esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Future Work
### Application and Error Analysis of High-frequency CMLs  
With fifth-generation (5G) and even sixth-generation (6G) wireless systems in operation, huge data throughput is forcing operators to design E-band CMLs at 71 to 86 GHz and replace older equipment. This may lead to new opportunities because these high-frequency CMLs are more sensitive to light rain and water vapor [47; 198; 199]. However, the increase in frequency also leads to nonlinearities in the \(\gamma\)-\(R\) relationship and increased dependence on DSD. In addition, high-frequency CMLs also suffer from more energy loss and, therefore, the path length is correspondingly shorter, which may lead to a more severe negative impact of WAA [186]. An error analysis of high-frequency CMLs could contribute to their future application in environmental monitoring.
## 6 Simulation Results
### Performance Comparison
According to the simulation results in Figures 9 and 10, it can be found that the proposed framework of the MDRL-UL is more stable and has better performance than the DDPG-UL and the DQN-DDPG, which proves the effectiveness of the framework in the time-varying dynamic environment. In order to test the advantages of multi-agent reinforcement learning in NOMA systems, a single-agent baseline method is implemented to compare with the MDRL-UL method in terms of system performance, as shown in Figure 11. It can be seen that the energy efficiency of the proposed MDRL-UL framework outperforms the single-agent baseline method for all channel numbers, and the gap between the two increases as the channel number increases. In Figure 11b, it can be found that the NOMA system under  
Figure 8: The sum rate obtained by different algorithms as the maximum transmission power of the base station varies from 2 W to 12 W.  
multi-agent has a higher energy efficiency and is more stable. In addition, because the single-agent baseline method also needs to collect global channel gain information, its state space and action space will grow exponentially with the number of channels and users, thus increasing the overhead of resources. In the face of high-dimensional time-varying environments, the single agent requires a huge number of neural network parameters to capture deep information, which may cause an excessive or even unbearable load. For the multi-agent with complexity \(O\big{(}T_{step}\cdot(N+1)\big{)}\cdot O\big{(}\sum_{i\in[1,L-1]}V_{i}V_{i+1} \big{)}\), it can reduce the number of parameters of each agent by cooperating with each other, thus reducing the computational load while maintaining the overall stability. In addition, to verify the impact of unsupervised learning on the system performance, we plot the power of the user clusters corresponding to the subchannels in each cell and the system performance under each time slot, as shown in Figure 12. We can find that the power of each  
Figure 10. The sum rate of different algorithms in a time-varying dynamic environment. Figure 9. The energy efficiency of different algorithms in a time-varying dynamic environment. subchannel is maintained at a low level over time. This is because, according to (4), (5), and (6), as the power increases, each user cluster is similarly subject to higher external interference. Therefore, when the channel conditions are better, the unsupervised learning neural network allocates a lower power to each subchannel to reduce the external interference suffered by each user cluster, thus improving the system performance. Under the poor channel conditions, it can be found that the subchannel powers can adjust each other to ensure the overall stability of the system and maintain the system performance at a high level.
## 2 Materials and Methods
### Electroless Plating of MWCNT
#### 2.1.3 Transient Liquid Phase Joining  
Copper substrates (20 mm \(\times\) 10 mm \(\times\) 1 mm) were polished and cleaned in 5% HNO\({}_{3}\) to remove any surface impurities. The Sn coated Cu-MWCNT composite powders were mixed with 10 wt% Rosin mildly activated (RMA) type sparkle flux (Senju Metal Industry. Co. Ltd) to form a paste. The paste was printed using 150 \(\upmu\)m thick stainless-steel stencil and sandwiched between top and bottom copper substrates. The samples were joined in air at 260 \({}^{\circ}\)C for 3, 5, and 8 min at 10 MPa applied pressure. After joining, the samples were cut cross-sectional, ground using 800, 1200, and 2400, grit SiC papers, and polished with diamond paste. The microstructure of the joints was analyzed using SEM and EDS. Shear test samples were fabricated by stencil printing of TLP paste with a thickness of 150 \(\upmu\)m on copper substrate with a dimension of 20 mm \(\times\) 10 mm \(\times\) 1 mm. Copper die with dimensions of 3 mm \(\times\) 3 mm \(\times\) 2 mm were placed on the top of the stencil printed TLP paste and joined with an effective bonding area of 9 mm\({}^{2}\). The strength of the joint was tested using shear tester (Rhesca) at the rate of 1 mm/min at room temperature.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 6 Experimental Results
### Indoor Environment  
Table 3 shows the values obtained from the measurements made indoors. Observing the reported RSSI values, it can be observed that these reflect quite well the ideal trend reported in Figure 5, and it is possible to appreciate even more the similarity between the real and the ideal decreasing exponential trend in Figure 12.  
As the distance between the devices increases, the RSSI increases negatively, meaning that the signal strength reaching the receiver tends to decrease as the devices move further  
Figure 12: Real RSSI trend graph in _indoor_ environment.  
\begin{table}
\begin{tabular}{c c c} \hline \(d\) **(m)** & **RSSI (dBm)** & \(\sigma\) \\ \hline
0 & \(-1\) & 2.132 \\
1 & \(-49\) & 0.748 \\
2 & \(-54\) & 0.787 \\
3 & \(-58\) & 0.810 \\
4 & \(-60\) & 0.911 \\
5 & \(-62\) & 2.328 \\
6 & \(-69\) & 2.484 \\
7 & \(-73\) & 3.801 \\ \hline \end{tabular}
\end{table}
Table 3: RSSI in indoor environment.  
apart, as is normal. It is also good to note that the standard deviation increases when the distance increases, in agreement with the fact that the measurements taken will be less accurate as the distance increases. It was expected that the data obtained were not perfectly equal to the ideal ones, precisely because the signal of radio communication is influenced by the environment in which it propagates and by the effects mentioned earlier, such as shadowing, multipath, etc.
# Global Buckling Resistance of Cold-Formed Steel Beams with Omega-Shaped Sections
## 4 Numerical Models
### CUFSM Model  
The CFS omega-shaped profiles were also numerically simulated using the software CUFSM v5.05 to determine the critical elastic buckling moments and possible omega section buckling modes. All the sections (70 \(\times\) 1.5, 170 \(\times\) 2.0, and 300 \(\times\) 2.5) were analyzed for both upward and downward loading cases, but only the simulations performed in the 170 \(\times\) 2.0 section are described.  
In Figure 18, the geometric model of this section is presented, showing the stresses applied at the profile's critical section (in kN.m) for downward and upward loading, respectively. These stresses were applied assuming that the profile's upper flange reached the yield stress of the steel (\(f_{y}\) = 330.0 MPa). The lines in blue represent compressive stresses, while the lines in red represent tensile stresses along the omega section.  
To perform elastic buckling analysis in CUFSM, several assumptions were made: the signature curve method was chosen as the solution approach, and the beam support configurations were defined as simple-simple (S-S) to replicate the experimental setup's condition of a simply supported beam. Additionally, the analysis covered half-wavelengths ranging from 1.0 mm to 10,000 mm.  
Figure 19 illustrates the signature curve of the 170 \(\times\) 2.0 profile for upward loading. It is noteworthy that the curve's minimum points correspond to critical buckling modes. Moreover, the critical global buckling mode can be directly identified on the curve when the half-wavelength equals the beam's unbraced length, provided it is identified as the global mode through modal identification analysis. In Figure 19, the critical modes identified for the curve minima and the half-wavelength equal to the unbraced length (4000 mm) are highlighted, along with the corresponding critical elastic buckling moment, which is 1.46 kN.m.  
It is important to note that CUFSM only allows the consideration of a uniform bending moment along the length of the profile. For other types of loading, such as non-uniform bending moment diagrams, a modification factor needs to be calculated [6], as outlined in Section 2.2. Consequently, the critical elastic buckling moments obtained with GBTUL and CUFSM do not align due to this limitation.  
Figure 18: Critical section for the 170 \(\times\) 2.0 profile under (**a**) downward loading and (**b**) upward loading.
## 5 Docking Analysis of AMPK with Flavonoids  
AMPK activators are broadly categorized into two types: indirect AMPK activators and direct AMPK activators. Indirect activators, such as biguanides and thiazoltiones (TZDs), activate AMPK by altering the cellular energy status, typically by increasing the AMP/ATP ratio. Metformin, a widely used anti-diabetic drug, exemplifies this mechanism by inhibiting mitochondrial complex I, which results in elevated AMP levels and subsequent AMPK activation, although some effects of metformin may be independent of AMPK [66]. Similarly, TZDs, including rosiglitazone, activate AMPK by inhibiting mitochondrial function and inducing adiponectin release, which further activates AMPK in other tissues [67]. In contrast, direct AMPK activators work by directly binding to and activating AMPK without significantly altering the cellular levels of ATP, ADP, or AMP. For example, AICAR, the first known direct activator, mimics AMP and binds to the \(\gamma\) subunit of AMPK, effectively activating it [68,69]. Thienopyridones, such as A-769662, allosterically activate AMPK, particularly in \(\beta\)1-containing complexes, without the need for Thr172 phosphorylation [70,71]. Salicylate, a pro-drug of aspirin, directly activates AMPK by binding to a site overlapping with that of A-769662, especially in \(\beta\)1-containing complexes [72]. Compound-2 (C-2) acts as a potent allosteric activator of AMPK by mimicking the effects of AMP. It binds to the AMPK\(\gamma\) subunit, competing with AMP, but does not affect other AMP-regulated enzymes [73,74]. The activation of AMPK through direct activators involves specific interactions with the \(\alpha\) and \(\beta\) subunits. Thr172 phosphorylation on the \(\alpha\) subunit is crucial for AMPK activation, and while AMP typically promotes this phosphorylation by binding to the \(\gamma\) subunit, direct activators like A-769662 can enhance Thr172 phosphorylation
# Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix
## 4 Vessel Position Spatio-Temporal Prediction Model
### Description of the Problem
#### 4.2.3 Determining a Priori Matrix  
When the ASTGCN model processes trajectory data, it needs to define the prior matrix of node information, so that based on the spatio-temporal attention mechanism, the temporal and spatial features of trajectory data can be effectively captured through the graph convolutional layer, and the dependencies between feature nodes can be further established. For the specific task of AIS trajectory prediction, the prior matrix can be regarded as the correlation coefficient matrix between trajectory features, and the self-attention matrix output by the StemGNN model can be used as the prior input of ASTGCN. When dealing with multivariate time series prediction problems such as trajectory prediction, the StemGNN model learns the hidden association between variables through the Latent Correlation Layer as an adjacency matrix, which is passed into the two-layer StemGNN block. This model is universal to all multi-dimensional time series without predefined topology structures, and the output self-attention matrix can be used as the prior input of other graph-based time series prediction models. Therefore, this paper uses StemGNN as one of the comparison models. The Latent correlation layer correlation matrix of the StemGNN after training is used as the prior input of ASTGCN, and the experiment of vessel trajectory prediction is then carried out.
## 2 Methodology
### Experimental Setup  
A switching impulse voltage generator available at the High Voltage laboratory of SASTRA was used to inject the switching impulse voltage into (1U) terminal of the delta connected HV winding. The other terminals of the delta windings (1V & 1W) were shorted and grounded through a low ohmic resistor (245 \(\Omega\) in this case). The core, tank, and neutral terminal (2n) of the LV winding were grounded. The terminals (2u, 2v, and 2w) of the LV windings were kept floating [29,30]. Inter-turn shorts were emulated by connecting the 2400 \(\Omega\) resistor between the tappings, and the four PD cases were emulated by connecting the PD model (the series combination of the oil gap and a 2400 \(\Omega\) resistor (as referred in Table 1).  
Input (V1) and Response (V2) voltage waveforms were measured while using a DSO. The FFT function of the DSO was used to obtain the magnitudes of the various frequency components of (V1) and (V2).  
Figure 2 shows how the connections can be made at the special tappings of the transformers for the emulation of the inter-turn shorts and the PD sources across the minor and major insulation. All of the inter-turn shorts and PD cases listed in Table 1 were emulated in a similar manner.  
Figure 1: (**a**) Circuit representation of the Transformer. (**b**) Photograph of the transformer (**c**) Circuit representation of the Partial Discharge (PD) model. (**d**) Photograph of the PD model.  
Figure 2: Circuit connections for emulation of faults: (**a**) Inter-turn short (Sh-1). (**b**) PD source across minor insulation (PD-1-minor) (**c**) PD source across major insulation (PD-3-major).  
Figure 3 shows the experimental setup of one of the cases (PD sources across the minor insulation of the tested winding). Figure 4 shows the experimental setup photograph.
## 1 Introduction
It is called \(\eta\)-strongly monotone if there is \(\eta>0\) such that  
\[\eta\|x-y\|^{2}\leq\langle Fx-Fy,x-y\rangle,\quad\forall x,y\in C.\]  
Moreover, it is called \(\alpha\)-inverse-strongly monotone (or \(\alpha\)-cocoercive) if there is a constant \(\alpha>0\) such that  
\[\alpha\|Fx-Fy\|^{2}\leq\langle Fx-Fy,x-y\rangle,\quad\forall x,y\in C.\]  
The class of inverse-strongly monotone operators or \(\alpha\)-cocoercive operators has been in the spotlight of theoretical research and studied from the viewpoint of numerical computation and many results were obtained in Hilbert (and more generally, in Banach) spaces; see References [19; 20; 21; 22; 23; 24] and the references therein. Let \(X\) be a real Banach space whose dual space is denoted by \(X^{*}\). The well-known normalized duality operator \(J:X\to 2^{X^{*}}\) is defined by  
\[J(x)=\{\psi\in X^{*}:\langle x,\psi\rangle=\|x\|^{2}=\|\psi\|^{2}\},\quad \forall x\in X,\]  
where \(\langle\cdot,\cdot\rangle\) is the duality pairing between \(E\) and \(E^{*}\). A mapping \(T\) with domain \(D(T)\) and range \(R(T)\) in \(X\) is called pseudocontractive if the inequality holds  
\[\|x-y+r((I-T)x-(I-T)y)\|\geq\|x-y\|,\quad\forall x,y\in D(T),\forall r>0.\]  
[PERSON]'s results [25] told us that the notion of pseudocontraction is equivalent to the one that for each \(x,y\in D(T)\), there exists \(j(x-y)\in J(x-y)\) such that  
\[\langle Tx-Ty,j(x-y)\rangle\leq\|x-y\|^{2}.\]  
The purpose of this paper is act as a continuation of Reference [26], that is to introduce and analyze a multiple hybrid implicit iteration method for solving a monotone variational inequality with a variational inequality constraint for two inverse-strongly monotone mappings and a common fixed point problem (CFPP) of a countable family of uniformly Lipschitzian pseudocontractive mappings and an asymptotically nonexpansive mapping in Hilbert spaces, which is called the triple hierarchical constrained variational inequality (THCVI). Here, the multiple hybrid implicit iteration method is based on the [PERSON]'s viscosity approximation method, [PERSON]'s extragradient method, [PERSON] method, and the hybrid steepest-descent method. Under some suitable assumptions, strong convergence of the proposed method to the unique solution of the THCVI is derived.
## 2 Dynamic Modeling of Fully Flexible Base-Link-Joint Space Robot
### Flexible Link Model  
In this paper, the flexible link of the robot arm is equated to a simply supported beam, which is analyzed by using the Euler-Bernoulli beam correlation theory with the hypothetical modal method. Take its flexural stiffness \(EI_{k}\) as a constant value, and the deformation of flexible link \(B_{k}(k=1,2)\) as:  
\[v_{k}(x_{k},t)=\sum_{j=1}^{n_{k}}\phi_{kj}(x_{k})\delta_{kj}(t),\ (0\leq x_{k} \leq\ l_{k}), \tag{1}\]  
where \(\phi_{kj},\delta_{kj}\) denotes the j order modal function of link \(B_{k}\) and its corresponding modal coordinates, \(\phi_{kj}(x_{k})=\sin\Bigl{(}\frac{j\pi}{l_{k}}x_{k}\Bigr{)}\), \(n_{k}\) are the retained modal numbers, and since the large-amplitude vibration is mainly composed of the first few modal orders, this paper takes \(n_{k}=2\).
# Scanning Electron Microscopy Techniques in the Analysis of Gunshot Residues: A Literature Review
### Historical Evolution and Technological Advances in SEM for GSR Analysis  
The application of scanning electron microscopy (SEM) in forensic science represents a significant advancement in analytical methodology, particularly in the detection and characterization of gunshot residues (GSRs). The historical development of SEM for forensic purposes can be traced back to the late 1960s and early 1970s, a period marked by the need for more reliable, objective, and reproducible techniques to identify microscopic evidence. Initial approaches in GSR detection heavily relied on chemical tests such as the Modified Griess Test and sodium rhodizonate staining, which provided limited information due to their susceptibility to false positives and a lack of specificity in complex forensic scenarios.  
The introduction of SEM into forensic laboratories revolutionized the analytical workflow for GSR detection, allowing for the direct observation of individual particles at high magnification and resolution. SEM's unique capability to perform simultaneous morphological and elemental analysis, particularly when coupled with energy-dispersive X-ray spectroscopy (EDS), enabled forensic experts to move beyond simple presumptive tests toward the conclusive identification of GSRs. This combination of imaging and chemical profiling has since become the gold standard for GSR analysis, providing highly reliable results that are widely accepted in judicial settings. Over the years, technological advances--such as improved electron optics, the development of field-emission electron guns (FEGs), and the integration of automated particle recognition systems--have further enhanced the resolution, sensitivity, and throughput of SEM-based GSR analysis.
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
## 2 Data Description  
The COSMIC-2 near-real-time (NRT) level 2 data (UCAR COSMIC Program) [36] are used in this study. These data were operationally processed by the COSMIC Data Analysis and Archive Center (CDAAC). The vertical profiles of bending angle and refractivity observations are provided at a vertical interval of 20 m, and the 1D-Var retrieved vertical profiles of temperature and specific humidity are given at an interval of 100 m. The observed bending angle profiles are used for obtaining the lowest height of RO observations. The COSMIC-2 RO data are available on the website www.cosmic.ucar.edu (accessed on 1 April 2022).  
Figure 1 shows the monthly variations of data count of COSMIC-2 RO measurements in 2020 over the SEP area. The data count is the largest in August (about 8000 profiles), and the smallest in June (about 6000 profiles). The data count gradually decreases from the 5-km altitude to the surface (see Figure 1a) because some RO profiles end at certain heights. The vertical variations of RO data count are similar among different months. The data counts at 0.2, 3, and 8-km altitudes are shown in Figure 1b in different months of 2020. It can be seen that more than 50% of the COSMIC-2 RO observations can detect below 200 m, and there is a small monthly variation of RO data count at 0.2, 3, and 8 km. We employed the RO data in August 2020 for this study.  
The ECMWF ERA5 reanalysis is used to obtain the atmospheric conditions near the lowest height of the COSMIC-2 RO profiles. The ERA5 reanalysis provides hourly data of global atmospheric temperature, humidity, cloud liquid/ice water content (LWC/IWC), cloud liquid/ice water path (LWP/IWP), and PBL top height at the horizontal resolution of 0.25 \(\times\) 0.25\({}^{\circ}\). Besides conventional observations and satellite radiance measurements, GPS RO observations were also assimilated in the ERA5 reanalysis [37]. For vertical profiles of the atmospheric variables, the ERA5 reanalysis provided to the public have a total of 37 levels from 1000 to 1 hPa. Figure 2 shows the monthly mean LWP distribution within the latitudinal band (60\({}^{\circ}\)S, 60\({}^{\circ}\)N) in August 2020 from the ERA5 reanalysis. The Intertropical Convergence Zone (ITCZ) is characterized by a zone of maximum LWP near the equator in the west and middle Pacific oceans in the Northern Hemisphere. It is seen that the SEP and SCP areas, which are defined as the regions (115\({}^{\circ}\)W-70\({}^{\circ}\)W, 40\({}^{\circ}\)S-0\({}^{\circ}\)) and (170\({}^{\circ}\)W-130\({}^{\circ}\)W, 40\({}^{\circ}\)S-0\({}^{\circ}\)), respectively, are located to the south of the ITCZ.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Evaluation Criteria of Sparse Time-Series Clustering
#### 2.3.2 Adjusted Rand Index  
The _Adjusted Rand Index_ (ARI) [25; 26] is essentially a normalization of RI so that a random clustering obtains an ARI equal to 0. A simple and natural way to define ARI is:  
\[\text{ARI}=\frac{\text{RI}-\mathbb{E}[\text{RI}]}{1-\mathbb{E}[\text{RI}]}\,, \tag{6}\]  
where \(\mathbb{E}[\text{RI}]\) is computed over all random clusterings with given cluster sizes compared against the ground truth clustering. (6) defines ARI as the fraction by which the RI of the computed clustering (against a fixed ground truth clustering) outperforms the RI of a random clustering (with given cluster sizes, against the same ground truth clustering).  
An equivalent definition of ARI is given in ([27], Section 2):  
\[\text{ARI}=\frac{2\left(\text{SS}\cdot\text{DD}-\text{SD}\cdot\text{DS}\right) }{(\text{SS}+\text{SD})(\text{SD}+\text{DD})+(\text{SS}+\text{DS})(\text{ DS}+\text{DD})}\,, \tag{7}\]  
where SS (resp. DD) is the number of pairs of data points that belong to the same cluster (resp. to different clusters) in both clusterings and DS (resp. SD) is the number of pairs of data points they belong to different clusters (resp. to the same cluster) in the ground truth clustering and to the same cluster (resp. to different clusters) in the clustering computed by the algorithm.  
A perfect agreement among two clusterings is denoted by \(\text{ARI}=1.0\), while an essentially random clustering is denoted by \(\text{ARI}=0.0\). ARI can take negative values, denotingclusterings with an unusually high number of discordant data point pairs. However, correcting for \(\mathbb{E}[\mathrm{RI}]\) in (6) implies that ARI values do not explicitly depend on the number \(k\) of clusters.