3628.5 \\ Qingshuihe & 97.13 & 33.80 & 4415.4 \\ Banma & 100.75 & 32.93 & 3530.0 \\ Dulan & 98.10 & 36.31 & 3189.0 \\ Qumalai & 95.80 & 34.13 & 4175.0 \\ Majin & 100.25 & 34.48 & 3719.0 \\ Guinan & 100.75 & 35.59 & 3120.0 \\ Gonghe & 100.62 & 36.27 & 2835.0 \\ Guide & 101.37 & 36.02 & 2237.1 \\ Zhiduo & 95.62 & 33.85 & 4179.1 \\ Yushu & 96.97 & 33.00 & 3716.9 \\ Songpan & 103.60 & 32.67 & 2850.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Longitude, latitude and elevation information of meteorological stations.