esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Reservoir Space Characterization
#### 4.1.4 Microfracture  
Microfractures not only provide reservoir spaces for hydrocarbon, but also act as migration pathways for hydrocarbon. The microfractures of Section 1 were mainly developed between minerals and organic matter and had the characteristics of a narrow width, a short length, and a smooth surface (Figure 2H). The microfractures of Section 2 were formed as the result of shrinkage within clay minerals during diagenesis, and their lengths and widths were larger than those of Section 1 (Figure 2I). The origin of microfractures in Section 3 was the same as that in Section 2 (Figure 2J). The microfractures of Section 4 were also formed by the shrinkage of clay minerals caused by the diagenetic stage. The widths and lengths of the microfractures in Section 4 were the largest of the four sections (Figure 2K). In general, the microfractures in the deep-marine shale reservoir of the south Sichuan Basin were mainly related to clay minerals, and microfractures related to organic matter were rare (Figure 2L). Vertically, the development degree of microfractures increased gradually from Section 1 to Section 4.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 7 Conclusions  
We have presented a machine learning-based solution to predict the performance of the current version of the EGNOS system called EGNOS v2. Our solution provides a considerably better performance than the macro model that we compared it against, in terms of the accuracy of the predicted UDREI and GIVEI. To what extent this translates into operational gains and reduced risks for actual users of EGNOS will be investigated in the next (and final) phase of the project, during which the user-level performances of the macro model and our machine learning approach will be assessed more thoroughly.  
Conceptualization and methodology, P.B.O., J.D., T.P., J.N.; C.F., M.D. and G.B.; software, J.N. and C.F.; validation, P.B.O. and M.D.; writing--original draft preparation, review and editing, P.B.O.; visualization, P.B.O. and M.D.; project administration, P.B.O.; funding acquisition, P.B.O. All authors have read and agreed to the published version of the manuscript.  
The reported work was carried out under ESA NAVISP Element 1, which is devoted to the development of innovative PNT, systems, technologies, algorithms, and techniques.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
Thanks to our anonymous reviewer for the constructive suggestions.  
The authors declare no conflicts of interest. The research received funding from the European Space Agency, who have assisted in providing the visualization of the results.
# A Case Study of a Large Unstable Mass Stabilization:
## 2 Materials and Methods
### Satellite Surveys  
After conducting stabilization works on the slope, the ground-based SAR (GB-SAR) technique was used to monitor the landslide during the years 2006 and 2007 as well as to predict the slope movement evolution [14]. That technique is based on SAR interferometry, i.e., the use of consecutive pairs of SAR images for obtaining information about displacements according to the phase difference (interferogram) between two consecutive SAR images [31; 32]. For monitoring the landslide movements [33], the GB-SAR sensor was installed about 600 m from the slope (target distance was between 200 and 1300 m), and worked for 47 days, with an acquisition rate of 1 image per hour, a range resolution of 1.7 m and an azimuth resolution of 0.74\({}^{\circ}\).  
Results of GB-SAR were validated with the results of five Differential GPS (DGPS) campaigns, where 93 points were monitored, both stable points located next to the landslide active area and points within the landslide mass. A good match between the two techniques (GB-SAR and DGPS) was found, being the maximum difference 1.5 cm.  
In addition, the Differential Synthetic Aperture Radar Interferometry (DinSAR) technique was employed [21; 22] to monitor different landslides of the Upper Tena Valley (located in the Central Spanish Pyrenees area), including the area under study. That technique uses microwave remote sensing and produces the measurement of surface displacement of high accuracy with a great coverage capacity [34]. The SPN approach [35],  
Figure 3: Stabilization measures projected; red dotted line and green line show the slope profile before and after implementing the stabilization measures (highlighted in bold), respectively.  
which combines both the Persistent Scatterers [35; 36; 37; 38] and Small Baselines [39; 40; 41; 42; 43] methods, was used.  
In total, 43 SAR images acquired by ERS-2 and ENVISAT satellites (from 2001 to 2007), 14 SAR images acquired by the TerraSAR-X satellite (2008) and 12 SAR images acquired by ALOS PALSAR (L-band) satellite (from 2006 to 2010) were processed. An auxiliary DGPS campaign was also undertaken to validate the results.  
More details about this investigation can be found in [14; 21; 22; 23; 24].
## 3 Optimization Modeling for the Uniformity Design of Accuracy Lifetimes
### Selection of Objective Functions  
In the optimization model of uniform design, the target parameters can be the uniformity \(\rho^{\prime}\) of lifetimes, accuracy lifetime \(T^{\prime}\), total improvement cost \(C_{s}\) etc. The target functions are listed as follows:  
1. The uniformity of lifetimes is the maximum: \[\max\rho^{\prime}.\] (7)
2. The effective accuracy lifetime \(T^{\prime}\) of the measurement system is the maximum. Assuming that the effective lifetime of the system equals the minimum lifetime of all, the objective function is as follows: \[\max T^{\prime}=\min\big{\{}T^{\prime}_{1},T^{\prime}_{2},\cdots,T^{\prime}_{ n^{\prime}},T^{\prime}_{s}\big{\}}.\] (8)
3. The total improvement cost \(C_{s}\) is the minimum. Suppose that the relationship between the improvement cost \(C_{i}\) and the designed value \(\Delta\overline{v}_{ei}^{\prime}\) of the \(i\)-th component is \(C_{i}=f_{i}(\Delta\overline{v}_{ei}^{\prime})\), the objective function can be derived: \[\min\,C_{s}=\sum_{i=1}^{n}C_{i}=\sum_{i=1}^{n}f_{i}(\Delta\overline{v}_{ei}^{ \prime}).\] (9)  
Sometimes, designers are concerned with more than one objective. For instance, when the maximum uniformity with minimal improvement cost is required, the objective function may be established as follows:  
\[\max\rho^{\prime}+\frac{1}{C_{s}}. \tag{10}\]  
This is suitable for other cases.
# Three-Dimensional Modeling of a Robotic Fish Based on Real Carp Locomotion
###### Abstract  
This work focuses on developing a complete non-linear dynamic model comprising entirely kinematic and hydrodynamic effects of Carangiform locomotion based on the Lagrange approach by adapting the parameters and behaviors of a real carp. In order to imitate biological features, swimming patterns of a real carp for forward, turning and up-down motions are analyzed by using the Kineova 8.20 software. The proportional optimum link lengths according to actual size, swimming speed, flapping frequency, proportional physical parameters and different swimming motions of the real carp are investigated with the designed robotic fish model. Three-dimensional (3D) locomotion is evaluated by tracking two trajectories in a MATLAB environment. A Reaching Law Control (RLC) approach for inner loop (Euler angles-speed control) and a guidance system for the outer loop (orientation control) are proposed to provide an effective closed-loop control performance. In order to illustrate the 3D performance of the proposed closed loop control system in a virtual reality platform, the designed robotic fish model is also implemented using the Virtual Reality Modeling Language (VRML). Simulation and experimental analysis show that the proposed model gives us significant key solutions to design a fish-like robotic prototype.  
dynamic model; robotic fish; trajectory tracking; biomimetic modeling; fish-like motion Article
## 3 Materials and Methods
### Extracting the Representative CAR
```
Input: A dataset \(D\), minimum support and minimum confidence Output: A compact and accurate model  Initialization of variables: assign the number of classes to N, zero to i and j  Generate the class association rules (CAR) by applying the APRIORI algorithm  Group the CARs based on class label while(i < N)do // We do cluster for each group (class) of CARs  compute the similarity matrix of CARs belonging to group i by using one of the distance metrics: WCB | WCI | WCD  do cluster to obtain the cluster_heights // HCCL algorithm is used to cluster the CARs  Find the optimal number of clusters C by using cluster_heights  perform clustering to identify the clusters of CARs // HCCL algorithm is employed with defined parameter (number of cluster) C  assign zero to j while(j < C)do // A representative CAR is extracted for each cluster  extract a representative CAR among CARs in cluster j // RC method is utilized to extract a representative CAR defined in Algorithm 1  add representative CAR to the model  jump to next cluster, increase the value of j by 1  End  jump to next class, increase the value of i by 1  End  return model
```  
**Algorithm 2** **Developed Associative Classification Model**  
Algorithm 2 describes every step of the proposed model. The model is given a dataset with minimum support and confidence constraints, which are used to produce class association rules. CARs are organized into groups based on their class labels in line 3. For each group of CARs (lines 4-14), the hierarchical clustering algorithm complete linkage method (HCCL) builds the distance matrix using one of the distance measures described in Section 3.2.2 (line 5), computes the cluster heights (distances between clusters) using the distance matrix in line 6, and then uses these heights (distances) to calculate the ideal number of clusters (line 7). Then, to locate the clusters of CARs, the HCCL method is employed once again. Lines 10-14 extract the representative CAR for each cluster using the technique outlined in Section 3.4 and then incorporate it into our final classifier. The following classifiers were constructed: CCC1 was built based on WCB; CCC2 was formed based on WCI; CCC3 was formed based on WCD; and all algorithms used the RC method defined in Algorithm 1 for extracting a representative CAR (based on cluster center).
# Post-Flood UAV-Based Free Space Optics Recovery Communications with Spatial Mode Diversity
## 6 Conclusions  
A 4 \(\times\) 2-channel UAV-based spatial mode diversity FSO system was developed for post-flood recovery communications, achieving 2.4 Mbps for a distance of 400 m, under various weather conditions. Spatial mode diversity was realized by using distinct linearly polarized spatial modes in the primary and backup channels. Backup channels were used when the SINR in the primary channels declined below 25 dB. SINR plots from the composite channel showed that dips in the SINR from atmospheric fluctuations were successfully mitigated through spatial mode diversity. The power in the dominant modes increased by 10% to 19% through temporal switching to backup channels during SINR dips in the primary channels. Hence, this provided uninterrupted transmission, even under heavy rain. The spatial mode diversity scheme improved the BER by 38% to 55% compared to the original BER. In the future, the number of modes used for spatial mode diversity may be extended in conjunction with the maximal-ratio combination for better signal quality. The UAV-based spatial mode diversity FSO system is valuable for rapid recovery communications after natural disasters, especially when conventional ground base stations are damaged. Further analysis of nonlinear time-varying deterministic channel characteristics would be valuable for emulating atmospheric turbulence under various weather conditions, toward the implementation of more efficient turbulence compensation approaches.  
Conceptualization, A.A. and T.-K.N.; formal analysis, A.A., N.A., T.-K.N. and M.B.J.; funding acquisition, A.A.; investigation, A.A. and A.M.R.; resources, N.A.; software, M.B.J. and T.-K.N.; supervision, A.A. and T.-K.N.; validation, N.A. and M.B.J.; visualization, T.-K.N. and A.M.R.; writing--original draft and revisions A.A., T.-K.N. and M.B.J. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful for the financial support received from the Sunway University Individual Research Grant GRTIN-IGS-DCIS[S]-01-2022 and the Sunway University Elasticities Research Cluster Grant STR-RCGS-E-CITIES[S]-004-2021.  
The authors wish to express their appreciation to Telekom Malaysia (TM) Berhad and TM Research & Development Sdn. Bhd. for collaborating with us on this research.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Procedures  
The races were filmed with six fixed JVC GY-DV300 video cameras (Japan Victor Company, Yokohama, Japan) located at the main stands and operating at 50 fps (frames per second; shutter speed: 1/1000), similar to that previously used in other studies [5; 9; 11; 12; 15]. Four cameras had a predominantly side view: camera one recorded the first 13 m; camera two from 13 to 30 m; camera three from 30 to 47 m; camera four the last 13 m (47 to 60 m) of the race. To avoid visual occlusion of the athletes, cameras five and six were placed to capture the frontal view: camera five recorded the first 30 m (including the referees' starting shot) and camera six the last 30 m. A common event (foot landing or take-off) in the field of view of two different cameras were employed for the camera synchronization.  
An experienced observer manually digitized the hurdles' foot landing and take-off points from the race footage using Photo 23D software (Technical University of Madrid, Spain) [20]. The official lane marks were employed as control points (six in each camera) for calibration purposes and direct lineal transformation algorithms [21] were used to reconstruct the real coordinates (in meters) from the screen coordinates (in pixels). Thirty points of known spatial coordinates (different from those used for calibration purposes) represented by the official lane marks and uniformly distributed in the field of view were used for error estimation. The root mean square error [22] was less than 0.04 m for the step length (x-axis) and less than 0.02 m for the step width (y-axis) on the six cameras, providing a validity of the measurements similar to those of other previous research on race analysis [23]. The step length and step width were calculated by the difference on the x (longitudinal) or y (transverse) axes, respectively, of the most forward point of the foot on the ground during successive contacts on it.  
The definition (Figure 1) proposed by [PERSON] and [PERSON] [9] was used for the organization of the temporal and spatial parameters of the five hurdle-unit phases of the 60 m hurdles, which integrated the preparatory step, hurdle step (divided into take-off distance and landing distance), landing step, and recovery step. The calculation of the split times used the same sequence, including the time elapsed from the beginning of the preparatory step to the beginning of the next preparatory step. Alternatively, the split times were also calculated from the landing step to the next landing step as done by the temporal studies [4; 5; 6; 7], for comparative purposes.
## 2 Related Work
The forwarding metric in these protocols is similar to that of SPEED, where the packet deadline is mapped to the velocity requirement. When a node has data to forward, the node searches for the node that has the highest estimator module that calculates the packet delay from the sender to its potential forwarder. The protocols work well in enhancing real-time routing and outperform SPEED in terms of time deadline success ratio. To deal with path failures and sleep nodes, a real-time routing protocol [20] has been proposed by applying neural networks (NN) to SPEED. Through NNs, the protocol evaluates QoS parameters and selects the optimized path to a destination based on the QoS parameters. However, the above-mentioned protocols focus only on real-time transmission toward a fixed destination (i.e., the sink in wireless sensor networks) which never moves during the network operation time. However, if the sink moves, the protocols cannot support real-time data dissemination. When a source disseminates data to a mobile sink in a desired time deadline, the mobile sink randomly changes its own location in a sensor field. As a result, the distance between the source and the mobile sink may be also dynamically altered. This means that the delivery speed should be recalculated by the immediate reflection of the distance alteration during real-time data dissemination to the mobile sink. EAR2 was proposed to overcome the limitation of the previous real-time protocols and support mobile sinks. EAR2 considers the expected location of a mobile sink based on its movement speed. EAR2 guarantees that the total summation of the unicasting time to the expected area and the flooding time within the expected area is smaller than the desired time deadline. However, EAR2 has a high flooding time and data loss problems because it uses a single flooding point. In this paper, EAR2M is proposed to reduce the flooding time and enhance the reliability of data transmission by using multiple flooding points. In another approach, to reduce the flooding time and energy consumption of EAR2, a grid structure-based real-time protocol [24] was proposed with both unicasting, multicasting, and one-hop forwarding. This protocol considers the expected grids of a mobile sink as well as its expected area and uses unicasting from a source node to the expected area, multicasting from the expected area to the expected grids, and one-hop forwarding from the expected grid to where the mobile sink is located in the expected grid. However, this protocol has a high energy consumption overhead for the construction and management of a grid structure for the whole network.
# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 2 Motivation
This approach is analogous to partitioning the nodes within the coverage of a centrally located base station into pizza slices, such that each slice only contains one node from each group (Figure 1, left). This can be compared to an onion ring partitioning approach, where nodes are grouped such that nodes with similar signal strength are placed in the same group. With line-of-sight and free-space propagation, this would result in the groups forming concentric shells, similar to the rings of an onion (Figure 1, right). In more complex channel environments, especially with shadowing or multipath components, the slices and rings will not have such simple geometric shapes as shown in the figure and may even consist of multiple disjointed regions. Although we focus on the pizza approach in this paper, with sufficiently many nodes, the two methods can be readily combined by composition, for example, dividing the nodes into onion rings and then into pizza slices, or vice versa. Figure 1: Two node partitioning approaches: “pizza” partitioning (**left**), in which nodes are grouped such that the nodes in each group have large differences in their directions from the base station, and “onion” partitioning (**right**), in which nodes with similar signal strength are grouped together. With line-of-sight and free-space propagation, shown here, the former is equivalent to dividing the nodes into angular slices, whereas the latter is equivalent to grouping the nodes into rings based on their distance from the base station.
# Study on Inertia Load Resistance Analysis Method of Light Truck Door Latch
## 2 Light Truck Door Latch and Its Inertia Load Resistance Analysis Process
### Analysis of Unlocking Causes of Light Truck Door Latch under Side Impact Load  
According to the holding mechanism of the light truck door latch, the following conditions will cause the latch to disengage from the striker:  
1. The structures of the fork-bolt, pawl, bottom plate, auxiliary bottom plate, and riveting shaft are greatly deformed or even damaged, which makes the holding function invalid.
2. The pawl rotates in the unlocking direction until it cannot block the rotation of the fork-bolt so that the fork-bolt cannot lock the striker, and the latch is unlocked and disengaged from the striker, as shown in Figure 5.  
Since the total mass of the light truck door lock body is about 300 g, under the action of a 60 g impact load, the inertial force of the entire latch is about 180 N and the generated stress is far less than the strength limit of the material. Therefore, the possibility of structural deformation or even damage was not considered in the inertia load resistance analysis. It was only necessary to study the inertia load resistance of the latch and analyze the rotation of the pawl. When the door is closed, the pawl is held in the fully latched position by the preload torque of the pawl torsion spring. In the normal opening condition, the opening force acts on the outside handle to overcome the torque of the outside handle torsion spring so that the counterweight mechanism rotates. The counterweight mechanism exerts a pulling force on the latch through the lever so that the outside opening toggle arm drives the pawl connect arm to move through the opening connect arm. When the pawl connect arm hooks the pawl connect shaft, it drives the pawl to rotate to unlock the latch. When subjected to a 60 g side inertia load, under the action of inertial force, the outside handle  
Figure 5: Schematic diagram of the disengagement of the latch and the striker. (1) Striker, (2) pawl, (3) pawl riveting shaft, (4) bottom plate, (5) fork-bolt, (6) auxiliary bottom plate, (7) fork-bolt riveting shaft.  
and the parts of the outer opening kinematic chain of the lock will generate a resultant moment that overcomes the preload torque of the torsion spring of the pawl, and the pawl will rotate. As a result, the latch is abnormally unlocked. Friction only makes the latch more difficult to unlock, so it can be ignored in calculations and simulations.