esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 4 Simulation Environment
### Tracking Bit Errors  
The first part of the simulation process is conceived of encapsulating an MPDU in a PPDU, before exposing it to AWGN. The distorted sequence is then demapped, decoded, descrambled, and compared to the initial sequence. The process is repeated till an adequate number of bit errors is reached or the maximal allowed number of bits has been transmitted. The two values are set to 100 and 10\({}^{8}\), respectively. The only exception is that at least 3 PPDUs must be successfully received before the simulation is allowed to terminate. This results in the generation of up to 48 random MPDU sequences spanning the longest supported PPDU data payload length (262 kB). The Monte Carlo simulation is repeated for each input MCS and \(\frac{E_{b}}{N_{0}}\) combination. Upon every PPDU transmission, the number of bit errors, the individual packet error, and the average number of decoder iterations over all codewords is stored.  
Pointed out by Figure 7, two blocks in the simulation framework change between the two study steps. In step number 2, the decoder may execute up to 10 decoding iterations, and exit prematurely if the early exit criterion is met. The criterion allows the decoder to stop execution if no bit errors are detected in the received codeword [46]. It is not altered during the simulation, as only the MCS and \(\frac{E_{b}}{N_{0}}\) combinations are changed. Step 3 adds decoder tuning, sweeping the number of allowed decoding iterations between 1 and 100. Regardless, the decoder verifies the early exit criterion upon every iteration. After PPDU transmission, the number of incurred iterations is always stored, as it is a vital part of the succeeding time-based simulation. Both steps 2 and 3 use decision threshold demapping during bit error tracking.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 5 Analysis of the Simulation and Test
### Simulation in NS2
#### 5.1.2 Influence of \(T_{ref}\) on the Network Convergence Time  
In the simulation, networks with 60, 100, 300, 600, 900, and 1500 nodes were tested separately, and the values of \(T_{ref}\) were set to 1 and 3 ms, respectively. The simulation result is shown in Figure 6. As can be seen, the convergence time became longer when the number of nodes grew. In the simulation with the same number of nodes, the synchronization convergence time was positively related to \(T_{ref}\). This shows that \(T_{ref}\) was the main factor affecting the convergence time of the whole network.
###### Abstract  
The conflict between exploitation of coal resources and environmental protection is highly pronounced in the Wanli mining area, located in the arid and semi-arid region of Inner Mongolia, China. The impact of mining operations has led to varying degrees of surface subsidence, which further threatens the ecological environment as coal extraction continues. The Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique offers significant advantages over traditional subsidence monitoring methods, particularly in complex terrain with vertical and horizontal valleys. This approach enables large-scale, low-cost, and all-weather monitoring. Based on 64 Sentinel-1A SAR images from 2018 to 2023, this study aims to promptly identify the location, deformation degree, and evolution characteristics of mining-induced subsidence within the study area using SBAS-InSAR techniques. The results indicate that the area affected by mining-induced subsidence covers 109.73 km\({}^{2}\), with a maximum cumulative subsidence of 283.41 mm and a maximum subsidence velocity of 46.45 mm/y. Additionally, during the field verification, 29 ground fractures, predominantly located along the precipitous borders of subsidence areas, were identified, validating the credibility of the monitoring results. This study demonstrates that SBAS-InSAR technology remains highly effective in the erosional terrain of the Loess Plateau. The monitoring data can help in-production mining to accurately identify the characteristics and patterns of surface subsidence induced by coal mining operations. It provides reliable policymaking data support and makes significant contributions to optimize cost-efficiency and guide targeted monitoring efforts in subsequent management work of the Wanli mining area as well as other mining areas.  
Wanli mining area; SBAS-InSAR; surface subsidence; feature analysis; field verification +
Footnote †: journal: Applied sciences  
Article
# Monitoring Perishable Commodities Using Cellular IoT:
## 1 Introduction
Finally, the system is capable of sending alarms immediately to a cell phone or via e-mail in case certain thresholds of the measured parameters are exceeded. The objective of this paper is to demonstrate that the designed system based on LPWAN communication is able to monitor in real time the most relevant parameters of perishable products during the whole postharvest supply chain thanks to the strategies employed for consumption optimization. This paper is divided into four sections. After the introduction in Section 1, Section 2 provides a description of the system architecture, including the hardware design, the components used and the software design for the microcontroller, communications, power optimization strategies and cloud programming. Furthermore, the methodology carried out for performing the tests is also described. In Section 3, the results obtained from the tests are presented, and, finally, conclusions and future work are included in Section 4.
# UAV Support for Mission Critical Services
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd Generation Partnership Project  
5G 5 th Generation  
5 GC 5G Core  
BVLOS 3 Beyond Visual Line of Sight  
C2  Command and Control  
CNF  Cloud-native Network Function  
CU  Centralised Unit  
DTN  Disruption Tolerant Networking  
DU  Distributed Unit  
EPC  Evolved Packet Core  
ETSI  European Telecommunications Standards Institute  
EU  European Union  
GCS  Ground Control Station  
GPS  Global Positioning System  
HAPS  High Altitude Platform System  
ICT  Information and Communications Technology  
IMS  IP Multimedia Subsystem  
IoT  Internet of Things  
ISM  Industrial, Scientific and Medical  
LEO  Low Earth Orbit  
LoraWAN  Long Range Wide Area Network  
LTE  Long Term Evolution  
LTE-M  LTE Machine Type Communication  
LTE-MC  LTE Mission Critical  
MANET  Mobile Ad hoc Network  
MANO  Management and Orchestration  
MCS  Mission-Critical System  
MEC  Multi-access Edge Computing  
MEO  Medium Earth Orbit  
MIMO  Multiple Input Multiple Output  
NASA  National Aeronautics and Space Administration  
NB-IoT  Narrowband Internet of Things  
NF  Network Function Virtualisation  
NFVI  NF Infrastructure  
NR  New Radio  
NS  Network Slicing  
NTN  Non-Terrestrial Network  
P25  Project 25  
POTS  Plain Old Telephone Service  
PPDR  Public Protection & Disaster Relief  
ProSe  Proximity-based Services  
QoS  Quality of Service  
RRH  Remote Radio Head  
SBA  Service-Based Architecture  
SCADA  Supervisory Control And Data Acquisition  
SMS  Short Message Service  
TDMA  Time-Division Multiple Access  
TEDS  TETRA Enhanced Data Service  
TETRA  Terrestrial Trunked Radio  
TETRAPOL  Terrestrial Trunked Radio Police  
UAS  Unmanned Aircraft System  
UAV  Unmanned Aerial Vehicle  
UE  User Equipment
# Mass Collaboration and Learning: Opportunities, Challenges, and Influential Factors
## 4 Analysis of Affecting Factors
### Organizational Structures
#### 4.1.1 Types of Organizational Structure  
Various types of organizational structures can be defined to meet the requirements of an organization or community. Some authors [17] categorize them into eight groups: (1) line organizational structure, (2) staff or functional authority organizational structure (3) line and staff organizational structure, (4) committee organizational structure, (5) divisional organizational structure, (6) project organizational structure, (7) matrix organizational structure, and (8) hybrid organizational structure. The organizational structures are also classified by Wikipedia into six categories: (a) pre-bureaucratic structure, (b) bureaucratic structure, (c) post-bureaucratic structure, (d) functional structure, (e) divisional structure, (f) matrix structure. In another classification [18] an organizational structure is sorted into five main classes: (1) simple form, (2) machine bureaucracy, (3) professional bureaucracy, (4) divisionalized form, and (5) adhocracy.  
This diversity of structures shows that in the long run different organizations or communities can take the advantages of different structures. However, neither one universal organizational structure is recommended that can be used for every organization or community, nor is there a clear way to select the best structure.
# Calibrating the SAR SSH of Sentinel-3A and CryoSat-2 over the Corsica Facilities
## 4 Discussion and Conclusions  
The configuration of the Sentinel-3A and CryoSat-2 passes allowed us to cross-compare the results obtained independently from both the Senetosa and Ajaccio sites. Our results confirm previous results that were obtained from SARAL/AltiKa together with GPS-based sea level measurements [3]. The SSH measured by the Ajaccio tide gauge is biased by \(\sim\)30 mm and our present study shows the origin of this offset. By taking into account this offset, we compared results from the Senetosa and Ajaccio sites for both the Sentinel-3A and CryoSat-2 altimeters. For the Sentinel-3A the site-difference between Senetosa and Ajaccio was \(-4\) mm (21-25 mm, see Figure 6) while for CryoSat-2 the difference was \(-14\) mm ((\(-751\))-(\(-737\)) mm, see Figure 9 from SAMOSA+ results). These comparisons show that no significant systematic error remains in the geodetic datum or in situ measurements. This is a very important result that gives us confidence also for the historical calibrations performed independently at each site. However, differences still exist at the millimeter level between the SSH bias obtained at the Senetosa and Ajaccio sites for Sentinel-3A and CryoSat-2 (respectively \(-4\) and \(-14\) mm). This probably reflects some remaining errors in the altimeter measurement system (coastal contamination, sea state bias,... ) and/or in the local geodetic datum of both the Senetosa and Ajaccio sites. Finally, our analysis of the wet tropospheric corrections available for the Sentinel-3A calibration showed that the effect of land contamination on the radiometer was \(-\)\(-\)20 mm, which is actually the main part of the SSH bias (\(+22\)\(\pm\) 7 mm). In terms of the range itself, we can consider that SAR measurement system on Sentinel-3A as \"unbiased\". For CryoSat-2, it is important to recall that the product used from G-POD is based on baseline B, so to be comparable to the current baseline C, one must remove the known range bias of 673 mm to the SSH biases determined in this study: when using baseline C products, the CryoSat-2 SSH bias is \(-73\)\(\pm\) 5 mm. Last but not least, the SSH biases for both Sentinel-3A and CryoSat-2 were compared to the range biases determined with transponders located respectively in Crete and Svalbard, and the remaining differences (respectively 22 mm and 24 mm) between these two determinations can be due to geodetic datum errors or geophysical corrections and, notably, the sea state bias that is not affecting transponder processing. This is probably the first time in altimetry that the results from transponders and in situ SSH calibration agree at this level of accuracy.  
This study was conducted and financed thanks to Centre National d'Etudes Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), and the French Ministry of Research.  
[PERSON] and [PERSON] designed the study, analyzed the data and wrote the paper. All the other co-authors helped in paper writing and analysis.  
The authors declare no conflict of interest.
###### Abstract  
Polarization encoding has been extensively used in quantum key distribution (QKD) implementations along free-space links. However, the calculation model to characterize channel transmittance and quantum bit error rate (QBER) for free-space QKD has not been systematically studied. As a result, it is often assumed that misalignment error is equal to a fixed value, which is not theoretically rigorous. In this paper, we investigate the depolarization and rotation of the signal beams resulting from spatially-dependent polarization effects of the use of curved optics in an off-axis configuration, where decoherence can be characterized by the Huygens-Fresnel principle and the cross-spectral density matrix (CSDM). The transmittance and misalignment error in a practical free-space QKD can thus be estimated using the method. Furthermore, the numerical simulations clearly show that the polarization effect caused by turbulence can be effectively mitigated when maintaining good beam coherence properties.  
QKD; polarization properties; free space; channel modeling +
Footnote †: journal: _Entropy_
## 6 A Discussion of the Qualitative Aspects (Joint Conditions) of Existing GSI Charts
Comparison of P32\({}_{\text{UNIT VOLUME}}\) Estimates Using the V-GSI Method, [PERSON]'s P32\({}_{\text{REV}}\) Method, and P32\({}_{\text{DFN}}\) Using DFN Modelling  
The true spacings in Table 2 used in [PERSON]'s example are based on real measurements that are corrected for orientation bias. These same data can be used to quantify both VFC and P32\({}_{\text{UNIT VOLUME}}\) using the V-GSI method and attain similar values compared to [PERSON]'s result, as shown in Figure 11. Table 3 shows the results of the quantification of VFC (P30\({}_{\text{UNIT VOLUME}}\)) and P32\({}_{\text{UNIT VOLUME}}\) using Equations (1) through (4) using [PERSON]'s data. With reference to Table 3, a mean VFC of 2.62 fractures/m\({}^{3}\) is estimated. The lower and upper VFC 95% confidence limits on the mean VFC are estimated at 1.85 fractures/m\({}^{3}\) and 5.07 fractures/m\({}^{3}\) respectively. Then using Equation (3) this translates to a mean P32\({}_{\text{UNIT VOLUME}}\) of 3.16 m\({}^{-1}\). When the estimated confidence limits on spacings are considered, the lower limit P32\({}_{\text{UNIT VOLUME}}\) is estimated at 2.24 m\({}^{-1}\) and an upper limit of P32\({}_{\text{UNIT VOLUME}}\) is estimated at 6.13 m\({}^{-1}\), respectively. These results are also plotted in Figure 11 (green arrows and dashed lines). The mean P32\({}_{\text{UNIT VOLUME}}\) and limits are not remarkably different to the range of P32 values found by [PERSON] using his new method, or P32\({}_{\text{DFN}}\) estimates provided by [PERSON] as a check (cf Figure 11). Certainly, a similar range of P32 is obtained for all three methods of quantification (Figure 11). The slight overestimate of the mean P32\({}_{\text{UNIT VOLUME}}\) (3.16 m\({}^{-1}\)) compared to [PERSON]'s estimate (2.46 m\({}^{-1}\)) and the P32\({}_{\text{DFN}}\) estimate (2.65 m\({}^{-1}\)) is attributable to the fact that Equation (3) assumes all the fractures for each set are persistent within the unit sphere volume (1 m\({}^{3}\)). In [PERSON] example it is evident that the mean size of the fractures per set (Table 2) is slightly less than if the full diameter of an intersecting fracture in the unit sphere volume is assumed. Given the close fit of the P32 estimates using [PERSON]'s data and the P32\({}_{\text{UNIT VOLUME}}\) estimate, it is contended that Equation (3) provides a reasonable first estimate of P32 despite the slight overestimate of the mean size of the fractures. A fracture size correction factor Pi is introduced below for inclusion in the VFC estimates, where the fracture sizes are less than the diameter of a unit sphere, to address this type of limitation.
# A Self-Supervised Tree-Structured Framework for Fine-Grained Classification
## 4 Experiment
### Experimental Setting
#### 4.1.1 Datasets  
The efficacy of the tree-structured classifier is assessed by evaluating its performance on benchmark datasets for fine-grained visual classification of aircraft, birds, and cars. These datasets are FGVC aircraft [28], CUB 200 2011 [29], and Stanford car [30], respectively. Notably, these datasets are established benchmarks for fine-grained aircraft classification. The aircraft dataset consists of 10,000 images and encompasses 100 subclasses, the bird dataset comprises 11,788 images and contains 200 subclasses, and the car dataset comprises 16,191 images and 196 subclasses. Furthermore, the datasets are uniformly segregated into three sets, namely, training, validation, and testing, with a ratio of approximately 1:1:1.
# Hybrid Particle Swarm Optimization Genetic LQR Controller for Active Suspension
## 1 Introduction
GA has better converge performance, and can expand the search space to avoid falling into local solutions. According to the research in [22], to optimize the weight matrixes, the GA was successfully used in LQR control for active suspension. However, in the optimization based on GA, the search speed is slow and the robustness is poor and prone to produce early convergence [25]. To overcome the weak points of the LQR control, ref. [21] applied the PSO algorithm to optimize the weight coefficients of matrix Q and matrix R. PSO is computationally fast, simpler, more efficient, and converges easily compared with other algorithms [26]. However, this method is prone to falling into local optima [28]. The artificial fish algorithm is essentially similar to PSO, an optimization algorithm that simulates animal predation [27]. As a result, in this paper, a hybrid particle swarm optimization genetic algorithm is applied to optimize the LQR controller. The optimization method proposed in this paper fully considers the computational speed of PSO and the global search capability of GA. Therefore, the optimization method proposed in this paper has both optimization speed and global search capabilities. In the active suspension systems control, the dynamics of the suspension system should be collected in real-time; however, the installation of a large number of sensors will not only affect the cost of the vehicle, but will also reduce the stability of the vehicle control system [29]. Therefore, this paper constructs a full-order state observer to observe the condition of the active suspension. The main contributions of this paper are as follows: The random road input model and quarter car suspension model are established. A hybrid optimization algorithm that combines the advantages of the particle swarm optimization and genetic algorithm is used to optimize the weight matrix Q and matrix R in the LQR control. Moreover, the new controller is applied to the active suspension system. A full-order state observer is designed to observe the state of the active suspension. The simulation analysis of the active suspension system under case 1 and case 2 is carried out, and, lastly, the conclusion based on the data is presented.
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria
### Dynamic Buckling Criteria  
The dynamic buckling problem can be solved if the relationship between the static buckling force and maximum force is established. In this section, the relationships between the maximum forces of collision and static buckling forces according to Equation (3) are investigated.  
Figure 18 shows the X-stress of the shell under the external pressure of 15 MPa. At the beginning, the initial state is the same between the cases of velocities of 2.8 m/s and 2.7 m/s when the pressure load and configuration of the shell are the same.  
Figure 19a-h shows the mechanisms of collision for the cases with impact velocities of 2.8 m/s and 2.7 m/s. It demonstrates that in the velocity case of 2.7 m/s, the deformation of the spherical shell can be restored elastically after the collisions. However, when the velocity is larger than 2.7 m/s (even at 2.8 m/s), the deformation of the spherical shell cannot be restored after the collisions, which became larger and larger under the load of pressure and contact force. In that case, the shell can be considered to have buckled under the dynamic collisions.  
Figure 17: Collision contact force vs. time with fitting result, \(R=2.0\) m, \(t=0.06\) m, \(q=15\) MPa, and \(E=210\) GPa.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Thickness** & \(k_{M}\) & \(C_{M}\) & \(Knt_{M}\) & \(Cm_{M}\) & \(k_{M}/knt_{M}\) & \(C_{M}/Cm_{M}\) \\ \hline
0.04 m & 7.4 & 10 & 4.3 & 5.9 & 1.72 & 1.69 \\
0.06 m & 10.8 & 15.5 & 6.5 & 9.0 & 1.67 & 1.72 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The result of regression analysis on Equation (12).  
Figure 19: Cont.  
Figure 18: The initial state of shell in the X-stress (Pa). (a) 2.8 m/s; (b) 2.7 m/s.  
Figure 20 illustrates the Z displacement of three nodes, as previously shown in Figure 19h, which are marked as yellow spots with velocities of 2.8 m/s and 2.7 m/s, respectively. It can be found that the displacements are the same before collision contact, but they can be very different from the beginning of contact. In the velocity case of 2.7 m/s, the displacement will return back symmetrically, whilst in the case of 2.8 m/s, the displacement will return back in a steep slope (non-elastic), which implies that the shell had buckled already (the same conclusion as with Figure 19).  
Figure 20: The X displacement time history at velocities of 2.8 m/s and 2.7 m/s. **(a)** The case of 2.8 m/s velocity; **(b)** The case of 2.7 m/s velocity.