esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Satellite Self-Organizing Network Simulation Platform Based on OMNeT++
### Function Domain
#### 4.3.3 Time Slot Allocation  
In order to avoid congestion and packet loss caused by multiple nodes sending messages simultaneously during message transmission, it is necessary to allocate a time slot to each node. Nodes send messages within their allocated time slots to prevent sending messages simultaneously. Since the network has a layered structure and different clusters are relatively independent, time slots can be independently allocated within each cluster to achieve the reuse of time slot resources and improve resource utilization. In the scenario considered in this paper, the number of nodes in a cluster is limited to under 9, so 1 s can be divided into ten time slots, each 100 ms, and they can be allocated to the member nodes within the cluster. Time slot allocation can be divided into time slot allocation for nodes initially joining the network and time slot allocation for isolated nodes joining the network. The specific process of time slot allocation for initial network access is as follows:  
1. When the ground node receives the satellite node's network access request, after assigning the cluster affiliation in the network layer, the Mac layer allocates an idle time slot to the node and sends the time slot information to the node through a message;
2. After the node receives the message containing the time slot information, it informs the cluster head of its time slot information through a message;
3. The cluster head updates the utilization of time slot resources in the cluster. The time slot allocation process for isolated nodes joining the network is as follows:  
1. The isolated node sends a network access request to the cluster head;
2. After receiving the access request, the cluster head checks if there are any remaining idle time slots in the cluster. If there are idle time slots, proceed to the next step;
3. The cluster head allocates an idle time slot to the new joining node and notifies the node through a message.  
The time slot allocation process is deployed at the Mac layer modules of satellite nodes and the ground node. Additionally, the Mac layer modules of the satellite nodes also have the function of managing time slots, where the cluster heads manage the time slots of the members within the cluster, and the member nodes send data based on their time slots.
## 3 BER Performance Estimation
### Average BER Performances Due to T Influence  
Instrictively, the ABER is reciprocally affected by the probability of incorrect detection due to TJ. For single-input, single-output (SISO) systems, this is given by the probability of either an early or a delayed signal arrival, while in SIMO systems the corresponding quantity arises from the joint probability of incorrect detection at all the receivers and is given as [48]:  
\[\widetilde{P}_{b,N,TJ,MS}=\left(\frac{P_{e}(T<-t_{sl,MS})}{2}+\frac{P_{e}(T>t_{sl, MS})}{2}\right)^{N}=\frac{1}{2^{N}}\left[\int\limits_{-\infty}^{-t_{sl,MS}}f_{TJ}(T)dT+\int \limits_{t_{sl,MS}}^{+\infty}f_{TJ}(T)dT\right]^{N} \tag{16}\]where \(t_{sl,MS}=\left[\begin{array}{c}t_{sl,OOK}\\ t_{sl,L-PPM}\\ t_{sl,L-PAM}\\ \end{array}\right]=\left[\begin{array}{c}1/R\\ \log_{2}L/(LR)\\ \log_{2}L/R\\ \end{array}\right]\), denotes the time slot interval for the selected modulation scheme, with \(R\) being the bit rate.  
Supposing a symmetrical GGD around the time slot center, i.e., \(\mu_{T}=0\) s, the ABER due to the T] effect, with relation to the modulation scheme used, is easily rendered by substituting Equation (7) in (16), as:  
\[\widetilde{P}_{b,N,T,J,MS}=\left[\frac{\Gamma\left(\beta^{-1},\left(t_{sl,MS} \alpha^{-1}\right)^{\beta}\right)}{2\Gamma\left(\beta^{-1}\right)}\right]^{N}= \left[\begin{array}{c}1\\ 2\Gamma\left(\beta^{-1}\right)\end{array}G_{1,2}^{2,0}\bigg{(}\left(\alpha^{- 1}t_{sl,MS}\right)^{\beta}\bigg{|}\begin{array}{c}1\\ \beta^{-1},\ 0\end{array}\bigg{)}\right]^{N} \tag{17}\]
## 4 Simulation
### Impact of Message Generation Ratio  
In the simulation, we vary the data generation rate in order to evaluate the performance of the four protocols under different transmission loads. As the date generation rate varies from 0.01 to 0.1 message/s, the performance of four protocols is as shown in Figure 4.  
From Figure 4(a) we can see that MEDR achieves the highest data delivery ratio, obviously outperforming the other three protocols, which means that MEDR provides a more efficient data gathering scheme for DTMSNs. The direct transmission has the lowest data delivery ratio, since sensors just communicate with the sink node directly in this protocol, and if a sensor has no chance to move into the communication range of the sink node, those data generated by it may never be delivered successfully. We also notice that the data delivery ratio of the epidemic protocol is higher than direct transmission when the data generation rate is very low, but the value decreases dramatically as the data generation rate increases. This is due to MAC layer collision and rapid exhaustion of the limited network resources resulting from forwarding a tremendous amount of copies in epidemic routing. In addition, we find that FAD outperforms direct transmission and epidemic protocol as to the data delivery ratio. As the transmission load increase, its performance descends gradually since generating  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Default value** \\ \hline Network size (m) & 200 \(\times\)200 \\ Number of sensor node & 100 \\ Transmission radii \(r\) (m) & 5 \\ Speed of sensor node v (m/s) & 3 \\ Maximum queue size of sensor (message) & 200 \\ Data message size (bytes) & 100 \\ Control message size (bytes) & 25 \\ Message generation ratio (message/s) & 0.01 \\ Maximum delay tolerant value (s) & 1,800 \\ Position of sink node & (100, 100) \\ \(t_{hop}\) value(s) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
very many copies in this protocol. What's more, the performance of FAD is unstable, which is influenced by the timer expiration value \(\Delta\) and parameter \(\alpha\) greatly. To have a fair comparison, we adjust these two parameters to get the close-to-optimal performance.
###### Abstract  
To clarify the mechanics of tooth movement produced by a unique distalizer, Class II Carriere Motion appliance (CMA), in which the maxillary canine is connected to the maxillary first molar with a stiff bar, long-term tooth movement was simulated by the finite element method (FEM). The FEM models of the maxillary canine, premolars, and first molar were made based on a dental study model. The periodontal ligament (PDL) was constructed on the root and assumed to be a nonlinear elastic material. The teeth and the alveolar bone were assumed to be rigid bodies. The tooth moved by accumulating the initial movement produced by the elastic deformation of the PDL. When retraction force was applied to the canine from the mandibular dentin, the canine tipped or rotated clockwise and extruded due to the vertical component of the retraction force. The molar and premolars also tipped and moved distally, but hardly extruded because the vertical force did not act on them. As a result of these tooth movements, the canine protruded from the dentition. An interproximal space was created between the canine and the lateral incisor. These movement patterns were similar to those in other clinical studies using the CMA.  
orthodontics; class II malocclusion; distalization; Carriere; finite element method +
Footnote †: journal: Applied  
1
Footnote 1: email: [EMAIL_ADDRESS]  
Previous clinical studies have shown that CMA was effective in improving Class II malocclusions [6, 7]. In addition, tooth movement patterns were measured precisely using cone beam computed tomography (CBCT) [8, 9].  
However, few studies have examined CMA from a mechanical point of view. The mechanics of CMA has not been clarified. In the present study, distalization of the maxillary molar with the standard CMA was simulated using the finite element method (FEM), and their results were compared with the tooth movement observed in other clinical studies.
# A Point Crack Source Location Method without Velocity Information in Anisotropic Plates
## 2 Methodology  
### Elliptic Wavefront  
We consider a wave propagation in an elliptical shape on the CFRP's surface. For an ellipse reaching the \(i\)th sensor, the center is the location of the crack \((x,y)\), and the lengths of the two axes are \((t_{i}-t)v_{x}\) and \((t_{i}-t)v_{y}\) in the directions \(x\) and \(y\), respectively, where \(t_{i}\) is the wave arrival time at the \(i\)th sensor and \(v_{x}\) and \(v_{y}\) are the wave speed in the \(x\) and \(y\) directions, respectively (see Figure 1). Thus, we have the following ellipse equation:  
\[\frac{(x_{i}-x)^{2}}{(t_{i}-t)^{2}v_{x}^{2}}+\frac{(y_{i}-y)^{2}}{(t_{i}-t)^{2 }v_{y}^{2}}=1 \tag{1}\]  
where \(t\) is the crack occurrence time.  
Figure 1: (**a**) Elastic wave propagation when the source position is a red dot in an anisotropic object, whose lateral elastic wave velocity is higher than that in the vertical direction. (**b**) An elliptical wave having a source at the location \((x,y)\) at time \(t\) arrives at the sensor \((x_{i},y_{i})\) at time \(t_{i}\). The horizontal and vertical velocities of the elliptic wave are \(v_{x}\) and \(v_{y}\), respectively.  
We multiply \(v_{x}^{2}\) on both sides of Equation (1) and let \(\alpha=v_{x}/v_{y}\), and then  
\[\frac{(x_{i}-x)^{2}}{(t_{i}-t)^{2}}+\frac{(y_{i}-y)^{2}\alpha^{2}}{(t_{i}-t)^{2} }=v_{x}^{2} \tag{2}\]  
for any \(i=1,2,\ldots,n\), where \(n\) is the number of sensors. Note here that the problem involves finding \((x,y)\) from \(t_{i}\) for \(i=1,2,\ldots,n\), where \(t\), \(v_{x}\), and \(\alpha\) are unknown values.  
Since \(v_{x}\) is a constant independent of \(i=1,2,\ldots,n\) in Equation (2), we have  
\[v_{x}^{2}=\frac{(x_{i}-x)^{2}+(y_{i}-y)^{2}\alpha^{2}}{(t_{i}-t)^{2}}=\frac{( x_{j}-x)^{2}+(y_{j}-y)^{2}\alpha^{2}}{(t_{j}-t)^{2}}\]  
for any \(i,j=1,2,\ldots,n\). Multiplication of the fraction by the denominator on the left and right sides gives  
\[(t_{j}-t)^{2}\Big{(}(x_{i}-x)^{2}+(y_{i}-y)^{2}\alpha^{2}\Big{)}-(t_{i}-t)^{2 }\Big{(}(x_{j}-x)^{2}+(y_{j}-y)^{2}\alpha^{2}\Big{)}=0 \tag{3}\]  
for distinct \(i,j=1,2,\ldots,n\). Here, we can obtain as many equations as the number of combinations of \(i\) and \(j\).
## 4 Discussion
### Temporal Evolution of the Estimated Plasma Parameters
The resulting recombination time scale at atmospheric pressure thus happens to be in the same order of magnitude as the considered pulse repetition period (\(\sim\)23 \(\upmu\)s for 43 kHz and \(\sim\)17 \(\upmu\)s for 60 kHz). If a recombination time of \(\sim\)20 us is assumed, a slight change in the repetition rate of the pulsed excitation can strongly influence the ionization degree. Accordingly, if the repetition period is shorter than the recombination time scale, each current pulse excitation encounters a plasma channel, which is not yet fully neutralized and whose still-charged ions may strongly disturb and hinder the acceleration of the free electrons, making any collision less efficient for an eventual ionization. As a result, the higher repetition rate of 60 kHz leads to a plasma with a lower ionization than in the case of the 43 kHz pulse repetition rate.
## Materials and Methods
### Data Preprocessing, Sample Partition, and Cross-Validation
On the contrary, the iterative random partition method is relatively more complex because it randomly generates training sets and test sets for each iteration, and then builds training models and predictions from it, stopping when a specified number of iterations is reached. As the number of iterations increases, the difference between the training set and the test set of the model gradually decreases, and the overall predictive performance of the model becomes more and more stable, thus better reflecting the true predictive ability of the model. However, the disadvantage is the high computational complexity. Cross-validation is a model validation method used to evaluate how the results of statistical analysis will be generalized to independent test sets. In chemometrics, it is a very useful tool to evaluate both the complexity of the model, such as the number of latent variables in a partial least squares discriminant analysis model and the performance of the model when applied to an unknown sample. In this study, a Venetian blinds method belonging to k-fold cross-validation was used. In this approach, the raw data set is randomly equated into k-folds. In the k-fold, one subsample was retained for the test data used to validate the model, and the remaining k-1 subsample was used as training data. The cross-validation process was then repeated k times, with each of the k subsamples being used as a test sample only once. These k results are then used to calculate the mean of the root mean square error (RMSEC) for the training set and the root mean square error (RMSECV) for the cross-validation set to assess the pros and cons of the model fitting data and the ability of the model to predict unknown samples.
## 2 Materials and Methods
### Models and Analysis  
#### 2.2.1 Grain Yield Estimation  
At each of the three studied phenological stages (tillering, heading, and ripening), the previously described vegetation indices were calculated per field on ArcGis Pro 2.3.0 using the downloaded Sentinel-2 imagery. Four different statistical summary extractions of each vegetation index (maximum, minimum, mean, and median) were obtained for each field at each different phenological stage, considering the regional differences outlined in Tables 2 and 3. Mean and median were calculated with the closest available images to the phenological stage using zonal statistics, while minimum and maximum were respectively the averages of the lowest and highest pixel values between the two closest dates to the phenological stage. These calculations were performed on ArcGis Pro 2.3.0 and Microsoft Excel. First, the _Raster to Point_ and _Spatial join_ tools on ArcGis were used in order to list, per field, all the pixel values; second, with the corresponding attribute tables the maximum and minimum vegetation indices pixel values between the dates were calculated with Microsoft Excel. Finally, the maximum and minimum were calculated on the points' attribute table were converted into two rasters in ArcGis in order to perform zonal statistics and obtain maximum and minimum averages per field over the specific periods. Pearson regressions against grain yield were calculated on R studio (\"cor.test\") for each phenological stage and summary (min, max, mean, and median). Following this, in order to find the combination of the best vegetation indices and statistical summary to estimate grain yield, multilinear stepwise regressions were studied at the most suitable phenological stage in R Studio (library \"MASS\"). The selected best model was chosen on the basis of Akaike Information Criterion, computed with the stepAIC function from the MASS library. The normal distribution of residuals and collinearity of the independent variables were studied with the library \"olsrr\", the variance inflator factor (VIF) was measured as it is an efficient method to asses collinearity [47; 48; 49], which strongly limits stepwise selection method. Furthermore, the residuals normality Shapiro-Wilk test was calculated.  
Figure 3: Three stratified areas corresponding to three agrarian zones, II in Montana, V in media, and VI in Ribera Alta. The stratification for crop type classification and the corresponding training points. UTM coordinates are expressed in meters.  
The 39 monitored fields were separated into two groups, one for training and the other one for validation of the multilinear stepwise equations. The 20 fields from 2018 season were used for modeling and the 19 fields from 2019 were used for validation. For the 2019 season, only one phenological stage, heading, was eventually analyzed, as it appeared to be the most suitable for grain yield estimation (Table 7), with the following images: Montana, 09-05 and 14-05; Media, 14-05 and 08-06; and Ribera Alta, 29-04 and 09-05.
## 3 Analysis of Results
### Workability  
The results obtained for consistency are shown graphically in Figure 5 together with those obtained in the previous article [14] for GCW-D wastes. Very different behaviors were observed with the two different granite wastes. The consistency of fresh UHPFRC increased when GCW-D was used as an alternative to micronized quartz, while GCW-GS produced a loss in the consistency. The inferior workability of the mix with GCW-GS is most likely due to the presence of CaO in the mix [33], which reacts with water to produce calcium hydroxide and thereby interfering with the cement hydration process. Although to some degree this is compensated for by adding a superplasticizer, its effect is still evident.  
The loss of workability for UHPFRC with GCW-GS when increasing the percentage of substitution concurs with the results obtained in other studies when incorporating the waste of granite stone slurry as an alternative to the finest aggregate [34].
## 6 Conclusions and Future Work  
In this paper, a positioning method based on DVB-T Single Frequency Networks has been presented. This method uses the scattered pilot subcarriers of OFDM symbols to measure the Time of Arrival (ToA) of the signals transmitted by the DVB-T emitters. In our study, we suppose that the user is equipped with a hybrid GNSS-DVB-T device. GNSS is used in a first initialization phase to solve the ambiguities referring to the various DVB-T emitters. When this phase is completed, the user position can be obtained by using DVB-T signals when the user enters a GNSS-blocked area. (Note that transmission of the emitter ID would be very useful for positioning. Since it is optional, it has not been considered in the study, but obviously, it would further simplify initialization and tracking of DVB signals.)  
The method has been tested by simulation in a dynamic scenario. The simulation results show that a position mean error = 6.0747 m can be achieved if the user can correctly associate the signals to the emitters. If the receiver makes a wrong association, some additional information is needed to determine which trajectory is the correct one. The Hata model has been used to simulate emitters with different SNRs, and a Rayleigh channel has been also introduced to take into account the multipath. The results shows that the range error changes with respect to SNR, while the position estimates do not change significantly along the trajectory. However, if one of the DVB-T emitters is unsynchronized, the position error will increase as the time offset increases.  
The result of this study is that the structure of the DVB-T SFN signals is such that a PD working with DVB-T signals can be conceived. At this point, the performance of the positioning algorithms in the presence of different errors sources has to be evaluated. In our future work, a more realistic multipath channel will be considered, and an analysis taking into account other error sources will be performed.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 4 Experiments and Evaluation
### Evaluation Using Real Environment
Due to the elimination of dynamic feature points, our system can achieve faster tracking with local optimization compared to ORB-SLAM2. while the semantic segmentation speed of the SegNet network of DS-SLAM is far less rapid than that of the BiSeNet V2 network, which results in a situation that the tracking module needs to wait for the semantic segmentation module, finally leading to a decrease in the overall system slow-down. Moreover, as can be seen from Figure 1, our semantic segmentation network is not directly involved in the tracking module, so there is no tracking thread waiting for the semantic segmentation results.
## 3 Results and Discussion
### Limitations of the Data and Methods  
The CYGNSS small satellite constellation has proposed a new paradigm in earth environmental monitoring due to its unique data acquiring mechanism [5]. For monitoring flooding over land, as shown in this study, it is able to reach a high temporal resolution, i.e., at a daily scale. However, as shown in Figure 2, it could not guarantee a high spatial resolution for inundation mapping in regions with moderate-to-high topography and surface roughness [6]. Using the spaceborne GNSS-R technique, through the design of new constellations for monitoring land rather than ocean, e.g., constellations with more small satellites and better data transmission approaches, the spatial and temporal resolution of this technique will be greatly improved.  
The methods used in this study have several limitations: (1) this study used the version 2.0 CYGNSS data, and the newly released version 2.1 data could be used to derive a better-calibrated SNR; (2) the simple threshold to isolate water and land is not universal. Calibration should be performed to produce flood products using CYGNSS data or data in future GNSS-R missions; (3) gridding the daily CYGNSS data to 30 km or 50 km is likely inappropriate as it will grossly overestimate the actual extent of flooding.
## 2 Altimetry in the GEOSS and GMES perspectives
Continuation of the precision altimeter missions in the T/P - Jason orbit is necessary for sustaining this level of accuracy in monitoring long-term sea level change. Although adequate for many other applications, altimetry from the planned National Polar-orbiting Operational Environmental Satellite System (NPOESS) will not achieve this level of accuracy. Four altimeter missions are flying at this time: T/P, Jason-1, Envisat, and Geosat Follow-on. By 2008, it is possible that the only altimeter flying will be Jason-2. Two different solutions can help achieving the space and time requirements of observations to provide adequate resolution: a constellation of several nadir altimetry satellites (see [1] for instance) or the use of wide swath altimetry (onboard one platform) with at least a ten km resolution and 200 km swath ([PERSON] and [PERSON] [4], [PERSON] et al. [5]). Throughout this paper, only the first solution will be considered.
## 4 Case Study of Punjab OSM Data
### Data Preparation and Tools
#### 4.1.2 Routing Graph Preparation  
[PERSON] and [PERSON] [48] have suggested that the preparation of a routable OSM graph requires thorough preprocessing and conversion to a real topology. Shapefiles of proprietary datasets normally carry such information, but the database prepared from OSM-shapefiles using the shp2 pgsql tool should be cautiously handled ([[http://pgrouting.org/docs/howto/shapefiles.html](http://pgrouting.org/docs/howto/shapefiles.html)]([http://pgrouting.org/docs/howto/shapefiles.html](http://pgrouting.org/docs/howto/shapefiles.html)), accessed on 27 June 2016). In this study, India data in.osm.pbf format were downloaded from ([[http://download.geofabrik.de/osm/](http://download.geofabrik.de/osm/)]([http://download.geofabrik.de/osm/](http://download.geofabrik.de/osm/))), and Punjab data were extracted using osmosis based on the polygon. A tool, osm2 pgrouting ([[https://github.com/pgRouting/osm2](https://github.com/pgRouting/osm2) pgrouting]([https://github.com/pgRouting/osm2](https://github.com/pgRouting/osm2) pgrouting), accessed on 26 June 2016), was used to load the data to PostgreSQL database. It parsed OSM XML data and created a topologically-correct routing graph. Thereafter, it generated SQL files for PostGIS, which was compatible with pgRouting and QGIS. Further, pgRouting functions were used to assess the quality of routing graph data, i.e., pgr_analzeGraph() and pgr_nodeNetwork(), to fix some issues like noding of data for ensuring that intersections in the network are represented as the start or end of an edge, gaps, self-intersections and dead nodes, etc.