esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Degree of Saturation on Soil-Pile Interactions for Piles in Expansive Soils
## 3 Laboratory Testing and Results
### Pile Uplift Model Tests  
A series of laboratory tests were conducted to analyze soil-pile interaction and determine the uplift coefficient under varying saturation levels. Soil samples were prepared at 60%, 70%, 80%, and 90% saturation based on the compaction curve for the direct shear tests. A cement mortar pile model (1:20.45 mix, reinforced with RB6) was used, measuring 2.17 cm in diameter and 26 cm in length (refer to Figure 6a).  
The pile uplift test was conducted in a modified sphere container (25 cm in diameter \(\times\) 30 cm in height) with perforations for water infiltration. Perforated pipes facilitated soil saturation, while a sand layer at the bottom, separated by geotextile, allowed infiltration from below. To reduce friction-induced stress loss, the container walls were lubricated with grease (refer to Figure 6b).  
The reinforced cement mortar pile was centrally positioned within the mold during compaction, ensuring the pile tip was at the base to eliminate uplift force at the end. The soil samples were placed in six layers, each statically loaded, and stored in a humidity-controlled environment for 24 h to maintain uniform initial water content.  
The schematic view of the pile uplift model test is presented in Figure 7. During the testing, the unsaturated soil was gradually wetted via external water infiltration through the center of the container. Tensiometers (Model 2100F) at various depths recorded changes in matric suction. Water infiltration caused soil swelling, generating uplift forces along the pile shaft. Uplift displacement was measured using a transducer, with the load frame's adjustable screw maintaining the pile's position. The uplift forces were recorded using  
Figure 5: Variation in shear strength with matric suction under different net normal stress.  
Figure 6: Pile test model and container used in the model. (**a**) Pile uplift model. (**b**) Container used.  
a 2 kN load cell at 10 min intervals until stabilization or the estimated maximum was reached. This procedure was repeated for different initial saturation levels, with matrix suction measured throughout the test.  
Uplift force development was driven by skin friction resistance. As water infiltrated, soil have increased uplift stress, leading to pile displacement. Initially, the uplift force rose sharply, but as saturation neared completion, the swelling potential was reduced, stabilizing the uplift interface shear stress. The test data indicated that full stabilization took between two and three weeks. Initially, when the soil is not wetted, matric suction dominates downward interface shear stress; however, as the soil is wetted, the swelling pressure becomes the primary contributor to the uplift force. -
# Analysis of Thermomechanical Stresses of a Photovoltaic Panel Using a Passive System of Cooling
## 3 Results
The mechanical stress at the corner is low because of the possibility of free expanding. Figure 8b shows the critical area, which represents the electric contact that, under high cycles, could lead to a fracture. Figure 7: Temperature fields. (**a**) Temperature distribution of the whole panel EVA 0.5 mm. (**b**) Temperature distribution of the whole panel, EVA 0.2 mm. (**c**) Temperature distribution of the whole panel with rectangular fins. (**d**) Temperature distribution of the whole panel, EVA 0.2 mm. (**e**) Temperature distribution of the whole panel, fins 1 mm of thickness EVA 0.2 mm. Figure 8: _Cont._  
Comparing Figure 8a with Figure 8c, a considerable increase in equivalent stress can be seen. This is because the fins on the back panel restrict the free movement. A 60 MPa increment is present in Figure 8b versus Figure 8d, which could lead to a fracture. In regard to Figure 8e, it can be seen there is 0.223 mm of displacement that generates the up stresses. Figure 8: Maximum and minimum stress fields. (**a**) Stress distribution of the whole panel. (**b**) Stress distribution at the electric contact. (**c**) Stress distribution of the whole panel with fins. (**d**) Stress distribution at the electric contact with fins. (**e**) Total deformation at the panel. Figure 9 shows the results obtained on the back of the panel with the different models studied and Table 2 shows the parameters used. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Convection & 20 W/m2 \({}^{\circ}\)C @ 30 \({}^{\circ}\)C \\ \hline Radiation & 0.85 @ 30 \({}^{\circ}\)C \\ \hline Radiation 2 & 0.90 @ 30 \({}^{\circ}\)C \\ \hline Radiation 3 & 0.71 @ 30 \({}^{\circ}\)C \\ \hline Heat flux & 1000 W/m2 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of different designs. Figure 9: Results of temperature and stresses of the studied cases. (**a**) Field temperature distribution of the geometries studied; (**b**) Field stress distribution of the geometries studied. In Figure 9, it can been seen that there were 19 geometries studied with different configurations (Table 3). Initially, the conventional panel had a maximum temperature and stresses of 50.19 \({}^{\circ}\)C and 56.3 MPa, respectively. Secondly, an array with 51 fins of 5 cm \(\times\) 5 cm \(\times\) 1 m and 1 mm of width was tested; a considerable drop was seen of almost 10 \({}^{\circ}\)C and stresses remained stable. Then, 24 triangle fins maintained a mean temperature, but an important increase in stress was shown. From 10 to 14, an array with 48 triangle fins presented a temperature average of 40 \({}^{\circ}\)C and a maximum stress of 63.87 MPa. Finally, a stress of 107.11 MPa was presented with 53 triangle fins of 5 cm \(\times\) 5 cm \(\times\) 30 cm and 1 mm of width.
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
## 3 Results and Discussion
### Interference Management  
Interference management in 5G communication systems using time series analysis is the process of controlling and minimizing interference from sources that could disrupt the network and data transmission.  
Figure 4 exposes the comparison of interference administration. In a computation cycle, the proposed TSA reached 99.36% interference management. The existing FCS achieved 69.58%, MDM reached 44.12%, NMAA reached 90.12%, and SBRA obtained 65.89% interference management.
## 5 Materials and Preparation of Digital Models for Acoustic Simulations  
Among all considered case studies, only the Panathenaic Stadium is still existing, while Circus Maximum and Stadium of Domitian are no longer in place. The digital reconstruction of these three archaeological sites as they appeared in their original shapes were realised with the aid of historical drawings, notes, and descriptions coming from the outcomes of archaeologists, as indicated in Section 3. Table 1 summarises the architectural features of the _stadia_ as they have been digitally rebuilt using their original aspects.  
All the surfaces were drawn as flat planes in AutoCAD 2024 software, while the layers were grouped by material, which were mainly:  
* stone for the steps,
* timber for the steps under the porch (case of Circus Maximum),
* sand for the ground where the games were played.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Panathenaic Stadium** & **Circus Maximum** & **Stadium of Domitian** \\ \hline Dimension of longitudinal axis at & & & \\ the external perimeter (m) & 262 & 601 & 275 \\ Dimension of transversal axis at the & & & \\ external perimeter (m) & 128.5 & 160 & 106 \\ Width of ground (m) & 42.7 & 84 & 50 \\ Length of _spina_ (m) & - & 369 & 130 \\ No. of _mamiana_ & 2 & 3 & 2 \\ Total no. of steps & 48 & 63 & 28 \\ Total capacity (no. of spectators) & 50,000 & 385–450,000 & 50,000 \\ External height (m) & 20.7 & 66.5 & 19.5 \\ Volume (m\({}^{3}\)) & Approx. 650,808 & Approx. 5,852,997 & Approx. 248,187 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architectural characteristics of ancient sport buildings.  
Figure 4: Digital reconstruction of the Stadium of Domitian in Rome.  
Regarding the material properties applied to the digital surfaces, the coefficients related to stone and wood are taken from in situ measurements inside the Roman theatre of Tymdaris by using the Laser Doppler Vibrometer (LDV) as a non-invasive methodology appropriate for archaeological sites [9]. The material properties of sand are taken from previous research on granular silica experimental tests [10]. Absorption coefficient equal to \(\alpha=1\) across all the frequency bandwidth is applied to the opening surfaces. The acoustic simulations are conducted in unoccupied conditions and with 100% audience occupancy [11]; this latest scenario has been simulated by applying the coefficients of light upholstery to the steps [12], excluding the corridors (_praccinctions_) separating the _maeniana_. Table 2 summarises the absorption and scattering coefficients applied to the surfaces of the digital models.  
Since there was no possibility to conduct acoustic measurements in these ancient _stadia_, the calibration process is not included as one of the steps of the full procedure to run the acoustic simulations. This represents a limitation of this study. However, the reliability of the results consists of the site measurements on materials existing in other Roman theatres and amphitheatres, conducted by the authors with innovative techniques, as anticipated in the previous paragraph [9].
## 3 Results and Discussion
### Ablation Study
Figure 5: Visualization of some feature maps generated by the first residual group in RRG, where three similar feature map pair examples are annotated with boxes of the same color. One feature map in the pair can be approximately obtained by transforming the other one through inexpensive operations. Evaluating the effectiveness of the multi-level spatial aggregation strategy (MSAS):  
We selected MSAS as the baseline and summarize all the relevant results in Table 3. We observed that ASP-MSAS consistently improved performance in both the spatial attention (SA) and Inter-scale Attention (ISA) schemes, while MSAS showed a slight performance increase compared to the baseline object context (OC) mechanism. In Formula (3), we found that our method consistently outperformed the baseline under different group settings, and achieved the best results when the number of groups was set to eight. Therefore, in all experiments, we defaulted the number of groups to eight, enabling the model to achieve optimal performance.
## 3 Results and Discussion
### Settlement and Pore Water Pressure  
The settlement amount of the model structure was analyzed using the value measured through the LVDT, installed in the center of the model structure; in the case of the pore water pressure, the value measured through the PWP1 sensor, installed closest to the base surface of the model structure, was analyzed. In both the non-reinforcing and reinforcing experiments, the results were analyzed using the LVDT and pore water pressure measurement values at the same location.
# Image Quality Improvement and Memory-Saving in a Permanent-Magnet-Array-Based MRI System
## 2 Theory
### Signal Equation & Iterative Matrix Method for Image Reconstruction in a PMA-Based MRI System
For image reconstruction, Equation (3) in time domain can be solved by using conjugate gradient method [16] where a symmetric, positively definite matrix need to be generated. In the  
Figure 1: An irregular inward-outward ring-pair aggregate proposed in [6] (**a**) 3D view, (**b**) front view, (**c**) Pulse Sequence. time domain, the conjugate of the matrix \(\bar{\bar{E}}\), \(\bar{\bar{E}}^{\dagger}\) multiplies both sides of Equation (3), we arrive at the following equation,  
\[\bar{\bar{E}}^{\dagger}\bar{s}=\bar{\bar{E}}^{\dagger}\bar{\bar{E}}\bar{m} \tag{5}\]  
Let \(\bar{b}=\bar{\bar{E}}^{\dagger}s\), and \(\bar{\bar{A}}=\bar{\bar{E}}^{\dagger}\bar{\bar{E}}\), Equation (5) can be re-expressed as,  
\[\bar{b}=\bar{A}\bar{m} \tag{6}\]  
The image can be reconstructed by solving Equation (6).
# Deep Reinforcement Learning Based Resource Management in UAV-Assisted IoT Networks
## 3 System Model
To calculate the entire throughput of the separate user and total throughput (\(T\)) as in Equation (6):  
\[T_{U}<Phy_{u},T_{e_{u}}=t_{u}T_{U},\ \forall_{u}\ \in\ (M_{U}),T_{U}=min(R_{U},\ T_{e_{u}}), \tag{5}\]  
\[T=\sum u\in Nu\ T_{u},\ \ T=\sum Rss\in Rss_{n}\ T_{u}\ \frac{Rss_{u}+\ Rss_{2}+\ Rss_{2} \ldots\ Rss_{n}}{Rss_{u}}, \tag{6}\]  
\[T=\ \sum Rss\ \in\ Rss_{n}\ T_{u}\frac{ArgRss_{C}}{\frac{1}{2}},\]  
where \(Rss_{1}+\ Rss_{2}+\ Rss_{3}\ldots\ Rss_{n}\) is the sum of all \(Rss_{s}\) values in each cluster, \(AvgRss_{C}\) is the average number of \(Rss_{s}\) values per clusters, and \(Ts/s\) is the time slot to produce signal values. Therefore, this manages the optimal allocation of throughput values. Equation (7) calculated the analysis of power transmission (Pt) for the A2G access link with the altitude between M to U (\(D_{um}\)). Let \(PhyD_{um}\) represent the data rate access ratio from M to U access. As a reference, we used the Shannon formula, as in Equation (8), where \(G_{Tx}\) and \(G_{Rx}\) are the Tx and Rx antenna gains, respectively, i.e., the difference between the average transmitted and received power in a random transmit as well as received direction. Then, \(P_{l}\) is computed as:  
\[Pt_{Dum}(dB)\ =P_{l}G_{Tx}G_{Rx}\ (\frac{\lambda}{4_{NDum}})^{\alpha},\ PhyD_{um}= Blog_{2}(1+\frac{P_{l}G_{Tx}G_{Rx}}{I_{n,\ N}})(\frac{\lambda}{4_{NDum}})^{\alpha}), \tag{7}\]  
\[Pt_{Dum}(dB)\ =\ P_{Tx}+G_{Tx}+G_{Rx}+10 log10\Bigg{(}\frac{\lambda}{4_{\pi D_{um }2}}\Bigg{)}, \tag{8}\]  
where \(Pt_{D_{um}}\) is the full transmitting power of the UAV and the total received power of users by dBm in all directions. The value of lambda (\(\lambda\)) is the wavelength of the radio rate. In short, user throughput is generated through the available values of bandwidth, data rate ratio, and the efficient throughput capacity to users.
# Ru(II)-Dppz Derivatives and Their Interactions with DNA: Thirty Years and Counting
## 3 DNA Binding
### Solvent Effects on the Photophysical Properties of the Ru-Dppz Complexes  
The effect of the solvent on the spectroscopic and photophysical properties of the [Ru(phen)\({}_{2}\)dppz]\({}^{2+}\) complex was systematically studied for the first time by [PERSON] [105]. The studies carried out showed that the absorption spectra in water and in various non-aqueous solvents were very similar, while the emission energies, as well as the lifetime and quantum yield of luminescence, were extremely solvent-dependent. It was shown that there was no correlation between the maximum luminescence and the polarity of the solvent. In particular, as already pointed out, no emission was observed in water and trifluoroethanol. On the other hand, quantum yield and luminescence lifetime were particularly sensitive to solvent polarity.  
Both of these quantities were reduced in high-polarity solvents, and this trend was correlated with the empirical quantity E\({}_{\rm T}\), which reflected, to some extent, both a measure of the polarity of the solvent and its ability to make h-bond. Furthermore, studies carried out on [Ru(phen)\({}_{2}\)dppz]\({}^{2+}\) showed that k\({}_{\rm nr}\) (rate constants for non-radiative processes) was almost independent of solvent polarity, while all non-radiative constants were, and somehow became greater, as polarity increased.  
The energy gap law that correlated the non-radiative constants with the difference in energy between the fundamental and excited luminescent states did not appear to be respected in this case, unlike what happened for example for the parent compound [Ru(bpy)\({}_{3}\)]\({}^{2+}\).  
Contrary to the assumption that H-bonding to phenazine nitrogen atoms in the excited state was capable of activating non-radiative channels and thus quenching the emission, the improved correlation observed between ln(k\({}_{\rm nr}\)) and E\({}_{\rm T}\), compared to the H-bond-donating capacity of the solvent alone [106], made it possible to highlight that the forces at play, and the mechanism, were subtler (or involve concomitant events) than predicted by early studies.  
The light-switch phenomenon for this family of compounds was indeed closely related to the effect of the solvent on the two possible states, involving the metal and the bipyridine or phenazine moiety of the dppz. In two fundamental studies, it was shown that this effect resulted from a thermal equilibrium between these two excited states, one enthalpically favored (the state involving the phenazine fragment) and the other entropically favored. This behavior could be attributed to the fact that charge separation induced a rearrangement (and thus greater order) of the surrounding solvent [107].  
The competition between the two different thermodynamic contributions results in a non-obvious inversion of the populations of excited states, as a function of solvent--the photoluminescence 3 MLCT state essentially dominates in aprotic solvents (or in the presence of double-stranded DNA), while the CS state population becomes prominent in protic solvents [106].
## 2 Related Work
### Self-Attention Mechanisms  
As the key design in transformers, the original self-attention is implemented by matrix multiplication as \(softmax(\frac{(XW_{Q})(XW_{K})^{T}}{\sqrt{d_{z}}})(XW_{V})\), where the projections \(W_{Q},W_{K}\), \(W_{V}\in R^{dx\cdot dz}\) are parameter matrices, \(d_{x}\) is the number of queries (keys/values) and \(d_{z}\) is the projection dimension. From this formula, we can see that the computational complexity is quadratic to the sequence length (\(\Omega(n^{2})\)), i.e., input image size in vision transformers. For image classification, the input size is usually not very large, thus the computation cost is acceptable. But for some dense prediction tasks (e.g., object detection and segmentation), the input image size is much larger than image classification. In such a case, the quadratic cost is unbearable. There are two popular ways to overcome this. One is to apply the fine local self-attention [8; 21; 22; 23], and the other is to adopt sparse global self-attention [7; 9; 33] to approximate the original self-attention. [PERSON] et al. [23] proposed an efficient window-based self-attention called Swin Transformer, which employs a shifted-window partitioning approach to add the interactions across different local windows. [PERSON] et al. [33] proposed a pyramid vision transformer (PVT) for dense prediction tasks, in which spatial reduction attention (SRA) is presented to approximate the multi-head self-attention in ViT [5]. However, the small-scale information is sacrificed in PVT and the long-range dependencies are limited in a fixed-size window in Swin Transformer. Unlike the existing methods, in order to break the limitations of the fixed-size window, we propose a well-designed window-based self-attention mechanism. More specifically, our proposed self-attention mechanism can be decoupled into LSA and GSA. The LSA is used to enhance the local receptive file and the GSA uses multi-scaled dilated pooling to promote global contextual information.
# Theory of AdaDelSPGD Algorithm in Fiber Laser-Phased Array Multiplex Communication Systems
## 2 Theoretical Analysis and Simulation of the Improved SPGD Algorithm  
A structural diagram of the N-channel fiber laser-phased array multiplex communication system with the SPGD algorithm is presented in Figure 1. The system is mainly composed of four subsystems: laser transmitting, communication modulation, phase locking, and communication receiving.  
The laser transmitting subsystem is mainly composed of seed source and beam splitter, which is divided into N channels according to requirements.  
The communication modulation subsystem mainly modulates the laser communication signal. The bit error rate analyzer and microwave amplifier provide the communication voltage signal. The signal is added to the N channel intensity modulator to conduct communication modulation of the laser.  
The phase locking subsystem is a closed loop control system. After communication modulation, the optical signal goes through phase modulator first, and then through V slot beam output. The purpose of using V-groove here is to control the parallelism and spacing of N-channel laser. The laser output from the V-groove is then divided into two parts by a beam splitter. One part continues to output in this direction, while the other part enters the phase-locked detector (PD1). Here, an attenuator can be added before entering the detector according to the different power received by the detector. The voltage signal obtained by the detector enters the control module as a feedback signal. The algorithm is in the control module. After the algorithm, the new voltage value is added to the phase modulator.  
The communication receiving subsystem is mainly used to receive the synthesized beam after communication modulation and phase lock, and analyze the laser through oscilloscope and detector. After the previous beam splitter, the laser is coupled to the optical fiber through the collimator, and then the optical signal is amplified by the optical fiber amplifier. Finally, the detector converts the optical signal into electrical signal and observes the effect of communication modulation through oscilloscope.
## 5 Swarm Cluster Prediction
### Prediction Data Visualization  
In the path loss profiling and modeling, the predicted \(RSS\) and K-means clustering data were visualized. However, the complete procedure is described in Figure 24. Whereas Figure 25 shows the head map with two predictions: UAV clusters and their corresponding \(RSS\). In other words, the heat map can be considered to show the cluster power \(P_{n}\) of parameters (mentioned in Table 1). The odd column shows the predicted \(RSS\), and even columns are UAVs' clustering distances. The rows in the heat map are the numbers of UAVs; \(N=10\). Moreover, it has been observed that the UAVs, on there reallocation, receive power (\(P_{n}\)) in the range of \(-30\) to \(-40\) dB. In Figure 26, the contour plot shows the predicted power for the visualization of \(RSS\). Finally, it is shown the whole swarm has a similar range of \(RSS\) with normalized clustered distances.  
In Table 2, a comparison is presented wherein a number of research works are listed. According to that table, [30; 31; 32; 33; 34; 35] used the conventional approaches towards communication, control, networking and collaboration for UAVs. As listed in Table 2, most reported works did not incorporate a UAV swarm, whereas the proposed UAV swarm technique has the potential to enhance the effectiveness of such applications. Few researchers have adopted swarm UAVs for other applications while using a machine-learning approach. However, the presented work is intelligent enough to utilize a machine-learning algorithm for UAV swarm formation. Additionally, a machine-learning-based communication model is also presented here for intra-swarm communication. As a result, the proper UAV swarm formations, along with received signal strength (\(-30\) dB to \(-40\) dB), have been achieved. Therefore, the presented work can be considered a good candidate for search-and-rescue applications.  
Figure 23: Hierarchical clustering for isosceles-swarm.  
Figure 24: Hierarchical clustering for isosceles swarm.  
Figure 25: Heat map for hierarchical clustering to \(RSS\) (\(P_{n}\) cluster power) prediction with random forest.  
Figure 26: Contour plot for predicted \(RSS\) of UAVs cluster with \(N=\)10.