esa-sceva/satcom-chunk-collection huggingface dataset:
# Degradation Assessment of In-Service Transformer Oil Based on Electrical and Chemical Properties
## 4 Discussion
### Assessment of the DGA Results  
Transformer oil aging is a complex process accelerated by several operational stressors, particularly thermal and electrical faults. The degradation of oil over time is further influenced by contaminants, such as metallic particles, moisture, and cellulose fibers from insulation breakdown. These factors contribute to the chemical decomposition of both transformer oil- and cellulose-based insulation, generating various low-molecular-weight gases that dissolve within the oil. DGA enables the identification of specific fault types by quantifying these gases and interpreting their ratios.  
In this study, the concentration of dissolved gases across three transformers was observed to remain below critical diagnostic thresholds during successive service years, which precluded the application of conventional DGA fault identification techniques, such as the Dornenburg ratio and Roger ratio methods [6]. Instead, our analysis was directed towards understanding the implications of trace gas concentrations on the degradation patterns of transformer insulation materials according to Table 4[41].  
As observed from the results of DGA, the initial presence of gases, such as methane, acetylene, ethylene, ethane, and hydrogen, during the first two years of service for transformers T1 and T2, is indicative of the initial stages of insulation oil decomposition, likely resulting from fault activities. Notably, an increasing trend in hydrogen concentration was observed over these initial years, suggesting active degradation. In subsequent service years, the near absence of these gases indicates a significant reduction in the decomposition rate, which aligns with a decrease in LV side currents, oil temperature, and winding temperature, as mentioned in Table 3. This stabilization implies mitigation of the fault activity.
## 5 Conclusions  
This paper presents an approach for the assessment of IAQ in museum buildings taking into account both artwork conservation and visitor well-being. The proposed approach was implemented and applied in the world-famous Uffizi Gallery of Florence. Some representative rooms within the museum were identified and monitored over the short-term to assess relevant IAQ-related parameters, including the concentration of chemical (CO\({}_{2}\), VOCs) and biological pollutants (TBL and molds), in relation to microclimatic parameters (t, RH). No pathogens (microbiological) or chemical agents (concentrations of VOCs and CO\({}_{2}\)) were detected such as to require urgent intervention to eliminate potentially dangerous situations; however, IAQ can be improved through both simple and more complex interventions. Based on consideration of the indoor air pollution and the effectiveness of the HVAC systems in the Uffizi Gallery, some guidelines, that can be generalized for similar applications, are summarized below:  
* Thermo-hygrometric parameters: Check the HVAC regulation control system of the thermo-hygrometric parameters, especially on days when the museum is closed to the public, when the HVAC system may not be able to control these parameters to guarantee optimal ranges for both conservation and wellbeing;
* Carbon dioxide concentration: Increase the air flow rate, if possible, and ensure it is controlled based on CO\({}_{2}\) monitoring. Modification of the air distribution (inlet and outlet grids) can be achieved more easily with furnishing interventions; however, when these actions affect wall structures, their feasibility must be carefully assessed, especially in historical buildings. Consider the possibility of equipping the museum with a fixed CO\({}_{2}\) monitoring system;
* Volatile organic compound concentration: Although ventilation is a fundamental strategy for improving indoor air quality, the best way to maintain the quality of indoor air is to reduce sources of pollution indoors. After short-term monitoring, it is suggested that monitoring of VOCs is extended through targeted sampling of the most represented pollutants. Moreover, cleaning, sanitizing, disinfecting products and other products used for the maintenance of the building-plant system, can increase indoor air pollution as they can produce VOCs. To control IAQ, the composition of the chemicals used in the building must be checked and assessed. If dangerous or irritant substances are detected, green cleaning options, instead of chemical products containing high levels of VOCs, represents a safer and healthier strategy to prevent unsafe exposure and health effects, both for visitors and staff;
# Analysis of Development Coordination Levels between Skywalk Systems and Urban Spatial Environments
## 2 Literature
### Elements of Urban Environment Indicators  
#### 2.2.1 Classification of Indicators  
This subsection summarizes three types of environmental factors affecting the construction of a skywalk system, namely the natural environment, the socio-economic environment, and the urban built environment, based on relevant domestic and international research (Figure 2). The natural environment has a significant impact on the feasibility and comfort of the skywalk system [36]. Cities with extreme climates, such as high temperatures, cold weather, and heavy precipitation, are more likely to develop skywalk systems, with walkways serving as climate shelters. Socio-economic environmental factors play a crucial role in the acceptance and contestability of skywalk systems [41]. Cities with more complex social environments (e.g., high population density, high transportation demand, etc.) are more likely to generate skywalks, which can alleviate surface traffic congestion and provide alternative walking paths [7]. The urban built environment profoundly influences the form of skywalks. In addition, it is imperative to take into account the characteristics of existing buildings and the organic connection with above-ground and underground transportation systems while designing skywalks [5; 8]. This ensures a harmonious integration with the surrounding environment.
## 4 Ultrasonic-Assisted RIS Phase Shift Matrix Design Based on Subspace Self-Organizing Iterations
### RIS Phase Shift Matrix Design Based on Subspace Self-Organizing Iterations
According to the above analysis, orthogonality exists between \(\mathbf{G}_{RIS}\mathbf{A}_{RIS}\) and the noise subspace \(\mathbf{U}_{s}\). Therefore, we define a cost function:  
\[ \begin{array}{l}\overline{\mathbf{\Theta}}=\left\|\mathbf{U}_{n}^{H}G_{RIS}\mathbf{A}_{ RIS}\right\|_{\text{F}}^{2}\\ =\sum\limits_{i=1}^{N_{RIS}}\left\|\mathbf{U}_{n}^{H}\mathbf{G}_{RIS}\mathbf{a}_{RIS}( \overline{\theta}_{i})\right\|^{2}\\ =\sum\limits_{i=1}^{N_{RIS}}\left\|\mathbf{U}_{n}^{H}\mathbf{O}\big{(}\overline{\theta }_{i})\mathbf{g}^{H}\right\|^{2}\\ =\mathbf{g}\Bigg{(}\sum\limits_{i=1}^{N_{RIS}}\mathbf{U}_{n}^{H}\mathbf{O}\big{(}\overline {\theta}_{i})\mathbf{O}^{H}\big{(}\overline{\theta}_{i}\big{)}\mathbf{U}_{n}\Bigg{)} \mathbf{g}^{H}\end{array}  \tag{67}\]  
where \(\mathbf{O}\big{(}\overline{\theta}_{i}\big{)}=diag\big{\{}\mathbf{a}_{RIS}(\overline {\theta}_{i})\big{\}}\). The idea of the proposed RIS phase shift matrix design with self-organized iteration in subspace is to minimize the cost function as a criterion. One reflection element is fixed while adjusting the other elements from the first RIS reflection element. The final RIS phase shift matrix is obtained by iterating sequentially. Thus, the problem to be solved can be formulated as follows. \[ \begin{array}{l}\min\quad\overline{\mathbf{\Theta}}\\ \text{s.t.}\quad\mathbf{g}_{n}\Big{(}\mathbf{e}^{[n]}\Big{)}^{H}=1\end{array}  \tag{68}\]  
where \(\mathbf{e}^{[n]}\) denotes the unit column vector with the \(n\)-th vector being 1, and \(\mathbf{g}_{n}\) denotes the phase deviation vector when the \(n\)-th reflection element is fixed. In this process, \(\mathbf{g}_{n}\) is calculated as follows. \[ \begin{array}{l}\mathbf{g}_{n}=\frac{\Bigg{(}\sum\limits_{i=1}^{N_{RIS}}\mathbf{U}_{ n}^{H}\mathbf{O}\big{(}\overline{\theta}_{i}\big{)}\mathbf{O}^{H}\big{(}\overline{ \theta}_{i}\big{)}\mathbf{U}_{n}\Bigg{)}^{-1}\mathbf{e}^{[n]}}\\ \Big{(}\mathbf{e}^{[n]}\Big{)}^{H}\Bigg{(}\sum\limits_{i=1}^{N_{RIS}}\mathbf{U}_{n}^{H} \mathbf{O}\big{(}\overline{\theta}_{i}\big{)}\mathbf{O}^{H}\big{(}\overline{\theta}_{ i}\big{)}\mathbf{U}_{n}\Bigg{)}^{-1}\mathbf{e}^{[n]}}\end{array}  \tag{69}\]  
Next, the steps of the proposed subspace self-organized iterative RIS phase shift matrix design are shown in Algorithm 1:In the proposed subspace self-organizing iterative RIS phase shift matrix algorithm, the phase shift matrix will converge to the optimal solution after multiple iterations. And the iteration ends after meeting the threshold conditions. In addition, since our algorithm starts from the initial phase of the RIS obtained with ultrasound assistance, it does not start from a random phase value. This is beneficial as it reduces the number of iterations and improves the performance of the algorithm.
# Spatial Resolutions of On-Axis and Off-Axis Transmission Kikuchi Diffraction Methods
## 2 Materials and Methods  
### Sample Preparation  
To measure the spatial resolutions of the on-axis and off-axis TKD methods, an electron-transparent sample containing twin boundaries is needed. Ferritic steel was used in this study for this purpose. The sample was polished with a 1200-grit abrasive paper. Subsequently, diamond suspensions were used for mechanical polishing. Afterwards smooth finishing was carried out by ion milling by using a LEICA EM TIC 3X system (Leica Mikrosysteme GmbH, Vienna, Austria). During the ion milling, voltages of 5.5 kV and 4.5 kV were used for polishing.  
The polished sample was cut using a dual focused ion beam system in FEI Versa(tm) 3D SEM (FEI, Brno, Czech Republic). Before cutting, platinum was deposited on the surface of the sample to protect it from damage. The grain boundary of the sample was identified in a back-scatter electron channeling image. Subsequently, an area with both grains and boundaries was selected and cut off from the matrix. The sample was welded onto a copper holder for further thinning. Three samples were cut from the same twin boundary to ensure that the boundary width was the same. They were thinned to three different thicknesses by a low Ga ion current of 100 pA and a low voltage of 3 kV, which was also applied to remove the amorphous layer. Finally, the thicknesses of the three samples were evaluated by the platinum layer deposited on the surface using a back-scatter electron image. Five measurements were taken for each sample to obtain an average thickness; these were \(169\pm 1\) nm, \(91\pm 1\) nm, \(74\pm 2\) nm.
# Simulation of Two-Phase Flow of Shotcrete in a Bent Pipe Based on a CFD-DEM Coupling Model
## 2 Governing Equations
### Particle Contact Model  
The JKR contact theory is the extension of the Hertz contact theory. It assumes that the adhesion effect only occurs on the contact surface. When two particles do not adhere to each other, their contact radius \(a_{0}\) is expressed with the Hertz contact theory; otherwise, the external load is \(N\), and the contact surface radius is a \(>\upalpha_{0}\).  
To determine the effect of the adhesion force on the contact properties, [PERSON] [36] applied the Griffith energy method to determine the surface energy \(U_{s}=-\tau a^{2}\Delta\gamma\), where the total energy \(U_{T}\) of the system is a function of the contact area \(A\). When \(\frac{dU_{T}}{dA}=0\), \(U_{T}\) is in the equilibrium state. The equivalent load force \(N_{1}\) of the two particles affected by the external load \(N\) and surface adhesion can be determined as follows:  
\[N_{1}=N+3\pi R^{*}\Delta\gamma+\sqrt{\left(3\pi R^{*}\Delta\gamma\right)^{2}+6 \pi R^{*}\Delta\gamma N} \tag{9}\]  
The corresponding contact surface radius is as follows:  
\[a^{3}=\frac{3R^{*}}{4E^{*}}\bigg{[}N+3\pi R^{*}\Delta\gamma+\sqrt{\left(3\pi R ^{*}\Delta\gamma\right)^{2}+6\pi R^{*}\Delta\gamma N}\bigg{]} \tag{10}\]  
The amount of overlap \(\alpha\) of the two particles can be calculated with Equation (11):  
\[\alpha=\frac{a^{2}}{R^{*}}-\bigg{(}\frac{2\pi a\Delta\gamma}{E^{*}}\bigg{)}^{ 1/2} \tag{11}\]  
The increase in the normal force \(\Delta N\) with respect to the increase in the amount of overlap \(\Delta\alpha\) can be written as follows:  
\[\Delta N=2 aE^{*}\Delta\alpha\bigg{(}\frac{3\sqrt{N}-3\sqrt{N_{c}}}{3\sqrt{N}- \sqrt{N_{c}}}\bigg{)} \tag{12}\]  
Based on Equation (12), there are two conditions:  
For \(\Delta\gamma=0\), Equation (12) can be simplified to the Hertz contact force \(a^{3}=\frac{3R^{*}N}{4E^{*}}\) without considering the adhesion of the particles.  
For \(N=0\), the two particles adhere to each other, and the radius of the contact surface can be expressed as follows:  
\[a^{3}=\frac{9\pi\Delta\gamma\left(R^{*}\right)^{2}}{2E^{*}} \tag{13}\]  
When \(\left(3\pi R^{*}\Delta\gamma\right)^{2}+6\pi R^{*}\Delta\gamma N\geq 0\), Equation (1) can be solved. The result is presented in Equation (14):  
\[N\geq-\frac{3\pi R^{*}\Delta\gamma}{2} \tag{14}\]  
Thus, when the external load \(N\) is negative (the two particles attract each other), the radius of the contact surface decreases; for \(N\geq-\frac{3\pi R^{*}\Delta\gamma}{2}\), the particle adhesion is in a critical state, and when the pull force increases again, the two particles separate. The maximal pull force \(N_{c}\) required for separating the two particles is expressed as follows:  
\[N_{c}=\frac{3\pi R^{*}\Delta\gamma}{2} \tag{15}\]
## 7 IoRT Systems Based on Aerial NTN Networks
### Enhancement of Energy Efficiency  
This work introduces a drone-powered IoT relay system for efficient data collection in remote environmental monitoring applications [133]. The system utilizes 5-GHz communication technology for high-speed data transmission and low-power LoRa technology for energy optimization, enabling a sustained throughput of 3.5 MB/s for cached data collection at an altitude of 140 meters. This innovative approach offers a promising solution for intelligent environmental monitoring in regions lacking public networks, significantly enhancing data transmission from critical areas.  
In this study, the authors examine the future of 6G wireless communication networks, seeking improved metrics to connect a large number of devices, which presents challenges for current IoT applications [134]. Integrating non-orthogonal multiple access and spatial modulation techniques, a novel method called Spatial NOMA (S-NOMA) is introduced to increase energy efficiency. Multiple-Input Multiple-Output (MIMO) enables spatial modulation to selectively activate transmission antennas per symbol interval, thereby optimizing data rates and minimizing interantenna interference. In addition, an energy efficiency-focused optimization method for power allocation is proposed for the S-NOMA scheme. This method improves energy efficiency for all users by incorporating antenna selection bits from all users, in contrast to traditional NOMA. Simulation results demonstrate the improved performance of S-NOMA with energy-efficient power allocation over conventional NOMA.  
Table 14 summarizes the work on enhancing the energy efficiency of IoT systems using aerial NTN Networks.
## 2 Background and Literature Review
### NLP Models: RNN and LSTM
This evaluates the previous hidden state, denoted by \(h_{t-1}\), and the current input, \(x_{t}\), to allocate a value ranging from 0 to 1 for each part of the cell state \(C_{t-1}\). A value close to 1 suggests that the element should be entirely retained (\"keep it entirely\"), whereas a value near 0 indicates that the element should be fully discarded (\"eliminate it\"). Figure 2 depicts this selective retention and elimination process. The second stage involves determining the new information to be stored in the cell state, which consists of two components. The \"input gate layer\", which is a sigmoid layer, initially identifies the values intended for updating. Subsequently, a Tanh layer produces a vector of new candidate values \(C_{t}\), which may be incorporated into the state. The subsequent step involves merging these two components to formulate a state update. The step is shown in Figure 2 on the right [40]. The third step is to update the previous cell state, \(C_{t-1}\), to the new cell state, \(C_{t}\), as illustrated in Figure 3 on the left. The preceding steps have determined the necessary actions; now, it is time to implement them. We start by multiplying the old state by \(f_{t}\), effectively forgetting the elements we had previously decided to discard. Then, we add \(i_{t}*\tilde{C}_{t}\) to this product. Here, \(\tilde{C}_{t}\) represents the new candidate values, which are scaled by the amount with which we decided to update each state value [40]. To finalize our LSTM model, determining the output is crucial; this output is essentially a selectively filtered version of the cell state. A sigmoid layer evaluates which components of the cell state are pertinent for the output, as depicted in Figure 3 on the right. Following this, the cell state is passed through a Tanh function to normalize its values, and then it is multiplied by the output of the sigmoid gate. This operation ensures that only the relevant components, as identified by the sigmoid layer, are passed to the final output. Figure 2: LSTM: The first step and the second step.
## 3 Results  
Change detection-derived magnitude-frequency relationships were developed for several rock slope study sites located in Alaska, USA. The selected study sites provide an ideal test-bed setting due to their high rates of rockfall activity, accessibility, and the relative absence of vegetation. The rock slopes at the study sites were created as part of construction for two major roadways: Glenn and Parks Highways. The extreme climate of the region (and associated freeze-thaw cycling), steep artificial cuts, and unfavorable structural controls within the rock masses together contribute to high rates of rockfall at the sites. Where employed, rock-fall mitigation measures at the sites consist of roadside catchments that are periodically cleared as part of maintenance operations. The rock slopes themselves are free of directional stabilization measures such as rock bolts or mesh drapery. Close to 20 sites were evaluated in this study; however, for brevity, the discussion below will focus on three rock slopes having different geologic characteristics. Table 2 summarizes general characteristics for each site.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Geographic Coordinates** & **Length** & **Typical Exposed Cliff Height (m)** & **Typical Slope** & \multicolumn{1}{c}{**Geologic Description**} \\ \hline \multirow{2}{*}{A} & 61.805737 \(\mathrm{\SIUnitSymbolMicro N}\) & \multirow{2}{*}{90} & \multirow{2}{*}{8} & \multirow{2}{*}{\(>\)60 \(\mathrm{\SIUnitSymbolDegree}\)} & \multirow{2}{*}{Fine- to medium-grained, moderately weathered grey and tan hard sandstone of the Chickaloon formation} \\  & & & & & \\ \hline \multirow{2}{*}{B} & 61.809820 \(\mathrm{\SIUnitSymbolMicro N}\) & \multirow{2}{*}{110} & \multirow{2}{*}{11} & \multirow{2}{*}{50 \(\mathrm{\SIUnitSymbolDegree}\)} & Soft argillaceous rocks (carbonaceous siltstones) of the Chickaloon formation that have been intruded by hard, well-in-inductance mafic sills (basalt) \\  & & & & & \\ \hline \multirow{2}{*}{C} & 63.756959 \(\mathrm{\SIUnitSymbolMicro N}\) & \multirow{2}{*}{340} & \multirow{2}{*}{170} & \multirow{2}{*}{45 \(\mathrm{\SIUnitSymbolDegree}\)} & Heavily metamorphosed schists and phyllites, intruded by large volcanic dikes at some locations. \\  & & & & & \\ \hline \multirow{2}{*}{B} & 61.809820 \(\mathrm{\SIUnitSymbolMicro N}\) & \multirow{2}{*}{110} & \multirow{2}{*}{11} & Soft argillaceous rocks (carbonaceous siltstones) of the Chickaloon formation that have been intruded by hard, well-inductance mafic sills (basalt) \\  & & & & & \\ \hline \multirow{2}{*}{C} & 63.756959 \(\mathrm{\SIUnitSymbolMicro N}\) & \multirow{2}{*}{340} & \multirow{2}{*}{170} & Heavily metamorphosed schists and phyllites, intruded by large volcanic dikes at some locations. \\  & & & & & Highly weathered, medium strong muscovité-quartz and micaceous-quartz schist. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary characteristics for each site.
# 3D FEA of Infilled RC Framed Structures Protected by Seismic Joints and FRP Jackets
## 2 Brief Presentation of Existing Experimental Results  
### Seismic Protection of Infilled RC Frames with FRP Jackets  
Herein, the experimental results of the real-scale RC frame A2R from the study by [25; 35] were considered for the analytical elaborations. The RC frame had plane dimensions of a 270 cm-long beam with extensions of 30 cm on both sides and a height of 245 cm (245 cm to the level of the diaphragm). The columns had cross-section dimensions of 25 \(\times\) 25 cm, 8\(\Phi\)16 longitudinal continuous rebars (without lap splices) and \(\Phi\)10/100 mm closed stirrups (one peripheral and one rhombic per 100 mm). The columns had two top extensions of 30 cm, as well as a common foundation beam with dimensions of 30 \(\times\) 40 \(\times\) 355 cm. The top beam had cross-section dimensions of 25 \(\times\) 25 cm and was reinforced with 8\(\Phi\)14 longitudinal rebars and \(\Phi\)10/100 mm closed stirrups (peripheral). The dimensions of the RC frame and the detailing of the internal steel reinforcement are presented in Figure 1a and in Table 1. Specimen A2R included an infill wall with special thermal insulating hollow clay units, KEBE OrthoBlocks K100 (KEBE, Greece), with dimensions of 100/240/250 mm and a weight of about 100 kg/m\({}^{2}\) (infill wall with vertical holes in the bricks), built with mortar. The mortar for the building of the walls (with nominal strength class M10) formed 3 mm-thick layers at the head or bed joints as well as at the infill-RC frame interface (no seismic joint). The infill was retrofitted with an external glass grid (of type SikaWrap 350 G Grid, with a real weight of 360 g/m\({}^{2}\)) impregnated with highly deformable PS (Sika) polymer (fiber-reinforced polyurethane jacket, FRPU, Figure 1b). The corresponding properties are cited in Table 2.  
Figure 1: **(a)** Dimensions of the RC frame and detailing of the internal steel reinforcement and (b) specimen A2R with FRPU jacket on the infill surfaces.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **b (mm)** & **h (mm)** & **H (mm)** &
\begin{tabular}{c} **Longitudinal** \\ **Steel Bars** \\ \end{tabular} & **Stirrups** \\ \hline Columns & 250 & 250 & 2450 1 & 8\(\Phi\)16 & \(\Phi\)10/100 \\ Foundation & 300 & 400 & 3550 & 4\(\Phi\)25 + 4\(\Phi\)20 + 2\(\Phi\)16 & \(\Phi\)10/100 \\ beam & 250 & 250 & 3550 & 8\(\Phi\)14 & \(\Phi\)10/100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dimensions of the RC frame and the detailing of the internal steel reinforcement.  
The two columns of the frame received a constant concentric axial load of 375 kN each. The horizontal actuator imposed successive pseudo-static cycles of gradually increased displacements at the top beam. More details on the materials used, the experimental setup, the instrumentation and test results of the A2R can be found in [25; 35].