esa-sceva/satcom-chunk-collection huggingface dataset:
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 5 Results and Discussion
), while the \(x\)-axis corresponds to the bus number. Figure 11: Modified LSTM results: (**a**) AC voltage magnitudes and (**b**) AC voltage angles. \(y\)-axis corresponds to estimated bus voltage in (p.u. ), while the \(x\)-axis corresponds to the bus number. Figure 12: The 25% Pruned LSTM results: (**a**) AC voltage magnitudes and (**b**) AC voltage angles. \(y\)-axis corresponds to estimated bus voltage in (p.u. ), while the \(x\)-axis corresponds to the bus number. Figure 10: Standard LSTM results: (**a**) AC voltage magnitudes and (**b**) AC voltage angles. \(y\)-axis corresponds to estimated bus voltage in (p.u. ), while the \(x\)-axis corresponds to the bus number. Figure 14: The 75% Pruned LSTM results: (**a**) AC voltage magnitudes and (**b**) AC voltage angles. \(y\)-axis corresponds to estimated bus voltage in (p.u. ), while the \(x\)-axis corresponds to the bus number. Figure 15: (**a**) Weighted least square state estimation DC voltage magnitudes and (**b**) standard LSTM DC voltage magnitudes; \(y\)-axis corresponds to estimated bus voltage in (p.u. ), while the \(x\)-axis corresponds to the bus number. Finally, one of the telling signs of the graphs is the profile of the system state. The x-axis represents the bus numbers of the system. It is here we can see that there are two big dipping points in the voltage and angle profiles, both of which are interfaces between an under load tap changer (ULTC) and a three-phase transformer, respectively. Recall that the true system state is acquired from the PSCAD simulations; thus, we can be confident in the models, that is, the behavior is consistent with the documentation for the IEEE 34-bus test system. An example of this sign is that, in between the big voltage drops, we see long line segments which introduce relatively large impedances. Bulk of the spot loads are located at the far ends of the network; thus, the required current flow forces the voltage difference. It is also important to note that the sharp dip of angle measurement is not what it seems to be--mind the scale of the y-axis (\(0^{\circ}\) to \(-4^{\circ}\)).
## 1 Introduction
Recent research has demonstrated that carbon FRP (CFRP) bars may efficiently replace steel reinforcing bars in concrete members. The bonding strength of FRP reinforcing bars (rebars) in concrete is significantly lower than that of standard steel rebars, despite the evident benefits of FRPs [40; 41; 42; 43]. The bond between concrete and reinforcing bars affects the transmission of stress among them, and thus debonding has become one of the most challenging aspects of the investigation of concrete structures [44]. The bond between FRP reinforcing bars and the concrete matrix is complex, and a number of variables can affect the bonding properties of FRP reinforcements to concrete [45; 46; 47; 48; 49]. The geometry and surface characteristics of FRP rebars, concrete compressive strength, confinement pressure, rebar diameter and location in the cast and specimen, embedment length, temperature variations and environmental factors all impact bonding between FRP rebars and concrete [50; 51]. Numerous experimental investigations have been performed to examine the bond strength of FRP rebars in concrete and the effects of factors such as fiber type, surface treatment, bar diameter and temperature on the bond properties of FRP rebars [52; 53; 54; 55; 56]. In addition, these studies demonstrated that the needed embedment lengths of these rebars must be carefully considered. In contrast to steel rebars, there is little information in the literature about the bonding behavior of CFRP rebars in normal and high-strength concrete members, particularly under cyclic loading circumstances [57]. To explore the seismic behavior of RC external beam-column joints with CFRP longitudinal bars in the beam, experimental testing followed by finite element (FE) model analysis were performed in this study. The influence of CFRP bar bond slip on joint hysteretic performance was also examined. An FE model was developed to simulate the behavior of the CFRP-RC joints. The accuracy of the FE simulation was accomplished by accounting for the nonlinear behavior of concrete, the decrease in concrete's compressive strength caused by crack initiation, the confinement given by the internal reinforcement and the bond slip relation between the reinforcement and concrete. For the purpose of accurately simulating the bond strength of the CFRP bars, further pull-out tests were conducted to determine the bond stress versus slip behavior. Finally, further FE analyses were performed to compare the performance of the CFRP-RC joints to conventionally steel-reinforced RC joints and to evaluate the potential of replacing conventional reinforcement with CFRP bars.
# Long-Term Deformations and Mechanical Properties of Fine Recycled Aggregate Earth Concrete
## 1 Introduction
In soils, the physical origin of creep is due to the primary consolidation process, which concerns the change in volume due to the dissipation of free water under applied stress and to the secondary compression process, which is the deformation under constant effective stress mainly due to the viscous behavior of the solid skeleton [21]. The occurrence of creep in soils can be due to the breakdown of interparticle bond, jumping of molecule bonds, sliding among particles, water flow in a double pore system, and structural viscosity [22]. Regarding the creep in concrete (as cement is an earth concrete component), the applied theories such as consolidation, seepage, bond breakage, and others are originally linked with soil and clay. Furthermore, they are all connected and involve water movement. The sum of elastic strain, basic creep in addition to autogenous shrinkage and drying shrinkage, results generally in a total deformation less than that obtained, at simultaneous drying, from a loaded drying specimen. This difference or additional strain is known as drying creep or Picket's effect. The extended microbrestress-solidification theory (XMPS) is allegedly to properly estimate the basic and drying creep in normal concrete. XMPS revealed that the viscosity of creep is significantly reduced by the existence of a water layer between the planer walls of C-S-H. Moreover, as the water layer moves, the effective viscosity of slip between the solid surfaces decreases [23; 24]. In addition to such intrinsic effects, structural effects can contribute to the additional strain which is represented most importantly by the microcracking effect [25]. For complete replacement of NA by RA, normal concrete would generally report 25-100% creep increase [26]. To our knowledge, few papers were dedicated on the creep of earth construction materials or the effect of RS on creep of these materials [27]. Thus, studies investigating the long-term deformations at environments possessing variations in relative humidity are required for earth construction materials. In the following study, to the aim is to assess the complete replacement of NS by RS on the long-term deformations of earth concrete under real exposure curing conditions. Specimens were then subjected to compressive tests in order to evaluate the residual mechanical properties, i.e., compressive strength and Young's modulus. Furthermore, the rupture tests were monitored in parallel with acoustic emission (AE) technique to better understand the fracture behavior [13; 14; 15; 28].
# Computation Offloading and Resource Allocation Based on P-DQN in LEO Satellite Edge Networks
## 3 System Model and Problem Description
### Problem Formulation  
Since multiple tasks may arrive at one region at the same slot, they (offloaded to the same satellite) would compete for computing resources. It is prerequisite to jointly optimize computing offloading and resource allocation per slot, to maximize the average number of satisfied tasks over the long run, i.e.,  
\[\begin{array}{rl}\max\frac{1}{T}\sum_{i=1}^{T}\sum_{k=1}^{K}\sum_{i=1}^{l} \sum_{\bar{t}=1}^{t}\eta^{I,t}_{k,i}\\ \text{s.t.}&\text{C1:}\quad\sum_{i=1}^{I}C^{I,L}_{k,i}\leq\omega^{t,L}_{k}, \forall k,\forall\bar{t},t\\ &\text{C2:}\quad 0\leq P^{I,L}_{k,i}\leq x^{I,t}_{k,i}.\quad P^{I}_{k,i}, \quad\forall k,\forall\bar{t},t\\ &\text{C3:}\quad\sum_{k=1}^{K}\sum_{i=1}^{I}C^{I,M}_{k,i}\leq\omega^{t,M}, \quad\forall\bar{t},t\\ &\text{C4:}\quad 0\leq P^{I,M}_{k,i}\leq x^{I,t}_{k,i}.\quad P^{M}_{\text{max}}, \quad\forall k,\forall\bar{t},t\\ &\text{C5:}\quad 0\leq C^{I,L}_{k,i}\leq x^{I,t}_{k,i}.\quad\omega^{t,L}_{k}, \quad\forall k,\forall\bar{t},t\\ &\text{C6:}\quad 0\leq C^{I,L}_{k,i}\leq x^{I,t}_{k,i}.\quad\omega^{t,M}, \quad\forall k,\forall\bar{t},t\end{array} \tag{11}\]  
where \(\omega^{t,L}_{k}\) and \(\omega^{t,M}\) are, respectively, the remaining amount of computing resources on \(l_{k}\) and \(M\), both at slot \(t\), and \(P^{L}_{k,i,\text{max}}\) and \(P^{M}_{\text{max}}\) are the constant maximum transmitting power budget per terminal and per satellite. \(C_{1}\) denotes that the sum computing resources allocated to offloaded task does not exceed the remaining ones of \(l_{k}\), \(C_{2}\) denotes that the transmit power on \(R^{I}_{k,i}\) does not exceed the budget, \(C_{3}\) specifies that the sum computing resources allocated to tasks (offloaded to \(M\)) are below the remaining capacity, and \(C_{4}\) is analogous to \(C_{2}\), except that the power is from \(l_{k}\) to \(M\). Further, \(C_{5}\) (\(C_{6}\)) indicates that only when \(x^{I,t}_{k,i}=1\) (\(x^{I,t}_{k,i}=1\)) holds, \(C^{I,L}_{k,i}\) (\(C^{I,M}_{k,i}\)) can take positive values; otherwise, \(C^{I,L}_{k,i}=0\) (\(C^{I,M}_{k,i}=0\)) must hold.
## 3 Results
### Impact of Herbicide Treatment on the Construction Materials
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & **G** & **S** & **G-a** & **O (0.5\%)** & **O (1.25\%)** & **O (100\%)** & **O. v.** & **T. z.** & **T. v.** \\ \hline \multirow{2}{*}{\(\Delta\)L*} & \multirow{2}{*}{Schist} & \(-1.13\pm\) & \(\mathbf{-4.03\pm}\) & \(-2.11\pm\) & \(-0.60\pm\) & \(-17.20\pm\) & \(-0.96\pm\) & \(-0.67\pm\) & \(-0.70\pm\) \\  & & 0.58 & \(\mathbf{0.81}\) & 0.35 & 0.87 & \(+0.17\pm 1.00\) & \(\mathbf{5.28}\) & 0.88 & 0.34 & 0.06 \\ \cline{2-10}  & \multirow{2}{*}{Granite} & \(-1.52\pm\) & \(\mathbf{-3.55\pm}\) & \(\mathbf{-3.68\pm}\) & \multirow{2}{*}{n.c.} & \(\mathbf{-2.86\pm}\) & \multirow{2}{*}{n.c.} & \(\mathbf{+1.16\pm 0.58}\) & \(\mathbf{+4.75\pm 1.13}\) & \(+1.50\pm 0.49\) \\  & & & **0.93** & **0.74** & & & & & \\ \hline \multirow{2}{*}{\(\Delta\)a*} & \multirow{2}{*}{Schist} & \(\mathbf{4.26\pm}\) & \(\mathbf{+4.53\pm 0.82}\) & \(\mathbf{+4.02\pm 0.82}\) & \(-0.02\pm\) & \multirow{2}{*}{\(+0.10\pm 0.17\)} & \multirow{2}{*}{\(\mathbf{+7.68\pm 2.42}\)} & \multirow{2}{*}{\(+0.02\pm 0.08\)} & \(-0.02\pm\) & \multirow{2}{*}{\(+0.00\pm 0.12\)} \\  & & **0.63** & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \cline{2-10}  & & & & & & & & & & \\ \hline \multirow{2}{*}{\(\Delta\)b*} & \multirow{2}{*}{Schist} & \(-\mathbf{5.54\pm}\) & \(\mathbf{-4.90\pm}\) & \(\mathbf{-4.34\pm}\) & \(-0.10\pm\) & \multirow{2}{*}{\(+0.52\pm 0.88\)} & \(\mathbf{+12.09\pm}\) & \multirow{2}{*}{\(+0.12\pm 0.04\)} & \(-0.26\pm\) & \(-0.48\pm\) \\  & & **0.91** & **0.93** & **0.86** & & & & & & \\ \hline \multirow{2}{*}{\(\Delta\)b*} & \multirow{2}{*}{Granite} & \(-\mathbf{3.22\pm}\) & \(-1.40\pm\) & \(-1.80\pm\) & \multirow{2}{*}{n.c.} & \(-0.67\pm\) & \multirow{2}{*}{n.c.} & \(-0.67\pm\) & \multirow{2}{*}{\(+0.97\pm 0.23\)} & \(-\mathbf{3.56\pm}\) & \(-1.24\pm\) \\  & & **0.67** & 0.33 & 0.51 & & & & & & \\ \hline \multirow{2}{*}{\(\Delta\)E*\({}_{\text{ab}}\)} & \multirow{2}{*}{Schist} & \(\mathbf{7.01\pm 0.45}\) & \(\mathbf{7.76\pm 0.79}\) & \(\mathbf{6.24\pm 0.81}\) & \(0.61\pm 0.48\) & \(0.56\pm 0.65\) & \(\mathbf{22.53\pm 7.48}\) & \(0.97\pm 0.87\) & \(0.72\pm 0.38\) & \(0.85\pm 0.28\) \\  & & **0.64** & **0.85** & **0.48** & **0.35** & n.c.} & \(\mathbf{3.14\pm 0.32}\) & n.c. & \(1.70\pm 0.84\) & \(\mathbf{6.06\pm 1.51}\) & \(1.92\pm 0.75\) \\ \hline \end{tabular}  
* G: glyphosate, S: sulphosate, G-a: glufosinate–ammonium, O: oxyfluorfen, _O. v._: _Origanum vulgarz_ L, _T. z.: Thymus zygis_ Loefl. ex L, _T. v.: Thymus vulgaris_ L.  
\end{table} 
Table 6: Partial changes in lightness (\(\Delta\)L*), the green–red colour component (\(\Delta\)a*) and the blue–yellow colour component (\(\Delta\)b*), and total colour change (\(\Delta\)E*\({}_{\text{ab}}\)). According to the values obtained and taking a threshold of 3 CIELAB units, perceptible changes (\(\geq\)3 CIELAB units) are indicated in bold. n.c.: not considered.
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 4 Adaptability of the Formulation
### Transition to Quantum Field Equations
#### 4.9.1 Wigner Function and Quantum Dynamics  
The Wigner function encapsulates the quantum state of a system within phase space, offering a quasi-probability distribution that elegantly extends classical dynamics into the quantum domain. The quantum field equation \(\frac{\partial\hat{W}}{\partial t}=[\hat{H}-T\hat{S},\hat{W}]\) governs its evolution towards the equilibrium of the system.
## 2 Experimental Setup and Procedures  
### Cutting Tool and Rock Specimens  
Cutting tools are classified as drag-type or roller-type tools. The conical pick of roadheaders is an example of a drag type, and this type of cutting tool is usually used in a partial-face excavation machine. Moreover, a TBM disc cutter is an example of a roller type tool, and it is usually used in a full-face excavation machine [20]. A pre-cutting machine can be classified as a partial-face machine; therefore, a drag-type tool is used.  
The shape of the cutting tool used in this study was obtained from a previous work [21]. The specifications of the cutting tool are shown in Figure 2a. The length (h), thickness (t), and width (d) were 80, 30, and 60 mm, respectively. The edge angle (\(\theta\)), clearance angle (\(\alpha\)) and rake angle (\(\beta\)) were \(120^{\circ}\), \(10^{\circ}\) and \(5^{\circ}\), respectively. This tool was manufactured using SKD 11 alloy steel with a hardness greater than 60 HRC to reduce tool wear. Moreover, the edge in contact with the rock was filled to a radius of 2 mm. Its actual appearance is shown in Figure 2b.  
It is difficult to obtain rocks of the same composition and strength. To overcome this limitation and for simplicity, rock-like materials such as ceramics and concrete have been used as substitutes in rock experiments [22; 23; 24]. In this study, rock specimens were produced using mortar consisting of sand and cement.  
The rock specimens were manufactured with dimensions 400 mm \(\times\) 400 mm \(\times\) 300 mm. Moreover, because mechanical precutting is effective when the compressive strength of the rock is less than 70 MPa [6], the strength of the rock specimens was designed to be less than 50 MPa. After a curing period of 28 days, uniaxial compression and Brazilian tests were performed to verify that the strengths of the rock specimens reached the target value (Figure 3). The target rock strengths and the physical and mechanical properties obtained from the tests are listed in Table 1.
## 7 Experimental Research
### Multipath Channel and Increased Bit Rate  
Let us consider the results of experimental research for \(R=2.22/T\). Intersymbol interference caused by the FTN signal generation and transmission conditions increases significantly under these conditions. Added to this is intersymbol interference caused by multipath propagation.  
Figure 13 shows the BER performance for a channel with fading in urban areas at \(R=2.22/T\) with the application of an MMSE equalizer. The same figure shows the results of simulation modeling (Section 5) for an AWGN channel for the same bit rate. As follows from the analysis of the curves in this figure, at \(R=2.22/T\) with an MMSE equalizer, the additional energy costs for \(BER=2\times 10^{-2}\) are about 7 dB relative to the costs in a channel with constant parameters. This comes at the cost of fading and an increased bit rate of 2.22 times the Nyquist barrier. Note that classical signals under such signal transmission conditions cannot be used due to the fact that the error probability will be equal to \(BER=0.5\) for any signal-to-noise ratio.
# Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)
## 2 Materials and Methods
### Visualization Improvement
#### 2.6.2 Principal Component Analysis  
It is a statistical technique that transforms data from multivariate radiance bands, which are often highly correlated (i.e., visually and numerically similar). It is an image space transformation designed to eliminate this spectral redundancy. The PCA transform is optimal in the sense that, of all the possible transformations, we choose the particular matrix that diagonalizes the covariance matrix of the original multispectral image.  
PCA is very practical when dealing with several spectral ranges [39], since it allows reducing the dataset to a more manageable number, eliminating redundant information. The Karhuenen-Loeve [40] transformation has been implemented in IDL language.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 7 Discussion
#### 7.1.2 Scalability and Reliability  
LWSN monitoring applications often span large geographical areas, necessitating scalable and reliable communication protocols for robust data transmission. However, techniques optimized for small-scale WSN deployments may encounter scalability and reliability challenges when applied to LWSNs [164]. For instance, geographic routing protocols, which rely on neighbor information and use the shortest distance between nodes for routing decisions, may struggle to maintain network connectivity and packet delivery ratios in linear deployments with sparse node distribution [165]. Additionally, the routing complexity in traditional WSNs contrasts with the straightforward routing paths dictated by the linear structure of LWSNs [40], where nodes have only two possible routing paths (left or right) [28]. Furthermore, techniques like topology control (location-driven and connection-driven) may not be suitable for LWSNs due to a lower spatial correlation in the data acquired by the nodes. Unlike traditional WSNs with random node deployment, thin LWSNs deploy nodes deterministically, resulting in fewer node redundancies [8]. As such, all the nodes, not a subset of the nodes, are needed to maintain coverage and operation.  
While certain techniques effective in traditional WSNs may not be directly applicable to LWSNs, cross-layer approaches, which consider the energy consumption issue on a system-wide level rather than focusing solely on individual components or protocols, may be advantageous [166]. Cross-layer solutions can be utilized to effectively reduce energy expenditure across the entire protocol stack since the parameters and protocols across various layers of the protocol stack in WSNs are jointly optimized [38; 167]. They achieve energy savings by making use of the information residing at different layers of the network protocol stack [168]. However, achieving cross-layer optimization, where parameters and protocols across different layers of the protocol stack are jointly optimized, is particularly challenging in LWSN monitoring applications. The linear topology and long-distance communication requirements introduce complexities that traditional cross-layer optimization techniques may struggle to address [162]. Therefore, innovative cross-layer approaches that account for the specific challenges and operational requirements of linear deployments can pave the way for efficient and sustainable energy management in LWSN monitoring applications.
# Design of a Polarization-Insensitive and Wide-Angle Triple-Band Metamaterial Absorber
## 4 Experiment and Verification
Considering the  
Figure 14: Photograph of the MMA, experimental environment, and schematic diagram of the test principle: **(a)** photograph; **(b)** experimental environment; **(c)** schematic diagram of the test principle. errors caused by the manufacturing process and experimental conditions, this degree of absorption frequency shift is acceptable. Then, the TE wave oblique incidence experiment was carried out, and the incident angle was changed from 0\({}^{\circ}\) to 40\({}^{\circ}\), with a step length of 10\({}^{\circ}\). The corresponding absorption diagram is shown in Figure 16. The absorption frequency charts obtained from the simulation and experiment are shown in Table 5. As can be seen, with the increase in the incident angle, the absorption frequencies obtained from the simulation remain at 4.23 GHz and 7.403 GHz, while the absorption frequencies from the experiment shift from 4.12 GHz and 7.123 GHz to 4.149 GHz and 7.126 GHz, respectively. As shown in Table 5, there is always a deviation of approximately 0.1 GHz and 0.2 GHz between the simulated and tested absorption frequencies, which may be caused by process errors and experimental conditions. It should be noted that the test results indicate that with the increase in the incident angle, the two absorption frequencies do not shift by more than 0.3 GHz, suggesting that the absorption characteristics of this MMA do not degrade significantly with changes in the incident angle. Figure 15: Absorption comparison of the simulated and measured response under normal incidence. \begin{table}
\begin{tabular}{c c c c c c c}  & \multirow{2}{*}{**Simulation**} & \multicolumn{5}{c}{**Experiment**} \\ \cline{3-6}  & & **Theta = 0** & **Theta = 0** & **Theta = 0** & **Theta = 0** & **Theta = 0** \\ \hline First Absorption peak/GHz & 4.230 & 4.120 & 4.109 & 4.109 & 4.116 & 4.149 \\ Second Absorption peak/GHz & 7.403 & 7.123 & 7.121 & 7.124 & 7.121 & 7.126 \\ \end{tabular}
\end{table} 
Table 5: Comparison table of the first two absorption frequencies of simulation and experiment.
## 3 Sink-Based Centralized Transmission Scheduling
### Node Model  
A sensor node consists of a data communication module, a Micro Controller Unit (MCU), and a low-power WuRx. The data communication module and the WuRx both work in the 920 MHz band, which has less interference and higher reachability compared with the 2.4 GHz ISM band. Due to the hardware constraint, there is a wake-up latency [12] during which a node transits from sleep to the active state but cannot transmit or receive data. There is also a delay in detecting a WuC, which is called the WuC detection time [13]. This is the time taken for a WuRx to match its own address with the address in a WuC. Generally, the circuit configuration and performance limit the ability to achieve low energy consumption in the WuRx. Therefore, the WuC detection time is relatively long.  
A sensor node can be in one of the six following states, where transition, idle, receiving, and transmitting are defined as active states.  
* Sleep: The communication module and the MCU are in the sleep mode. A WuRx is active to monitor for WuCs.
* Detecting WuC: A WuRx is receiving a WuC and matching it with its own address.
* Transition: The main part of a sensor node (the MCU and the communication module) transits from sleep to the active state.
* Idle: Both the MCU and the communication module are active, but not involved in transceiving.
* Receiving: The communication module is receiving a data frame or an ACK frame.
* Transmitting: The communication module is transmitting a data frame or an ACK frame.