esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory
### Recovery of Curve Fitting for Partially Overlapped Absorption Signal
#### 2.1.2 Multi-Peak Voigt Fitting  
The absorbance \(\alpha_{\
u}\) for multiple transitions can be further expanded, as shown in Equation (3) [20]:  
\[\alpha_{\
u}=\sum_{i=1}^{M}\alpha_{i}\phi_{\
u}(\
u;\
u_{0,i},\gamma_{i}) \tag{3}\]\(\phi_{V}(\
u;\
u_{0,i},\gamma_{i})\), which is generally explained by the Voigt function, can be represented by an individual absorption peak with a wavelength \(\
u\). Here, \(\alpha_{i}\) is the peak height, \(\
u_{0,i}\) is the peak position, and \(\gamma_{i}\) is the half-width at half maximum (HWHM). In addition, \(M\) and \(i\) are the number of peaks and the serial number of each peak, respectively. Therefore, the Voigt function can be expressed by Equation (4):  
\[\phi_{V}(\
u;\
u_{0,i},\gamma_{i})=A\frac{y}{\pi}\int_{-\infty}^{+\infty}\frac {exp\left(-t^{2}\right)}{y^{2}+(x-t)^{2}}dt=A\cdot Re[W(x,y)] \tag{4}\]  
For Equation (4), \(W(x,y)=\frac{i}{\pi}\int_{-\infty}^{+\infty}\frac{\exp\left(-t^{2}\right)}{x+ y-t}dt\) can be obtained. The Voigt function parameter in Equation (4) is composed of \(A=\frac{\sqrt{ln2}}{7\Omega\sqrt{\pi}}\), \(x=\sqrt{ln2}\frac{\
u-\
u_{0}}{\gamma_{D}}\), and \(y=\sqrt{ln2}\frac{\gamma_{L}}{\gamma_{D}}\). Here, \(\gamma_{D}\) and \(\gamma_{L}\) are the Doppler HWHM and Lorentzian's HWHM, respectively. As expressed by Equation (5), \(\gamma_{D}\) is a function of the temperature \(T\) and molecular weight \(M_{mole}\):  
\[\gamma_{D}=\frac{\
u_{0}}{c}\sqrt{\frac{2 kln2}{M_{mole}}}=3.5812\times 10^{-7} \
u_{0}\sqrt{\frac{T}{M_{mole}}} \tag{5}\]  
As expressed in Equation (6), \(\gamma_{L}\) can be expressed as self-collision HWHM, foreign gas collision HWHM, partial pressure of absorbed gas species, and total pressure by subdividing Lorenzian's HWHM:  
\[\gamma_{L}=\gamma_{self}P_{self}\left(\frac{T_{o}}{T}\right)^{n}+\gamma_{self }\left(P-P_{self}\right)\left(\frac{T_{o}}{T}\right)^{n} \tag{6}\]  
where \(n\) is the corresponding coefficient of temperature dependence. The ratio of \(y\) in Equation (4) is determined from the proportional relationship between the Lorentzian and Gaussian features of the lineshape. If \(y\) is lower than 1, the Voigt function can be represented as Gaussian, and if \(y\) is higher than 1, it can be represented as a Lorentzian. That is, the parameter of each individual peak is determined at the same time by minimizing the square variance between the measured absorbance and the model calculation. As a result, an appropriate curve fitting method should be applied to accurately determine the two partially overlapped H\({}_{2}\)O absorption signals based on the above equations [21].
## 1 Introduction
### Contributions  
To overcome these challenges, we propose FCM-OR, a fuzzy c-means (FCM) clustering-based opportunistic routing protocol:  
* Optimization is achieved by prioritizing nodes in the least dense area for the forwarder selection process. This is accomplished by employing FCM for local clustering within the sender's one-hop neighborhood. The forwarding rank (FR) is then determined using nodes from the least dense cluster, thereby minimizing contention and redundant transmissions. The optimization logic of our protocol is represented by this adaptive and selective forwarder identification.
* This protocol mitigates routing inefficiencies by dynamically adapting to changes in local density. This is achieved by only performing the FCM clustering process when there are more neighbor nodes than a predetermined threshold. As a result, FCM-OR can adapt its behavior according to the local density in real time, guaranteeing efficient routing in both dense and sparse areas.
* We minimize control overheads by performing localized clustering instead of relying on global network knowledge. Moreover, FCM is only performed when the local neighbor count exceeds a threshold, which keeps computational costs on par with existing schemes in sparse regions while enhancing routing efficiency in dense areas.
* This reduces the energy consumption of sensor nodes significantly, extending the network lifetime.  
[PERSON] [10] first proposed the FCM algorithm. This algorithm has been used in cluster analysis, pattern recognition, and image processing [11; 12]. This algorithm is considered a soft partition technique that assigns a degree of belongingness to a cluster to each sensor node. It has been applied to WSNs [11; 13; 14; 15] and can enhance network performance in terms of energy conservation, where data aggregation (on cluster heads) is used to minimize data forwarding (of cluster members) in the entire network. However, the clustering method was used in this study to choose potential forwarders from a less dense area to minimize the multiple receiver problem and control data forwarding between them. Thus, forwarded packets can avoid highly dense areas and redundant transmissions can be reduced.  
FCM-OR is the only opportunistic routing protocol that uses soft clustering locally for routing decision-making, as opposed to other protocols that either use fixed routing metrics or depend on global density information. This protocol integrates clustering with FR computation in a density-sensitive and energy-aware way, which is different from previous work. By integrating FCM clustering with opportunistic routing, our protocol significantly reduces redundant transmissions, enhances energy efficiency, and improves overall network performance [10; 16].
# Compact 50 W All-Solid-State Picosecond Laser System at 1 kHz
## 2 Experiment and Results
### End-Pumped Amplifier  
According to the regenerative amplifier presented above, we had obtained high-quality beam quality laser pulses. Higher pulse power required more traveling-wave amplifiers. In comparison with end-pumped traveling-wave amplifier, side-pumped traveling-wave can achieve higher single-pass gain. However, due to that the population inversion is more evenly distributed in end-pumped medium, end-pumped traveling-wave amplifier can obtain higher beam quality. The picosecond laser system used a hybrid power amplifier consisting of an end-pumped single-pass amplifier and a side-pumped double-pass amplifier. A \(3\times 3\times 40\) mm\({}^{3}\), 0.3 at % Nd-doped YAG crystal, which was wrapped with indium foil and mounted tightly on a TEC copper heat sink at 20 \({}^{\circ}\)C, was used as a gain medium in the end-pumped single-pass amplifier. A 300 W fiber-coupled quasi-CW laser diode with a 600 \(\upmu\)m core diameter centered at 808 nm (DILAS, Germany) was used as a pump source for end-pumped amplifier. The QCW laser diode produced 200 \(\upmu\)s pulses at the repetition of 1 kHz and focused on the Nd:YAG crystal via a lens system. When the laser pulses output by regenerative amplifier traversed the medium with an inverted population, the pulse power was amplified higher  
Figure 3: Beam quality of output pulses from regenerative amplifier with the output power of 6.5 W.  
In order to improve the amplification efficiency, the diameter of seed pulses should match with pump pulses in Nd:YAG crystal. L3 was a plane-convex lens used to adjust the beam size of the laser pulses. The measured power curve and beam image are shown in Figure 4. The picosecond pulses were amplified to 10.8 W after the end-pumped single-pass amplifier, corresponding to an optical efficiency of 17.7%.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 4 Background on MAC Protocols
### Evaluation Metrics  
#### 4.1.1 Normalized Offered Load (C)  
The normalized offered load (C) is the quotient between all the data injected into the network and the maximum data that could be sent at the transmission rate of the link. The latter corresponds to the product of the transmission rate and the total transmission time. The normalized offered load is calculated according to the following formula:  
\[C=\frac{\sum D_{i}}{T_{x}\cdot t_{t}}, \tag{1}\]  
where \(D_{i}\) is the data sent to the satellite by sensor \(i\), \(T_{x}\) is the link transmission rate, and \(t_{t}\) is the total transmission time.
## 6 Conclusions  
This paper describes a generalizable and reproducible method for quantitative and qualitative monitoring of land conversion by artificial sprawl. The main innovations consists of: (i) the object-based image analysis (OBIA) method used to ensure spatial-temporal reproducibility; (ii) the artificial patch concept, which aggregates all the peripheral areas that characterize artificial areas to allow the continuous generation of consistent data on artificialized areas. The method comprises three steps: (i) fine-scale mapping of artificial areas from the OBIA processing of high resolution spatial satellite images; (ii) the building of artificial patches from each map; (iii) diachronic analysis of artificial patch maps. This generates information that can be used at a fine scale (1/15,000) to enable characterization of artificial sprawl.  
The method was applied to Indian Remote Sensing (1996-1997) and RapidEye (2009) satellite image datasets on the South of France (Languedoc-Roussillon). We measured an increase in artificial areas from 113,000 ha (1997) to 133,000 ha (2009), _i.e._, an 18% increase in 12 years.  
The transferability of the method remains to be confirmed. However, the concepts developed and the use of OBIA and high-resolution images should ensure transferability. This means that we can envisage using this method both at national scale and periodically to observe how land conversion phenomena evolve over time.  
In this respect, our innovative method fits in with the French law of July 2010 on the modernization of agriculture and fishing, which aims at a 50% reduction in land conversion by 2020. The periodic use of this method would enable changes in land conversion to be monitored at a national scale and help ensure that this reduction objective is met.
# Hybrid Engineered Water-Polymer Flooding in Carbonates: A Review of Mechanisms and Case Studies
## 2 Polymer Flooding (PF)
### Challenges Associated with Polymer Flooding  
The viscosification ability of a polymer (particularly HPAM) is dramatically affected by the ionic strength and salinity of the makeup brine, as well as the formation water. This is because the polymer chains undergo severe coiling and distortion in highly saline water due to the shielding or neutralization of charges present on the backbone. As a result, the polymer solution loses viscosity. Similarly, hard water also has a deteriorating effect on the viscosity of the HPAM for the same reason, i.e., divalent ions (Ca\({}^{2+}\) and Mg\({}^{2+}\)) reduce the polymer chain expansion by consuming negative backbone charges, resulting in polymer precipitation if the Ca\({}^{2+}\) concentration is more than 200 ppm. High temperature also poses a challenge for HPAM, as it causes hydrolysis of the polymer. Many researchers have studied the effects of these critical parameters on the polymer performance [14; 44; 45; 46; 47; 48]. Some of the important challenges for PF, including challenges related to the salinity and hardness, are given below:  
* The adsorption of HPAM in carbonate reservoirs is higher than for sandstones, possibly due to the strong attraction forces between the negatively charged carboxylates on the HPAM backbone and the positively charged calcite surface (Figure 2);  
Figure 1: (**a**) The chemical structure [38] (**b**) physical structure of hydrolyzed polyacrylamide (HPAM) polymer.  
* The degree of polymer adsorption increases with increasing brine salinity (NaCl concentration), as seen in Figure 3[43];
* High-salinity and high-temperature conditions are major challenges for conventional PF due to the instability and degradation of polymers under such conditions [49];
* Extraction and recycling of polymer from the production stream is a major operational challenge, which can substantially increase the project cost [50; 51; 52].  
Scientists and researchers have put forth a great deal of effort and the PF technique has greatly improved over the years. Despite these advancements, there are several limitations that need to be carefully evaluated in order to make this method an economically viable solution for carbonates. The applicability of polymer flooding in carbonates is primarily limited by the formation water salinity, makeup water salinity, hardness, formation temperature, and high chemical degradation of polymers [53; 54; 21]. These constraints need to be addressed to accomplish oil recovery objectives, hence leading us to the idea of a novel hybrid EOR method that includes both PF and EWF.
## 2 Materials and Methods
### Data Reduction and Analysis  
Raw data were processed using Nexus software. After reconstruction and labeling/relabeling, gaps in the trajectories were filled using a combination of the following Nexus editing routines: spline, pattern fill, and rigid-body fill. Trajectory and raw ground reaction force data were filtered using Nexus pipelines. Additional Nexus pipelines were also implemented to create events to define the squat cycle. Based on an observation, the initiation of the squat was marked when the posterior superior iliac spine (PSIS) marker on the pelvis began to move, the endpoint of the squat was marked at the most inferior position of the PSIS marker, and return-to-neutral was marked when the PSIS marker reached its most superior position. Thus, the initiation to the endpoint defined the descent phase (0-50%), and the endpoint to return defined the ascent phase of the squat (51-100%).  
Three representative squat trials from each participant were exported to Visual3D (C-Motion, Germantown, MD, USA) for further dynamic processing and statistical descriptive analysis, i.e., determination of mean and standard deviation kinematic and kinetic curves. Joint angles were calculated using a Cardan rotation sequence, i.e., x-y-z. The biomechanical model created in Visual3D used the Harrington method for estimating hip joint center location [28; 30; 33]. Pelvic and trunk segmental motion was determined using the Euler sequence z-y-x [34]. Oxford foot model multi-segment foot angles were determined using the following rotation sequences: tibia/forefoot x-y-z; tibia/hindfoot and  
Figure 1: Example data collection set up for 3-D motion capture of the OHDS. (**A**) Beginning position, sagittal view; (**B**) beginning position frontal view; (**C**) 50% (full squat position) sagittal view; (**D**) 50% (full squat position) frontal view. The two blue arrows projecting upward represent the vertical ground reaction forces. The three arrows on the corner of the force plates represent the plate coordinate system: x (red), y (green), and z (blue).  
hindfoot/forefoot x-z-y; and forefoot/hallux x-y-z [27]. Visual 3D was also used to determine GRFs, and net internal hip, knee, and ankle joint moments (Nm.kg), and sagittal plane powers (W/kg), normalized to body mass. Normal mean (\(\pm\) standard deviation) kinematic and kinetic data figures were created in the most current Visual 3D (Vx64) software and graphed as the percent squat from 0 to 100%. This is consistent with time-normalized reporting of the OHDS used by [PERSON] et al. where graphic presentation is reported in a percentage from 0 to 100% [15].
# An Improved Multi-Sensor Fusion Navigation Algorithm Based on the Factor Graph
## 3 System Model for the Navigation System
### State Model of the System  
The state variable of the micro unmanned aerial vehicle system can be chosen as follows.  
\[\mathbf{X}(t)=\left[\begin{array}{cccc}\mathbf{p}^{T}&\mathbf{v}^{T}&\mathbf{q}^{T}&\
abla_{ a}^{T}&\
abla_{g}^{T}\end{array}\right] \tag{1}\]  
where \(\mathbf{p}=\left[\begin{array}{cccc}x&y&z\end{array}\right]^{T}\) represents the relative position vector to the desired hover position navigation position, \(\mathbf{v}=\left[\begin{array}{cccc}v_{x}&v_{y}&v_{z}\end{array}\right]^{T}\) represents the velocity vector, \(\mathbf{q}=\left[\begin{array}{cccc}q_{0}&q_{1}&q_{2}&q_{3}\end{array}\right]^{T}\) represents the attitude quaternion, \(\
abla_{a}=\left[\begin{array}{cccc}\
abla_{a_{x}}&\
abla_{a_{y}}&\
abla_{a_ {z}}\end{array}\right]^{T}\) is the acceleration random walk term, and \(\
abla_{g}=\left[\begin{array}{cccc}\
abla_{gx}&\
abla_{gy}&\
abla_{gz} \end{array}\right]^{T}\) is the gyro random walk term.  
Figure 1: System hardware structure of the MUAV.  
The continuous time kinematic navigation equations are given by:  
\[\begin{array}{l}\dot{\mathbf{p}}=\mathbf{v}\\ \dot{\mathbf{v}}=\mathbf{C}_{b}^{n}\cdot\left(\dot{\mathbf{f}}^{b}-\
abla_{a}-\mathbf{\omega} _{a}\right)+\left[\begin{array}{ccc}0&0&1\\ \end{array}\right]^{T}\!\!g\\ \dot{\mathbf{q}}=\frac{1}{2}\mathbf{q}\otimes\left(\widetilde{\mathbf{w}}_{ib}^{b}- \
abla_{g}-\mathbf{\omega}_{g}\right)\\ \
abla_{a}=\mathbf{\omega}_{b_{a}}\\ \dot{\
abla}_{g}=\mathbf{\omega}_{b_{g}}\end{array} \tag{2}\]  
where, \(\left[\begin{array}{ccc}\mathbf{\omega}_{p}^{T}&\mathbf{\omega}_{v}^{T}&\mathbf{\omega}_{ b_{a}}^{T}&\mathbf{\omega}_{b_{g}}^{T}&\mathbf{\omega}_{a}^{T}&\mathbf{\omega}_{g}^{T} \end{array}\right]\) are the gaussian white noises.
# Estimating Contact Force Chains Using Artificial Neural Network
## 3 Results
### Parametric Study  
A parametric study was conducted to investigate the effects of input particle-scale features and data size on the performance of the constructed ANN model. Table 2 summarizes the input parameters as well as the results of the tested model. The standard model, whose results have been presented in Section 3.1, is denoted as ML00. The symbol '/' indicates that the parameters used are the same as those of the standard model. We used the median value (\(R_{\text{m}}\)), standard deviation (\(R_{\text{s}}\)), maximum (\(R_{\text{b}}\)) and minimum (\(R_{\text{w}}\)) of the \(R\) values to characterize the distribution of \(R\) for all the eleven tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test ID** & **Feature** & **Bins** & \
\begin{tabular}{c} **Training** \\ **Data Size** \\ \end{tabular} & **Rb** & **Rw** & **Rm** & **Rs** \\ \hline ML00 & All features & 180 & 40,000 & 0.967 & 0.556 & 0.827 & 0.090 \\ ML01 & Coordination number & & 0.962 & 0.345 & 0.749 & 0.128 \\ ML02 & _x_-velocity & & 0.892 & 0.119 & 0.556 & 0.157 \\ ML03 & _y_-velocity & / & / & 0.932 & 0.064 & 0.626 & 0.170 \\ ML04 & Spin & & 0.733 & 0.099 & 0.316 & 0.172 \\ ML05 & Particle size & & 0.963 & 0.347 & 0.768 & 0.121 \\ ML06 &
\begin{tabular}{c} Particle size and \\ coordination number \\ \end{tabular} & & 0.959 & 0.392 & 0.773 & 0.115 \\ ML07 & & 40,000 & 0.967 & 0.556 & 0.827 & 0.090 \\ ML08 & & 20,000 & 0.953 & 0.491 & 0.790 & 0.101 \\ ML09 & / & / & 10,000 & 0.955 & 0.487 & 0.786 & 0.102 \\ ML10 & & 5000 & 0.953 & 0.48
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 3 Experimental Settings
### Configurations of CNAB  
In this study, the 6 s simulated speech signal with a sampling frequency of 16 kHz was divided into 1 s segments, each consisting of 16,000 samples. The Hilbert transform was applied to each input speech segment to extract the real and imaginary parts, which were used as inputs to the complex shared-LSTM. Since both real information and imaginary information were in the time domain, LSTM was used to extract features directly, as it is better suited for modeling temporal information. Table 1 summarizes the parameters of the CNAB model.  
According to Table 1, the input segment dimension was (B, 2, 16,000), with B representing the batch size, and the input had real and imaginary information, with the middle dimension being set to 2. Each segment consisted of 16,000 sampling points, which were divided into 100 temporal sequences, with each sequence having a duration of 10 ms, creating 160 sampling points. The input format of the complex shared-LSTM was \(batch\times 2\timestimesteps\timesinputsize\). A complex shared-LSTM layer was composed of two 512-cell LSTM layers, denoted by \(LSTM_{r}\) and \(LSTM_{i}\). The last step of the timesteps was used as input to the two complex split-LSTM layers. Finally, the complex linear activation layer was employed to estimate the beamforming filter coefficients of 25 sampling points.
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 4 Experiments
### Results and Analysis

Table 5: Comparisons of the proposed MBQDN with existing methods [6; 7; 11] on the zero-waste dataset. and TL-Branch and RP-Branch can accurately extract discriminative features of resource pool state and task state. In addition, the results also highlight an effective application of reinforcement learning to the satellite resource allocation problem.
## 4 Use Case and Simulation of the Proposed System
### PMIPv6/AAND  
Handover Comparative Analysis  
The PMIPv6/AAND handover delay is judged against MIPv6, PMIPv6 and F-PMIPv6. The comparative outcomes are given in Figure 18. The handover latency for MIPv6 is higher since it is a protocol based on the host-based criteria. Therefore, MIPv6 transfers a very small number of packets in the concerned time. Moreover, it needs MN contribution through mobility. The F-MIPv6 handover latency is less than the PMIPv6 as it creates a new link for every transfer. However, the PMIPv6 does not make a new fast transfer link like F-PMIPv6. Although the F-PMIPv6 and PMIPv6 are IPv6 mobile proxy protocols, the transfer latencies are higher than the proposed technique, i.e., PMIPv6/AAND, because the F-PMIPv6 and PMIPv6 messaging format does not use auxiliary parameters to view the virtual interface information of the concerned MN. Based on various topology mechanisms present in the proposed system, the handover delay of PMIPv6/AAND is less than the PMIPv6/SAND. For of this reason, PMIPv6/AAND transfers a greater number of packet sequences than the existing protocols. Thus, PMIPv6/AAND provides minimum transfer latency compared to other mobility protocols.  
Figure 17: PMIPv6/AAND—Handover Simulation Result.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Protocol** & **Handover Delay** \\ \hline MIPv6 & \((\text{TD}_{\text{Auth}}+\text{TD}_{\text{RRS}})+(\text{TD}_{\text{RS}}+\text{ TD}_{\text{RV}})+(\text{TD}_{\text{NAR}})+(\text{TD}_{\text{BUH}}+\text{TD}_{\text{ HBA}})+(\text{TD}_{\text{REG}}+\text{TD}_{\text{BUC}}+\text{TD}_{\text{CBA}})+\text{TTD}_{ \text{Data}}\) \\ \hline PMIPv6 & Layer 2 connection + \((\text{t}_{\text{PBA}}+\text{t}_{\text{BUH}})+\text{t}_{\text{RS}}+\text{t}_{ \text{RA}}+(\text{t}_{\text{AAAreq}}+\text{t}_{\text{AAres}})+\text{TTD}_{ \text{Data}}\) \\ \hline F-PMIPv6 & Layer 2 connection + \((\text{t}_{\text{AAAreq}}+\text{t}_{\text{AAres}})+2(\text{t}_{\text{PSA}}+ \text{t}_{\text{BUH}})+2(\text{t}_{\text{RS}}+\text{t}_{\text{RA}})+\text{TTD}_ {\text{Data}}\) \\ \hline PMIPv6/SAND & Layer 2 connection + \(2(\text{t}_{\text{PRA}}+\text{t}_{\text{BUH}})+2\)\((\text{t}_{\text{RS}}+\text{t}_{\text{RA}})+\text{TTD}_{\text{Data}}\) \\ \hline PMIPv6/AAND & Layer 2 connection + MAX \((\text{t}_{\text{RS}}\),\(\text{t}_{\text{RA}})+\text{TTD}_{\text{Data}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: List of various IPv6 mobility protocols.