esa-sceva/satcom-chunk-collection huggingface dataset:
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 3 Results
### Seasonal and Inter-Annual Variability in the Relationship Between GPP and Phenology  
Both the phenocam- and UAV-derived VIs were found to exhibit a strong correlation with GPP90. Correlation coefficients were \(>\)0.70 (r\({}_{\rm s}\), \(p<\) 0.01) for all indices except the NGRDI, for which correlation was moderate (r\({}_{\rm s}\) = 0.57, \(p<\) 0.05). Variability in the strength of the relationship between species-level VIs and GPP90 was low. Using the index reNDVI1 as an example, correlation between GPP90 and reNDVI1 for the survey area was 0.78 (r\({}_{\rm s}\), \(p<\) 0.01) and species values ranged from r\({}_{\rm s}\) = 0.72 (_Sphagnum_ spp.) to r\({}_{\rm s}\) = 0.78 (_C. vulgaris_; \(p<\) 0.01 for all).  
The phenocam dataset, with its high temporal coverage, facilitated examination of the above relationships in more detail. For the complete time-series (all years combined), changes in canopy greenness were observed to follow the same seasonal cycle as daily GPP90, and these two variables displayed a strong correlation (r\({}_{\rm s}\) = 0.88, \(p<\) 0.01). This bulk analysis, however, masks a large amount of seasonal variability, as illustrated in Figure 9. For example, during the peak growing season when the canopy was fully developed, the relationship between daily GPP90 and \(G_{cc}\) was not statistically significant for any year. The strongest relationships between greenness and GPP90 were observed during green-down and green-up (Figure 9). One discrepancy was the weak correlation between GPP90 and \(G_{cc}\) observed in the 2019 green-up (r\({}_{\rm s}\) = 0.39, \(p<\) 0.05). These results indicate an inter-annual and seasonal variability in the relationship between observed vegetation greenness and GPP at Aucheworth Moss.  
Figure 8: Change in selected VIs over Dryspell-1 and Dryspell-2 for each dominant species. Shown is the percentage change with respect to the seasonal amplitude for each VI (i.e., the maximum- minus the minimum species-averaged value during the year). Positive values (green) indicate an increase in greenness, whereas negative values (yellow) denote a reduction in greenness. Dryspell-1 plots (upper row) show change in species-level VIs from the flights on DOY 168–153; Dryspell-2 plots (bottom row) show VI change from flights on DOY 215–204.
# Climate-Quality Calibration for Low Earth-Orbit Microwave Radiometry +
## 4 Orbit Selection  
The orbit should facilitate inter-satellite calibration of instruments on several weather satellites. A 90\({}^{\rm o}\)-inclination orbit would intersect weather-satellite orbits at a distribution of latitudes [3]. Also, that orbit would not precess in sidereal time, so the measurements would cycle through local solar time and seasonal cycles (see Table 1 in [2]), unlike the very slow drift in equator-crossing time which created problems with the measurements from some weather satellites [26]. Hence it would provide a truer global average than a sun-synchronous orbit.  
A further benefit of a non-sun-synchronous orbit would be the possibilty of modeling the climatology of the diurnal and seasonal cycles of temperature, as a function of location. Better knowledge of the diurnal cycle could reduce uncertainties in the trends of tropospheric temperature inferred from MSU measurements starting from 1979. For example, one can calculate from Table 2 in [26] that 75% of the total variance of uncertainty in the global lower-troposphere temperature trend for the period 1979-2009 is due to the diurnal adjustment (which in that case was obtained from a general-circulation model). In subsequent work [27; 28] a second-harmonic correction to the model-simulated diurnal adjustment was derived from the satellite measurements, yielding improved agreement of trends from different satellite radiometers. This suggests that the availability of non-sun-synchronous satellite measurements might also stimulate improvements to the treatment of atmospheric energy flow in general-circulation models (which now can be tested only against sun-synchronous satellite measurements and twice-daily radiosonde launches), thus leading to better representation of the diurnal cycle in the models.  
Finally, it is desirable for the spacecraft to use propulsion to maintain orbit altitude, because altitude decay changes the relation of scan angle to earth incidence angle [29].
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Watermark Extraction Module
#### 4.3.1 Anti-Interference Mechanism  
In the complex network environment, there are network jitters and other factors that affect the arrival delay of data packets, resulting in deviation from the original set delay. In order to solve this problem, a watermark anti jamming mechanism is set up in the system.  
The time delay set of interval arrival \(E\) and the step length \(\Delta\) between the arrival delays of adjacent packets in the _L-Bits to N-Packets_ coding scheme are chosen to modify the watermark with Equation (13).  
\[C(x)=\left\{\begin{array}{ll}T_{1},&x\in[T_{1}-\Delta/2,T_{1}+\Delta/2)\\ T_{2}+\Delta,&x\in[T_{2}-\Delta/2,T_{2}+\Delta/2)\\ \vdots&\\ T_{n}+\Delta(n-1),&x\in[T_{n}-\Delta/2,T_{n}+\Delta/2)\end{array}\right. \tag{13}\]  
In Equation (13), \(T_{i}=T_{\min}+(i-1)\Delta(i=1,2,\cdots,n)\). If the interval arrival delay extracted by the controller is \(x\) and \(x\in[T_{i}-\Delta/2,T_{i}+\Delta/2)\), the interval arrival delay embedded in the sender is considered to be \(T_{i}\). Thus, the received delay sequence can be processed to obtain the original set sequence.
# Orthopedics-Related Applications of Ultrafast Laser and Its Recent Advances
## 5 Future Direction and Conclusions
### Technical Challenges  
Despite the readily apparent advantages with ultrafast lasers, there has been limited adoption to date, even in applications where the benefits are obvious. The most pressing challenge surrounding the use of ultrafast laser is the ablation rate compared to conventional laser ablation systems (and their associated drawbacks including carbonization) and mechanical drilling. It has been described in multiple studies that a femtosecondlaser system provides an ablation rate of approximately 1 um/pulse in hard tissue such as bone at a repetition rate of 10 Hz [36; 103]. This is compared to up to 30 um/pulse for Er:YAG laser systems at a pulse rate of 4 Hz. At this repetition rate, the ablation rate would be 10 um/s, compared to 120 um/s for conventional laser systems. This can be further compared to mechanical drilling rates of 4-5 mm/s with larger burrs/drill bits that can achieve the ablation volume in a much shorter time. Research is ongoing into higher repetition rate femtosecond laser systems (upwards of 40 KHz) that would mitigate some of the differences through matching the ablation rate; however, the volume ablated per time is still inherently mismatched due to the size of mechanical drilling devices compared to the spot size of ultrafast laser devices [44]. Due to the time constraints of many public operating rooms and the number of patients requiring procedures such as hip and knee arthroplasty, the ablation rate would be required to come close to matching the shaving/feed rate of high-speed mechanical drills for clinical translation.  
A second technical challenge for ultrafast laser systems is the adoption of these systems into endoscopic devices for minimally invasive surgery. These approaches are becoming more prevalent in all surgeries including orthopedic due to the potential for reduced tissue trauma and quicker recovery. In these approaches, such as transsphenoidal access or laminectomy, the limitation of ultrafast laser fibers in terms of attenuation and dispersion characteristics have precluded the adoption into endoscopes that are currently in use. Although fibers have been developed for implementation into an endoscope, the cutting power, modal quality, dispersion and attenuation are dependent on the bending radius, compromising cutting efficiency or ablation rates in these applications altogether. Ongoing research of fibers for integration into endoscopes, which are flexible enough to generate continuous power input, provides the basis to suggest that this problem will be overcome and adoption would be able to proceed [104; 105; 106].
## 2 Materials and Methods
### Models' Architecture Setup  
To compare the capacity of the ML models to precisely predict the flow characteristics, three DL models were built and tested using the PyTorch library for the Python language. These were, namely, Convolutional Long Short-Term Memory (ConvLSTM), 2D CNN + LSTM and the Fourier Neural Operator (FNO).  
For each one, several architecture configurations were tested, with the aim to identify the ones that provide the greatest balance between performance and computational load. The models were implemented in a standard form, already including the boundary conditions data in the input dataset. A set of configurable hyperparameters was proposed, and because exhaustively testing possible configurations would incur a prohibitive computational time, 10 random configurations were tested for up to 50 epochs instead. We could perceive that it was enough to achieve the best results for each base model. The best architectures were chosen to be more deeply trained, using up to 300 epochs. Further details about the tested architectures for each model are presented in the next subsections.  
During all the steps of the work, the training parameters were fixed. We selected the cases of Re = 1000, 2000, 4000, 6000, 14,000, 16,000, 18,000 and 20,000 for the data for training and the Re = 10,000 case for testing, to develop the models' architecture and compare their overall performance under the same conditions. The training and testing data included only the first 10 s of the simulation, as most flow dynamics were contained in this time frame. This simulation, with timesteps of 0.1 s, was divided into sequences of 3 inputs and 1 output, so our models were trained to forecast t\({}_{i+3}\) given the input sequence {ti, ti\({}_{i+1}\), ti\({}_{i+2}\)}.  
The learning rate was set to 1 \(\times\) 10\({}^{-3}\), and a scheduler was used to reduce its value by a factor of 2, whenever the validation loss stopped improving. The number of epochs could vary, because we used a \"scheduler patience\" of 10 epochs and implemented early stopping with a patience of 50 epochs. We used the overall Root-Mean-Square Error (RMSE), including the velocity components as well as the pressure, to assess the models' performance on the training and testing sets, as well as to plot predictions to visualize the models' forecasting accuracy. It is important to clarify that, even though the pressure field was also used to calculate the RMSE along with the x and y velocity fields, its value is always about 10 times smaller than theirs. In this sense, we opted to present the RMSE in units of m\(\cdot\)s\({}^{-1}\), which gives a physical meaning to the error without incurring any perceptible deviation.
## 5 Stability of System  
The stability of a system is the range within which the main parameters in the system are allowed to deviate from their theoretical or default values, provided that specific requirements are met. In general, the wider the allowed range, the better the stability of the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Conventional Systems** & **Our System** & **Improvement** \\ \hline BTB & \(-\)23.63 dBm & \(-\)23.953 dBm & 0.323 dBm \\ \hline
10 km & \(-\)23.05 dBm & \(-\)23.467 dBm & 0.417 dBm \\ \hline
20 km & \(-\)22.31 dBm & \(-\)22.793 dBm & 0.483 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 2: The received power of the PD1 at BER = \(10^{-9}\).  
Figure 14: Eye diagrams at different transmission distances in our scheme: (**a**) BTB; (**b**) 20 km; (c) 40 km; (**d**) 60 km.  
Figure 15: The relationship of the BER of the downlink with the received power of PD1.  
The parameter values of the devices chosen in the simulation experiments are based on the theoretical analysis values and default values of the simulation tool. In practical applications, due to the variance in the environment and the limitations of the device fabrication process, those values often deviate from the theoretical values or default values; this will inevitably affect the Q factor of the system.  
For the general requirements of digital communication systems, the BER is required to be lower than \(10^{-9}\), or a Q factor greater than 6 is needed.  
In practical systems, the factors that most affect the Q factor are the extinction ratios of the MZMs, the modulation index P of PM, and the gain G of the EG.  
The larger the MZM extinction ratio, the more effective it is in suppressing the undesired sidebands. According to the theoretical analysis presented above, the P and G only directly affect the realization of modulating data on the +8 th-order sideband.
## 2 Materials and Methods
### Oil Spill Model
#### 2.2.2 Weathering of Oil Spill  
Oil spills entering the river are affected by factors such as water flow, wind, and light radiation, and undergo processes such as evaporation, emulsification, dissolution, sedimentation, photo-oxidation, and biodegradation, resulting in changes in the physicochemical properties of the oil [20]. This study focuses on the evaporation and emulsification behavior of oil spills.  
Where oil evaporation is a short-term weathering process, evaporation will be more obvious in the early stage after the oil spill enters the water body. The [PERSON] model [40] was used to calculate the evaporation of an oil spill with the following equation:  
\[EVAP=\frac{K_{2}\cdot P_{vp}\cdot A}{R\cdot T}\cdot f\cdot MW \tag{11}\]  
where \(P_{vp}\) and \(A\) are the vapor pressure and the slick area of each particle in contact with the water surface; \(R\) is the gas constant, \(R\) = 8.206 \(\times\) 10\({}^{-5}\); \(T\), \(f\), and \(MW\) are the temperature, the fraction of the evaporative oil component, and the molecular weight; \(K_{2}\) is mass transfer coefficient, which can be calculated by:  
\[K_{2}=0.0292\cdot w^{0.78}\cdot D^{-0.11}\cdot Sc^{-0.67}\cdot\sqrt{\frac{MW+2 9}{MW}} \tag{12}\]  
where \(w\) and \(D\) are the wind speed and the diameter of each particle area in contact with the water surface; \(Sc\) is Schmidt number, \(Sc\) = 2.7.  
Emulsification is the process by which oil and water mix to form a mixture, and it is encouraged by wind and river turbulence, which usually occurs a few hours after the oil spill enters the water column. The emulsification process uses a first-order water release equation [41]:  
\[wateruptake=K_{em}*(U+1)^{2}\cdot\frac{(Y_{max}-Y_{w})}{Y_{max}} \tag{13}\]  
\[waterrelease=-\alpha\cdot Y_{w} \tag{14}\]  
where \(Y_{w}\) and \(Y_{max}\) are the water fraction and the maximum water fraction; \(K_{em}\) is the emulsification rate constant, \(K_{em}\) = 2 \(\times\) 10\({}^{-6}\) s/m\({}^{2}\); \(U\) is the wind speed; \(\alpha\) is water release rate which is related to the emulsion stability index \(S\).  
\[\alpha=\left\{\begin{matrix}\alpha_{0}-(\alpha_{0}-\alpha_{0,67})S/0.67&\quad \text{for}&S<0.67\\ \alpha_{0,67}[(1.22-S)/(1.22-0.67)]&\quad\text{for}&0.67\leq S<1.22\\ 0&\quad\text{for}&S\geq 1.22\end{matrix}\right. \tag{15}\]  
where \(\alpha_{0}\) and \(\alpha_{0,67}\) are the water release for unstable emulsion with \(S\) = 0 and the mesostable emulsion with \(S\) = 0.67. The stability index \(S\) is given by:  
\[S=X_{a}\cdot\exp\left[K_{ao}\cdot(1-X_{a}-X_{w})^{2}+K_{aw}\cdot X_{w}^{2} \right]\cdot\exp\left[-0.04\cdot(T-293)\right] \tag{16}\]  
where subscript \(a\), \(w\), and \(o\) represent asphaltene, wax, and other chemical components; \(K_{ao}\) and \(K_{aw}\) are 3.3 and 200 at 293 K; \(X_{a}\) and \(X_{w}\) are the fractions of asphaltenes and wax; \(T\) is the temperature.
## 3 Distributed Target Tracking Systems
### Phase III: Target Positioning
#### 3.3.2 Position Estimation Refinement  
Since the AOA measurement quality highly depends on the communication environment, this subsection presents estimation refinement criteria based on noisy AOA information, TOA information, and an angle-of-arrival neural networking (ANN) model. The purpose of the ANN model is to coordinate the initial target position estimate, the initial sample space, and the measurement information in a scenario with multiple tasking sensors such that effectively adjustment of angle information and a better sample area for particle filtering can be provided.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 2 Background
### Covert Channel Methodology
#### 2.1.1 Threat Model  
A typical threat model for covert channel communication assumes two entities at any abstraction level (stand alone PCs or chiplets, or cores within a monolithic processor), where one device is the transmitting entity, while the other device is the receiving entity. It should be noted that the transmitting and receiving entities can be executing on the same host, two separate hosts that are connected via a network, or two separate hosts that are not connected  
Figure 2: Timeline of various network-based, host-based and physical covert channels (**a**) TCP timestamps [28], (**b**) Cache Access [24], (**c**) acoustic channels [30], (**d**) Bitwhisper [33], (**e**) Led-it-GO [34], (**f**) Bitjabber [31], (**g**) PowerHammer [32].  
to one another in any fashion [5]. These two systems are capable of communicating while thwarting the underlying system security policy (i.e., the communication between the two devices is unknown to the host). The access control policy can be described as the following: the transmitting device has access to sensitive data (for example, by operating in a secure zone), but the transfer of data to the receiving device is not allowed. The attack model assumes that malicious code is able to execute on the transmitting entity in order to encode and transmit the data through various side-channel signals (thermal, EM, power, optical). The signal is received via sensors and decoded to retrieve the original, confidential data. The receiving device is assumed to be unsecured and, thus, is able to transmit the confidential data to the external world. The receiving and transmitting entities can be two separate IoT devices [32], two personal computers [31], two cores within a multi-core processor [37], or even two FPGAs in a data center [38].
## 3 Detection of Coal Burst Risk Active-Passive Seismic CT
### Coal Burst Prevention Aiming the \"Square Effect\" Based on the CT Inversion  
Based on the P-wave velocity distribution inversed by active-passive CT, the high-stress areas, which stand for the highly risky area of coal burst, can be determined during the \"square effect\" stage, as shown in Figure 7, and destress scheme can be designed more accurately. As analyzed above, the main reasons for the high stress and coal burst  
Figure 6: Cloud map of the P-wave velocity in the coal seam at the beginning stage of LW23908.  
Figure 7: Cloud map of passive wave CT inversion on May 10.  
danger are the depth and hard-thick roof; therefore, the hard-thick roof fracturing is the most feasible solution, and coal seam large-diameter distress is the auxiliary. Aiming the high-stress area 2 detected on 10/5/2022, the technical measures and parameters were as follows:  
1. Coal seam large-diameter borehole destress. The destress boreholes with a diameter of 150 mm were 25 m deep into the coal seam, a spacing interval of 2.0 m, and carried out in both roadways;
2. Deep-hole blasting of hard-thick roof. Three blasting holes as a group, marked as 1#, 2#, and 3#, distributing a fan shape on the section, as shown in Figure 8. The 1# and 3# blasting holes are both 35 m long with an elevation of 5\({}^{\circ}\) and 75\({}^{\circ}\), respectively, while 2# is 50 m with a 45\({}^{\circ}\) elevation. The diameters of the three blasting holes are all 89 mm; the charge length of 1#, 2#, and 3# blasting holes are 10 m, 15 m, and 10 m that are 40 kg, 60 kg, and 40 kg explosives, and the diameter of the explosive tube is 65 mm. The interval between the blasting group is 10 m; therefore, a total of 20 groups were implemented in each roadway. The induced cracks developed after blasting, which can reduce the strength of the hard-thick roof and the coal burst danger effectively [37]. The deep-hole blasting used to fracture the hard-thick roof can stimulate obvious tremors in the roof strata and could be recorded by the SOS microseismic system, but the energy of each blasting varies significantly, which reflects both the stress state and the difference in blasting effect. In this case, tremors provoked by deep-hole blasting can reach 10\({}^{5}\) J and most in the range of 10\({}^{3}\)\(\sim\)10\({}^{5}\) J.
# Vortex-Induced Vibration of Deep-Sea Mining Pipes: Analysis Using the Slicing Method
## 4 Numerical Simulation and Results
### Vortex-Induced Vibration Response of Deep-Sea Mining Pipe
#### 4.1.2 Modal Characteristics Analysis of Vortex-Induced Vibration of Deep-Sea Mining Pipe  
Figure 8 illustrates the envelope of the instantaneous dimensionless displacement in the cross-flow direction of a deep-sea mining pipe at different reduced velocity values: ur = 8, 18, 36, 10, 21, and 38.  
Our analysis reveals distinct vibration states: At ur = 8, the mining pipe exhibits a primary mode with the maximum displacement at the pipe's bottom reaching approximately 0.72 d. At ur = 18, it transitions to a second-order mode, with displacement increasing to about 0.81 d. Further elevation to ur = 36 leads to a third-order mode, where the bottom displacement peaks at around 0.86 d. These modal transitions are vividly captured in the figure, demonstrating the envelope of dimensionless displacements across these states. As ur values escalate, not only do the number of vibration modes in the transverse flow direction multiply, but the manifestation of multimodal vibrations becomes increasingly pronounced.  
At reduced velocities of ur = 10, ur = 21, and ur = 38, a progressive transition in the vibration modes of the deep-sea mining pipe can be observed in the cross-flow direction. Specifically, the mining pipe transitions from a first-order mode to a second-order mode, then from a second-order mode to a third-order mode, and finally to a fourth-order mode as the reduced velocity increases. Concurrently, a noticeable reduction in the maximum displacement at the bottom of the mining pipe accompanies each modal transition. For instance, at ur = 10, the maximum displacement, y/d, at the pipe's bottom is approximately 0.31 d. At a reduced velocity of ur = 21, this displacement increases to about 0.74 d, indicating a larger amplitude of vibration. At ur = 38, while the mining pipe exhibits characteristics of a first-order master mode, the maximum displacement, y/d, at the bottom reaches about 0.79 d, further illustrating the dynamic behavior of the mining pipe under varying flow conditions.  
Figure 7: The response-time curve and the moving-track diagram of the down-flow and cross-flow displacement of each section of the deep-sea mining pipe.