esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Methodology
#### 2.5.3 Compressive Strength  
The compressive strength of the concrete samples was determined to assess the mechanical performance of the mixtures. This assessment was conducted at two specific time points, 7 and 28 days, which are common intervals for evaluating the development of concrete strength. The testing procedure followed the guidelines outlined in ASTM C39 [38]. Cylindrical specimens with a standard diameter of 100 mm and a height-to-diameter ratio of approximately 2:1 were prepared from each concrete mixture. These dimensions conformed to the ASTM C39 requirements for cylindrical specimens. Then, fresh concrete was placed into cylindrical molds in layers, and each layer was compacted using a mechanical tamper to ensure proper consolidation and the elimination of air voids. After casting, the specimens were covered with a plastic sheet to prevent moisture loss and maintain a suitable curing environment. The curing conditions were controlled to meet the ASTM recommendations.  
A Tecnotest hydraulic press (model KE 300/A, serial number 2907) manufactured in Modena, Italy, with a maximum compression capacity of 3000 kN, 100 kN in flexion, and equipped with a loading system capable of applying a step load, was employed for conducting the compressive strength tests. During the testing process, a step load was applied to the cylindrical specimens at a constant rate. The rate of loading was set at  
0.25 MPa/s to maintain a consistent testing procedure (refer to Figure 4a). This loading rate was in accordance with ASTM C39 and allowed for the gradual application of pressure.  
Each cylindrical specimen was placed between the platens of the hydraulic press, ensuring that the load was evenly distributed over the entire surface of the specimen. This prevented any localized stress concentrations that could affect the results. As the hydraulic press applied the step load, the corresponding compressive forces and deformation were continuously monitored and recorded throughout the testing duration. Three specimens from each concrete mixture were tested at both the 7-day and 28-day intervals. The average compressive strength values obtained from these replicates were used for analysis.  
The compressive strength values were obtained directly from the hydraulic press, where each value was calculated by dividing the maximum load applied to the specimen by the cross-sectional area of the cylinder. Finally, the recorded compressive strength values for each concrete mixture were analyzed to assess the overall strength characteristics at 7 and 28 days of age.
## 2 Materials and Methods
### MSACN Deformation  
In this part, the depth of the network is categorized based on both relative depth and absolute depth. The absolute network depth generally refers to all layers of the network. Relative depth primarily denotes the dimensions of pooling layers in the network. As the characteristic resolution in satellite imagery is often associated with pooling layers, especially for medium-resolution remote sensing images, an excessively deep network with numerous pooling layers not only results in information resolution loss but also causes irreversible loss of resolution, leading to sub-optimal cloud extraction performance. Additionally, training such deep networks becomes challenging. Thus, considering the strong correlation between the depth of the network structure and the resolution characteristics of input images, we undertake a local deformation of the refined method: MSACN-small. We divide the entire network into four segments, using pooling layers and upsampling layers as boundaries for both encoding and decoding parts. In the context of medium-resolution remote sensing images, where pruning the network to reduce its scale is necessary, we conduct experiments and analyses with a downsized model, denoted as MSACN-small, feathering three pooling layers. This compact variant participates in the comparative analysis of our proposed method.
## 4 Discussion  
According to the previous research conclusions, the addition of an appropriate amount of geopolymer improves the mechanical properties and frost resistance of the base mixture. In order to compare the research in this study with the existing literature research and comprehensively discuss the influence of the geopolymer on the mechanical properties \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{3}{c}{**Correlation Degree**} \\ \cline{2-4}  & **Harmful Pores** & **More Harmful Pores** & **Total Porosity** \\ \hline UCS & 0.67 & 0.58 & 0.75 \\ ITS & 0.64 & 0.91 & 0.74 \\ CRM & 0.66 & 0.47 & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results of grey relation analysis between strength loss and pore size distribution of 20% geopolymer mixture after freeze–thaw cycle. and frost resistance of road construction materials, the relevant studies containing similar research were selected, as shown in Table 14.  
The previous research shows that although the types of geopolymer and the types of stabilized materials are different, in general, the addition of an appropriate amount of geopolymer can ensure the road performance of geopolymer-stabilized road materials, including mechanical properties, frost resistance and so on. In addition, due to the related characteristics of geopolymer materials, the geopolymer-stabilized mixture has better frost resistance than the cement-stabilized mixture [23], which is basically consistent with the previous research in Table 14.  
For geopolymer-stabilized materials, the mechanical strength mainly comes from a large number of hydration products generated by geopolymer raw materials after alkali excitation, for example, C-(A)-S-H gel, N-A-S-H gel, AFt, geopolymer gel, etc. On the one hand, a large number of hydration products covers the surface of the stabilized material to form a solid matrix, which also makes the loose particles interconnected and aggregated into one, so that the mixture forms strength. On the other hand, the hydration products generated by the geopolymer raw materials after alkali excitation effectively fill the pores and other structures inside the stabilized material, which enhances the compactness of the stabilized material and makes the mixture form strength [5; 8; 12; 15]. In addition, it is worth noting that the dosage of geopolymer generally has an optimal value. The reason is that when the dosage is below the optimal value, the amount of hydration products is lower and cannot cover the stabilized material. When the dosage is above the optimal value, it
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 5 Performance Test
### Effect of the Damping Coefficient  
Figure 8 shows the influence of pitch amplitude on energy conversion efficiency under different damping coefficients. The energy conversion efficiency is obviously affected by the damping coefficient. As shown in Equation (16), the energy conversion efficiency generally decreases as \(\varsigma_{y}\) increases. The best performance is obtained with minimum damping (\(\varsigma_{y}=30\) Nm\(\cdot\)s/rad) in most cases. The conversion efficiency obtained by the minimum damping coefficient is at most 1.99% higher than the efficiency corresponding to the medium damping coefficient (\(\varsigma_{y}=35\) Nm\(\cdot\)s/rad). The relative maximum damping coefficient (\(\varsigma_{y}=40\) Nm\(\cdot\)s/rad) is increased by at most 4.0%. It is noteworthy that not all maximum efficiency points appear at the minimum damping coefficient. As the pitch amplitude increases and the wave period decreases, the value of the damping coefficient at the point of maximum efficiency gradually increases. From a statistical point of view, the gap decreases as the pitch amplitude increases and the wave period decreases.  
Figure 8: Influence of damping coefficient and pitch amplitude: (**a**) cy = 30; (**b**) cy = 35; (**c**) cy = 40.  
On the other hand, the graphs show that for a given damping coefficient and wave period, the larger the wave pitch amplitude, the lower the conversion efficiency. Besides, for the same damping coefficient and pitch amplitude, the conversion efficiency decreases with the increase in the wave period, which is especially obvious at high wave periods. In the case of small damping, the attenuation rate of the conversion efficiency with increasing pitch amplitude is greater than that with large damping. For the wave state of T = 14 s and A = 5\({}^{\circ}\), the conversion efficiency can be improved 4% by adjusting the damping coefficient, so an appropriate damping coefficient should be selected. This improvement is more important than changing the wave period alone (3.7% with c\({}_{\text{y}}\) = 40 Nm\(\cdot\)s/ rad and A = 20\({}^{\circ}\)) or the pitch amplitude alone (3.1% with c\({}_{\text{y}}\) = 30 Nm\(\cdot\)s/ rad and T = 14 s). These results indicate that the damping coefficient should be the focus of attention when optimizing the GWEC device.  
The damping coefficient is quite important due to the analysis of dynamic characteristics in Figure 9. When the damping coefficient is larger than the rated value, the output power and conversion efficiency will decrease rapidly. For damping coefficients smaller than the rated value, the precession angle will be too large, which will cause the system to be uncontrolled. The platform counter torque will decrease with the increase in the damping coefficient. From the perspective of PTO, when the damping coefficient is the optimal value, the torque on the precession shaft also reaches the maximum, which is not convenient to realize from the perspective of the mechanical structure.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
###### Abstract  
Some applications, including disaster monitoring and recovery networks, use low-power wide-area networks (LPWAN). LPWAN sensors capture data bits and transmit them to public carrier networks (e.g., cellular networks) via dedicated gateways. One of the challenges encountered in disaster management scenarios revolves around the carry/forward sensed data and geographical location information dissemination to the disaster relief operatives (disaster relief agency; DRA) to identify, characterise, and prioritiise the affected areas. There are network topology options to reach its destination, including cellular, circuit switched, and peer-to-peer networks. In the context of natural disaster prediction, it is vital to access geographical location data as well as the timestamp. This paper proposes the usage of Pseudo A Number (PAN), that is, the calling party address, which is used by every network to include the location information instead of the actual calling party address of the gateway in LPWAN. This PAN information can be further analysed by the DRA to identify the affected areas and predict the complications of the disaster impacts in addition to the past history of damages. This paper aims to propose a solution that can predict disaster proceedings based on propagation and the velocity of impact using vector calculation of the location data and the timestamp, which are transmitted by sensors through the PAN of the gateway in LPWAN.  
Keywords:Pseudo A Number (PAN); lower power wide area network (LPWAN); disaster recovery system; machine to machine (M2M) interface; disaster recovery assistance agency (DRA); calling party address +
Footnote †: journal: _technologies_
# Stamping Tool Conditions Diagnosis: A Deep Metric Learning Approach
## 2 Materials and Methods  
### Metric Learning and Deep Metric Learning (DML)  
Metric learning is a type of machine learning based on the distances between objects, where the distance itself represents a similarity measurement. Suppose a given dataset \(X\) with \(X\ \in\ \mathbb{R}^{N\times D}\), where \(x_{i}\ \in\ \mathbb{R}^{N}\) is the \(i\)th datum and \(D\) is the total number of data samples; next, set \(X^{\prime}\) as the mapping of \(X\) with the distance function over \(d_{M}:X^{\prime}\times X^{\prime}\in\mathbb{R}^{m}\). This metric or distance function must satisfy the following axioms:  
1. Negativity: \(d_{M}\big{(}x_{i},x_{j}\big{)}\ \geq 0\),
2. Symmetry: \(d_{M}\big{(}x_{i},x_{j}\big{)}=d\big{(}x_{j},x_{i}\big{)}\),
3. Triangular inequality: \(d_{M}\big{(}x_{i},x_{j}\big{)}\ \leq\ d\big{(}x_{i},x_{k}\big{)}+d\big{(}x_{j},x_{k} \big{)}\),
4. Identity of indiscernible: \(d_{M}\big{(}x_{i},x_{j}\big{)}\ iff\ i=j\).  
A pair \((X^{\prime},d_{M})\) in which \(X^{\prime}\) is a set and \(d\) is a metric in the metric space. Although the metric obeys the five aforementioned axioms, metric learning ignores the final axiom (triangular inequality). Therefore, the properties of \((X^{\prime},d_{M})\) change from metric space to pseudometric space. Pseudometrics are sometimes referred to as metrics in metric learning. The distance metric itself is calculated as follows:  
\[d_{M}=\sqrt{\big{(}x_{i}-x_{j}\big{)}^{T}M\big{(}x_{i}-x_{j}\big{)}} \tag{1}\]  
where \(M\) must be a symmetrical and positive semidefinite matrix; if \(M=W^{T}W\), then function (1) can be written as function (2):  
\[d_{M}=\sqrt{\big{(}x_{i}-x_{j}\big{)}^{T}W^{T}W\big{(}x_{i}-x_{j}\big{)}} \tag{2}\]  
\[d_{M}{}^{2}=\|Wx_{i}-Wx_{j}\|_{2}^{2} \tag{3}\]  
\[d_{M}=\|x_{i}^{\prime}-x_{j}^{\prime}\|_{2} \tag{4}\]  
The distance \(d_{s}\) is the Eucledian distance of the feature representation vectors \(x_{i}^{\prime}=Wx_{i}\) and \(Wx_{j}\). The main objective of metric learning is training through the mapping function \(f\); if \(f:X\rightarrow\mathbb{R}^{n}\) is substituted into function (4), then it becomes function (5):  
\[d_{M}=\|f(x_{i})-f\big{(}x_{j}\big{)}\|_{2} \tag{5}\]  
Training \(f\) under various loss functions and other boundaries thus becomes the clear purpose of metric learning. Because deep learning models can be trained to learn linear or nonlinear problems, they can be used to map data points into feature space, and the weight and biases in deep learning architectures can be trained using various loss functions incorporating distance metric (5). The two most common architectures used for DML applications are Siamese neural networks and triplet networks.
## 2 Related Work
### Test-Time Augmentation  
Test-time Augmentation (TTA) is gaining attention as an effective technique for improving model prediction performance. A selective test-time augmentation technique was proposed in one study that significantly improved computational efficiency by selectively augmenting samples with high uncertainty [26]. This approach estimated prediction uncertainty based on Bayesian inference and introduced an adaptive strategy that applies various augmentations only to samples with high uncertainty. This resulted in a 30% improvement in processing speed compared to existing methods, demonstrating its effectiveness even in resource-constrained environments.  
An optimized TTA method using Bayesian model averaging was proposed in a study [27]. By combining ensemble methods and uncertainty estimation, this approach achieved more reliable prediction results, showing an average performance improvement of 5.2% across various classification tasks. Notably, more reliable ensemble results than simple averaging were obtained by integrating prediction results for each augmented sample within a Bayesian framework. Additionally, a meta-learning algorithm that dynamically adjusts augmentation policies was introduced to improve adaptability during test time.  
A new TTA method utilizing generative models instead of manually designed transformations was proposed in a prior study [28]. This approach improved segmentation performance by automatically generating various transformations of input images, achieving a 12% accuracy improvement compared to existing manual design approaches. The proposed method used conditional generative models to create diverse transformations while preserving the semantic characteristics of input images. Notably, it ensured that augmented samples generated through adversarial learning reflected the actual data distribution well and introduced regularization terms to maintain consistency in the feature space.
# Deep Q-Network Approach for Train Timetable Rescheduling Based on Alternative Graph
## 1 Introduction
### Preliminary: Reinforcement Learning  
Many multi-stage or sequential decision problems can be formulated as interactions between the environment and agent (decision-maker). In these interactions, the information being used by the agent to make a decision is referred to as the state, and the agent receives a reward for his/her action (or decision) at a particular state. At the next decision time, the agent will encounter new information, which might depend on the previous state and action he/she chooses.  
Generally, the next state of the environment depends on the entire history of pairs of states and actions. In the Markov decision process (MDP), however, it is assumed that the next state depends only on the current state and action. For a given MDP problem, Bellman optimality conditions [20] are usually derived, and the dynamic programming relying on these optimality conditions can be applied to find an optimal solution. Dynamic programming, however, is an enumerative solution approach that relies on searching for solutions in the entire solution space, such that it suffers from the so-called \"curse of dimensionality\" problem [21].  
Reinforcement learning is one way to mitigate the computational limitations of an enumerative search of the solution space, by relaxing the goal of finding any exact optimal solutions. To do this, instead of searching all possible pairs of states and actions, a relatively small samples of states and actions is simulated and an optimal or near-optimal solution for the problem with respect to these samples is deduced. One of the central concepts in reinforcement learning is the optimal value function of a state, or pair of states, and an action, which specifies the long-term cumulative reward achievable when the agent follows (possibly unknown) an optimal decision after the given state or the given pair of states and action. For a given sample, we can estimate the optimal value function, and the estimated value function can be used directly or indirectly to specify the decisions the agent makes. In the recent development of reinforcement learning, deep neural networks [22] have been used for estimating the value function or a function, i.e., policy, which outputs an optimal action for a given state as an input, which achieves state-of-the-art results for many benchmark problems [23,24].  
More background information on reinforcement learning can be found in [20,21].
## 5 Surface Current, Electric Field, and Magnetic Field Analysis  
The EM characteristics of metamaterials rely on different forces and fields, which are depended upon to produce charge. [PERSON]'s curl equations are the best tools to explain the generated electric and magnetic fields [22].  
\[\
abla\times H=J+\frac{\partial D}{\partial t} \tag{4}\]  
\[\
abla\times E=\frac{\partial H}{\partial t} \tag{5}\]  
where vector operator, \(\
abla=[\frac{\partial x}{\partial t},\frac{\partial y}{\partial t},\frac{ \partial z}{\partial t}]\).  
Equations (4) and (5) are not sufficient to describe the relationship between matter, the electric field, and the magnetic field. However, these limitations were solved using the following equations.  
\[\varepsilon\left(t\right)=\frac{D(t)}{E(t)} \tag{6}\]  
\[\mu\left(t\right)=\frac{B(t)}{H(t)} \tag{7}\]  
The equations presented above were used to assess the surface current, magnetic, and electric fields functions. The current distribution at three different resonance frequencies was 2.89 GHz, 9.42 GHz, and 15.16 GHz (Figure 8). The current flow differs with the change of frequency. Based on Figure 9a-c, the outside square ring of the unit cell was determined to have been strongly dominated by the surface current. In other words, it meant that the outer ring belonged to the resonance at 2.89 GHz. Moreover, it was notable that the hexagonal ring was also subsidised as a major part of the current. The magnetic field distribution for the proposed unit cell at various resonance frequencies (2.89 GHz, 9.42 GHz, and 15.16 GHz) is depicted in Figure 10a-c.  
Figure 7: S21 for changing field direction.  
According to the [PERSON]'s law, a magnetic field is produced due to the motion of charged particles or electrical charges [23]. The flow of higher electrical charges increased the strength of the magnetic field that was observable from the H field (Figure 10). The magnetic field centered in a wire at a distance (r) follows the equation (using the Biot-Savart law), \(B=\frac{\mu_{0}l}{2\pi r}\), where \(\mu_{0}\) is the permeability of free space. A similar outcome was also yielded in the E field (Figure 9). Compared to the electric field pattern presented in Figure 5 with the H field presented in Figure 6, it is evident that fewer changes in the H field at a particular point also measures low electric field intensity. Furthermore, the electric field intensity at the point of splits was higher since the split in the resonator ring gap formed a capacitor.  
Figure 8: Surface current: (**a**) 2.89 GHz, (**b**) 9.42 GHz, and (**c**) 15.16 GHz.  
Figure 10: Magnetic field distribution (**a**) 2.89 GHz, (**b**) 9.42 GHz, and (**c**) 15.16 GHz.
## 2 FSM Control System
### Tip-Tilt Controller of FSM Control System  
Proportional-integral (PI) controllers are widely used in real control systems as the tip-tilt controllers of FSM control systems. Moreover, tip-tilt controllers are designed to achieve the designed gain margin, phase margin, crossover frequency, and loop gain. The  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specification** & **Value** \\ \hline Resonance frequency & 397.1 Hz \\
5 Hz sensitivity & 166.7 urad/V \\ Gain of voltage amplifier & 10 V/V \\ Gain of sensor amplifier & 0.006 V/urad \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identified dynamic characteristics of real plant.  
Figure 1: Frequency response of real plant and modeled plant.  
Figure 2: Configuration of fast steering mirror (FSM) platform: (**a**) Scalexio AutoBox (dSPACE) and (**b**) tip-tilt actuating platform (PI Ceramic GmbH).  
transfer functions for continuous time and discrete time for the PI controller are denoted by \(C(s)\) and \(C(z)\), respectively, and expressed as follows:  
\[C(s)=k_{p}+\frac{k_{i}}{s}, \tag{2}\]  
\[C(z)=k_{p}+\frac{k_{i}T_{s}Z}{z-1}, \tag{3}\]  
Here, \(k_{p}\) and \(k_{i}\) refer to proportional gain and integral gain, respectively, and the control gain should be chosen based on a balance of stability and performance. In this paper, the tunable parameters (\(k_{p}\) and \(k_{i}\)) of the PI controller are manually designed in the experiment and set to be \(k_{p}\) = 0.75 and \(k_{i}\) = 950. Figure 3 depicts the open-loop transfer function of the PI controller designed for the FSM control system in this study. The margins of gain and phase are 6.03 dB and 47.1\({}^{\circ}\), respectively. Also, the crossover frequency and loop gain are 111 Hz and over 40 dB, respectively. Moreover, the disturbance-attenuation performance of the designed PI controller for the FSM control system is illustrated in Figure 4.