esa-sceva/satcom-chunk-collection huggingface dataset:
# Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories
## 2 Materials and Methods  
### Inverse Modeling System--NTFVAR  
#### 2.1.1 The Transport Model  
We used a global Eulerian-Lagrangian coupled model NIES-TM-FLEXPART-VAR (NTFVAR) that consists of the National Institute for Environmental Studies (NIES) model as a Eulerian three-dimensional transport model (TM), and FLEXPART (FLEXible PARTicle dispersion model) as the Lagrangian Particle Dispersion Model (LPDM). The forward transport model and model development were described by [PERSON] et al. [36] and [PERSON] et al. [30], and the application to inverse modeling using the Kalman smoother at moderate resolution of \(1^{\circ}\times 1^{\circ}\) was demonstrated by [PERSON] et al. [37], [PERSON] et al. [38], and [PERSON] et al. [29]. Our transport model is a further modification of the model described by [PERSON] et al. [30]. The coupled model combines NationalInstitute for Environmental Studies Transport Model (NIES-TM) v08.1i with a horizontal resolution of 2.5\({}^{\circ}\) and 32 hybrid-isentropic vertical levels [39] and Flexpart model v.8.0 [40], and runs in backward mode with a surface flux resolution of 0.1\({}^{\circ}\). The changes in the current version with respect to the study by [PERSON] et al. [30] include the derivation of adjoint components using the adjoint code compiler Tapenade [41] instead of the TAF (Transformation of Algorithms in Fortran) compiler [42]. Additionally, the transport matrix indexing and sorting algorithms were revised to allow efficient memory usage for handling large matrixes of Lagrangian responses to surface fluxes required for the application of the model to satellite data. A manually- derived adjoint of the NIES TM v08.1i was used, as in [PERSON] et al. [30], due to its computational efficiency. The second-order approximation Van Leer algorithm [43] was implemented in the adjoint and forward models, alternatively to the third order algorithm typically used in forward models [39]. The meteorological data used for the transport model were obtained from the Japanese Meteorological Agency (JMA) Climate Data Assimilation System (JCDAS) [44; 45], which provided the required parameters, such as three-dimensional wind fields, temperature, and humidity at 1.25\({}^{\circ}\times 1.25^{\circ}\) spatial resolution, 40 vertical hybrid sigma-pressure levels, and a temporal resolution of 6 h.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 2 Previous Works  
### GNNs  
Recently, there is an increasing interest in graph data [6; 7]. Their applications include human action recognition, chemistry molecules [8], citation network [9], agriculture [10], production planning [11], etc. Graph data are non-Euclidean [12; 13] and thus they do not satisfy the basic assumption that instances are independent. On the contrary, each node is related to others through various types of links. This dependency contributes to the intrinsic information. Moreover, transforming data to graphs can protect personal privacy. For example, human skeleton graphs remove human faces and surrounding environments in the captured videos, preventing the observed subjects from being identified.  
Motivated by the requirement of mining graph data, GNNs were first introduced by [PERSON] et al. [14] in 2005. Afterwards, inspired by convolutional neural networks (CNNs) and recurrent neural networks (RNNs), convolutional GNNs (ConvGNNs) and recurrent GNNs (RecGNNs) were developed gradually. ConvGNNs include spectral and spatial ConvGNNs. Spectral ConvGNNs adopt graph kernels to process input data in spectral space, while spatial ConvGNNs perform convolution on the \(k\)-order topological neighbors. Apart from them, alternatives such as graph autoencoders (GAEs) and spatio-temporal graph neural networks (STGNNs) have also emerged.  
Considering the success of GNNs, if human skeletons are built as graphs, then any GNN proposal can be a possible candidate for skeleton graph-based HAR. GNNs used for HAR are classified as spatial-based approaches, spatio-temporal-based approaches and generated approaches [15]. This work is based on ST-GCN, a typical example of the spatio-temporal approach, which is very powerful for HAR.  
Figure 1: The algorithm ST-GCN [1]. The blue dots represent human joints. The human skeleton at each frame is regarded as a graph.
# Nearly Linear-Phase 2-D Recursive Digital Filters Design Using Balanced Realization Model Reduction
## 5 Illustrative Examples and Numerical Evaluation
### 2-D Bandpass Filter  
In this example, we design a 2-D bandpass filter to satisfy the following specifications:  
\[\mid H(\omega_{1},\omega_{2})\mid=\left\{\begin{array}{ll}1&\omega_{p1}^{2} \leq{\omega_{1}}^{2}+{\omega_{2}}^{2}\leq\omega_{p2}^{2}\\ 0&\text{otherwise}\end{array}\right. \tag{18}\]  
Figure 2: Magnitude responses and group delays of 2-D lowpass FIR and IIR filters discussed in Example 1 of Section 5.1: (**a**) 2-D FIR lowpass filter of order (24,24); (**b**) 2-D IIR of reduced order (13,13) in [(6)]; (**c**) 2-D IIR of reduced order (13,13) using LMI; (**d**) magnitude contour of reduced-order filter; (**e**) group delay \(\tau_{1}\) of reduced order (13,13); (**f**) group delay \(\tau_{2}\) of reduced order (13,13).  
where \(\omega_{p1}=0.2\pi\) and \(\omega_{p2}=0.5\pi\). First, a linear-phase 2-D FIR filter of order (24,24) is designed using a window method to satisfy the design specifications. Then, a 2-D IIR bandpass filter of a reduced order (13,13) is obtained by using the proposed method. In this example, the computation time for controllability Gramian \(P^{s}\) and observability Gramian \(Q^{s}\) was 6.88 s. The number of coefficients for the FIR filter of order (24,24) is \((N_{1}+1)\times(N_{2}+1)=625\), while the obtained IIR filter of a reduced order of (13,13) has a reduced number of coefficients equal to 224. The magnitude response of the full-order (24,24) 2-D FIR bandpass filter is shown in Figure 2(a). The reduced-order (13,13) filter is stable, and its magnitude response is shown in Figure 2(b). The group delays \(\tau_{1}\) and \(\tau_{2}\) over the passband are illustrated in Figure 2(c,d).
## 10 Conclusions and Future Work  
In conclusion, this survey paper emphasizes the importance of non-terrestrial networks in the Internet of Remote Things ecosystem. These networks overcome the limitations of traditional terrestrial networks, enabling reliable connectivity in geographically isolated areas and challenging environments. The paper identifies seven key objectives for NTN research: energy efficiency, cost-effective deployment/operation, enhancement of coverage and availability, enhancement of throughput/transmission rate, enhancement of data transmission reliability, enhanced and timely data acquisition, and enhancement of edge computing capabilities. By addressing these objectives, NTNs for IoRT have the potential to revolutionize the way data are collected, processed, and utilized, enabling a wide range of applications in remote and isolated environments.  
However, to fully unlock this potential, further research is necessary. Building upon the findings of this survey, our future work will focus on two key areas:  
* Investigating the impact of distance, network resources, and aerial platform types on the feasibility and performance of remote IoT connectivity via NTNs. This will involve developing models to predict network performance and identify optimal configurations for different deployment scenarios.
* Exploring energy-efficient communication protocols and developing power-aware routing algorithms that leverage the unique characteristics of NTNs to minimize energy consumption. This may involve integrating renewable energy sources into remote IoT devices and quantifying the environmental impact of different IoRT deployment scenarios.  
By addressing these research areas, we aim to contribute to the development of a sustainable and efficient IoRT ecosystem, unlocking the full potential of NTNs.  
**Author Contributions:** Investigation, S.P., D.T. and D.N.S.; Writing--original draft, S.P., D.T. and D.N.S.; Review & editing, D.N.S., A.R., G.K. and C.S.; Supervision, D.N.S., A.R., G.K. and C.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Single-Factor Influence Analysis of the Segments' Horizontal Displacements Based on the Model  
### The Effect of the Jack Total Thrust  
The total jack thrust is set as N = 6000 kN, 7000 kN, 8000 kN, 9000 kN, and 10,000 kN, respectively, during the calculation in the model while other parameters are kept the same. The parameters in the theoretical model are summarized in Table 2. Then, the evolution law of the total thrust on the lateral accumulated displacements of a single ring is analyzed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Total Thrust** & **Lateral Force** & **Deviation** & \multicolumn{2}{c}{**Monitoring Data**} \\ \cline{3-6} \multicolumn{1}{c}{**F/(kN)**} & **Couple \(M\)/(kN-m)** & **Angle \(a\)/(C)** & **N\({}_{0}\)/(kN)** & **M\({}_{0}\)/(kN-m)** & **Q\({}_{0}\)/(kN)** \\ \hline
5931.47 & \(-\)1708.20 & \(-\)0.390 & 5931.33 & \(-\)1708.20 & \(-\)40.36 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial parameters for calculation model.  
Figure 8: Comparison of horizontal accumulated displacements of the segments.  
Figure 9 shows that the lateral accumulated displacement increases with the increasing distance between the ring and the shield tail. With the increase of the leaving distance (completed rings), the lateral accumulated displacements tend to be stable. The segment results in the deformation along the axis under the jack thrust. When the outside of the segment contacts with the surrounding medium, the friction is produced to resist the jack thrust. When the frictional resistance of the first ring inside the shield tail cannot resist the jack thrust, the remaining thrust can be delivered to the next rings. The transverse component of the remaining thrust can cause a horizontal axis deviation. Therefore, with the increase of the total jack thrust, the remaining thrust acting on the segments increase, and the horizontal axis deviation also increases.  
Based on the stable criteria that the displacement difference of the adjacent ring is no more than 0.1 mm, the stable ring number that was leaving the tail increase from the 12 th to the 17 th when the total thrust increases from 6000 kN to 10,000 kN. In the stable state, the minimum lateral accumulated displacement is \(-\)15.347 mm, while the maximum lateral accumulated displacement is \(-\)28.321 mm, increasing by 84.54%.
# Design Methodology and Application Dynamics of Compact Quasi-Zero Stiffness Isolators
## 3 Compact Design of QZS Isolation
### Other Type
#### 3.6.5 Air Spring Structure  
Air springs can realize strong output force in a small space and can adjust the air pressure inside the airbag to change the stiffness. [PERSON] et al. [98] developed an unrestrained air spring configuration by eliminating conventional guide rods and constraints, proposing a hybrid vibration isolation system comprising orthogonally arranged vertical and transverse air spring assemblies. Through parametric optimization of the system geometry and pressure distribution, the transverse air springs were engineered to generate precisely tuned negative stiffness, thereby synergistically counterbalancing the positive stiffness of the vertical components to achieve quasi-zero stiffness (QZS) operational characteristics.  
Figure 17: Schematic diagram of the energy harvester [94].  
Figure 18: Compliant quasi-zero-stiffness [95]: 1—a folded beam, 2—a curved beam, 3—the outer connecting ring, 4—the inner connecting disc, 5—a bolt hole, 6—a through hole for mounting the limit shaft, and 7—a groove.  
The analytical framework for this coupled stiffness interaction is illustrated in Figure 19. [PERSON] et al. [99] used sleeve air springs to improve the load-carrying capacity based on the triple-spring structure, and the overall system was simple and compact, which could effectively isolate the unpredictable roadway low-frequency interference.  
In conclusion, existing research has explored a wide range of engineering applications under space-constrained conditions, mainly involving the introduction of elastic elements with high positive and negative stiffness, embedded structural design, and the development of new compact materials. However, most of the studies tend to focus on compactness, while other important characteristics such as load-carrying capacity, large amplitude vibration isolation, broadband vibration isolation, and adaptive capability are often not taken into account. Future research should more systematically consider the synergistic optimization of multiple characteristics to achieve comprehensive performance improvement and design optimization.
# Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks
## 5 Limitations and Potential Solutions  
Conducting a simulation study to improve QoS in large-scale WSNs using multiple mobile sinks and random mobility models is a valuable approach. However, like any research methodology, it has limitations. The Table 3 below resumes the approach and the potential solutions or areas for future research:  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Criteria** & **Limitation** & **Potential Solutions/Future Research** \\ \hline \multirow{8}{*}{Random Mobility} & The memoryless Random mobility models used in simulations may not accurately represent real-world scenarios where sink mobility can be influenced by various factors such as environmental obstacles (Trees and mountains) or climatic perturbations (wind, rain, and snow)... & Investigate the use of mobility traces collected from real-world deployments to create more accurate random mobility models. \\
Based on the literature, the present study focused on the interference effect on the wind loads and dynamic response among quayside cranes. High-frequency force balance tests (HFFB) were carried out in the wind tunnel to obtain the aerodynamic characteristic of the whole structure. The tests considered different wind yaw angles, structure states, and spacing to analyze the influence of IE on wind loads. Meanwhile, computational fluid dynamics (CFD) was performed to analyze the wind fields in detail, and the drag coefficients of each member were obtained as well. Based on these results, a randomly generated wind time history was used to model the wind loads considering the interference effect using the weighted amplitude wave superposition (WAWS) method. Numerical models for the quayside cranes were built in the commercial software ANSYS and the transient analysis was conducted using the finite element method (FEM). Then, the interference effect on wind-induced response was discussed. Finally, some brief conclusions were drawn and potential improvements for risk reduction for quayside cranes under strong wind were proposed.
# Residual Echo Suppression Considering Harmonic Distortion and Temporal Correlation
## 5 Experiments
### Experiments with Simulated Data  
Firstly, the performances of the RES algorithms were evaluated with the simulated data. A total of 20 utterances spoken by 10 male and 10 female speakers were selected from the TIMIT database [31] as the far-end speech, while another 20 utterances from the same database were used as the near-end speech for the double-talk scenario. Background noises were Babble, White, and Factory noises from the NOISEX-92 database [32]. The sampling rates were 16 kHz. The ENR for the single-talk periods was set to be 10, 15, and 20 dB for each type of noise in addition to the noise-free condition. Therefore, the total number of data for the far-end single-talk scenario was \(20\times(3\times 3+1)=200\). As for the double-talk data, 20 pairs of utterances were used for near-end and far-end speech signals mixed at the SER of \(-5\), 0, and 5 dB. The SNR was set to be 10, 15, and 20 dB for each noise type in addition to the clean noiseless condition, making the total number of data \(20\times 3\times(3\times 3+1)=600\). To simulate the nonlinearity that arise from power amplifiers and loudspeakers, we adopted a clipping function [33] and a memoryless sigmoidal function [34]. The hard clipping function [33] is defined as:  
\[x_{hard}(t)= \begin{cases}-x_{max},&x(t)<-x_{max}\\ x(t),&|x(t)|\leq x_{max}\\ x_{max},&x(t)>x_{max}\end{cases} , \tag{30}\]  
where \(x_{max}\) was set to be 80% of the maximum volume of \(x(t)\). To model the nonlinear characteristics of the loudspeakers, the memoryless sigmoidal function [34] was used:  
\[x_{NL}(t)=\gamma\cdot\left(\frac{2}{1+\exp(-a\cdot b(t))}-1\right), \tag{31}\]  
where  
\[b(n)=1.5\times x(t)-0.3\times x(t)^{2}, \tag{32}\]  
the sigmoid gain parameter \(\gamma\) is set to 2, and the sigmoid slope is set to be \(a=4\) if \(b(t)>0\) and \(a=0.5\) otherwise. The echo path after the nonlinearity was modeled with the image method [35] which simulates a \(4\times 4\times 3\) m small office with the reverberation time of \(T_{60}=200\) ms. Tables 1 and 2 show the average ERLEs in the far-end single-talk periods and average PESQ scores in the double-talk periods for the simulated data. In all noise types and echo-to-noise ratios, the HDRES [28] improved the ERLE of the linear AEC while keeping the PESQ scores the same, and the proposed RES outperformed the HDRES in both the ERLE and PESQ scores. The PESQ scores for the PFRES was similar to those for HDRES, while the ERLE for the PFRES was in the middle of that for the HDRES and that for the proposed RES. On average, the ERLE was improved by 5.37 dB compared with the linear AEC output, by 0.74 dB compared with the PFRES, and by 4.43 dB compared with the HDRES, while the PESQ scores was improved by 0.064 compared with the linear AEC output, by 0.042 compared with the PFRES, and by 0.051 compared with the HDRES. We can conclude that exploiting the temporal correlation of the relevant signals in addition to the harmonic relation between frequency bins was effective to estimate the residual echo.
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 3 IANT Scheme Design  
In this section we introduced the IANT scheme in detail, which selects the best channel quality tags and noise generation tags to improve the system secrecy rate. The design of the scheme is shown in Figure 2, where each time slot is divided into three sub-slots.
# Is It Possible to Restore a Heavily Polluted, Shallow, Urban Lake?
## Material and Methods
### Bottom Sediment and Interstitial Water Sample Analysis  
The output, after being transported to the laboratory, was weighed to determine the volumes and densities of the fresh samples. The samples were then dried to constant weight, and the volume and density were redetermined. The interstitial water was obtained from the sediment by centrifugation at 3000 rpm for 20 min according to [PERSON] and [PERSON] (2018).  
In the interstitial water samples, ammonia was examined by ionic chromatography using an ICS-5000 DIONEX DC, TKN by IL 550 TOC-TN analyzer, Hach Inc. (Larimer Country, CO, USA), phosphates and total P were measured by a Nano color spectrophotometer, and organic P was calculated as the difference between the TP and phosphate amounts. In the investigated sediments, organic matter was measured by LOI (the percent weight loss during the ignition) using weight analysis ([PERSON], 2018), total nitrogen was measured by the Kjeldahl method and silica was measured using weight analysis after preliminary digestion of the sediment sample in a mixture of strong mineral acids (H\({}_{2}\)SO\({}_{4}\), HClO\({}_{4}\) and HNO\({}_{3}\) 1:2:3) and filtering through a No. 390 filter. The phosphorus fractions were analyzed by sequential extraction according to the Rydin and Welch method ([PERSON] and [PERSON], 1981) modified by [PERSON] et al. (2018). In the analyzed sediments, samples were examined for the following:  
* Labile phosphorus (NH\({}_{4}\)Cl-P; after 2 \(\times\) 2 h extraction in 1 M NH\({}_{4}\)Cl),
* Phosphorus sensitive for redox potential changes (BD-P; after 2 h extraction in 0.11 M buffered dithionite solution),
* Phosphorus bound with aluminum and organic matter (NaOH-TP; after 16 h extraction in 0.1 M NaOH and mineralization of extract using the 10 N mixture of H\({}_{2}\)SO\({}_{4}\) and HClO\({}_{4}\)),
* Phosphorus bound with aluminum and iron oxides and hydroxides (NaOH-rP; directly in the NaOH extract),  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Point Number** & **Latitude** & **Longitude** \\ \hline
1 & N:54\({}^{\circ}\)33\({}^{\prime}\)27.5\({}^{\prime\prime}\) & E: 18\({}^{\circ}\)19\({}^{\prime}\)61.4\({}^{\prime\prime}\) \\
2 & N:54\({}^{\circ}\)33\({}^{\prime}\)19.0\({}^{\prime\prime}\) & E:18  
* Phosphorus bound with organic matter (NaOH-nrP; was calculated as the difference between NaOH-TP and NaOH-rP),
* Calcium bound phosphorus (HCl-P; after 16 h extraction in 0.5 M HCl),
* Residual phosphorus (res-P; was calculated as the difference between TP after mineralization using a mixture of H\({}_{2}\)SO\({}_{4}\) and HClO\({}_{4}\) and the sum of the fractions of NH\({}_{4}\)Cl-P, BD-P, NaOH-TP and HCl-P).  
Extracted phosphorus was determined using the molybdate blue method according to [PERSON] et al. [34]. The results were statistically analyzed (basic statistics--mean values, standard deviation) using a Statistica 13.0 software package [35].