esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Actor-Critic Methods
### Proximal Policy Optimization (PPO)  
Proximal Policy Optimization (PPO), introduced by [PERSON] et al. [4], is a widely used reinforcement learning algorithm that strikes a balance between sample efficiency and computational simplicity. PPO builds upon the policy gradient methods and introduces a clipping mechanism to ensure stable and monotonic policy updates.  
The goal of PPO is to maximize the expected reward \(J(\pi_{\theta})\) while ensuring that policy updates do not deviate excessively from the current policy. The optimization objective is defined as follows:  
\[L^{\text{CLIP}}(\theta)=\mathbb{E}_{t}[\min(r_{t}(\theta)A_{t},\text{clip}(r_{t} (\theta),1-\epsilon,1+\epsilon)A_{t})], \tag{160}\]  
where \(r_{t}(\theta)\) is the probability ratio given by the following:  
\[r_{t}(\theta)=\frac{\pi_{\theta}(a_{t}|s_{t})}{\pi_{\theta_{\text{old}}}(a_{t} |s_{t})}. \tag{161}\]  
Here, \(A_{t}\) represents the advantage function, \(\epsilon\) is a hyperparameter that determines the clipping range, and \(\text{clip}(\cdot)\) restricts the probability ratio to the interval \([1-\epsilon,1+\epsilon]\).  
The advantage function \(A_{t}\) is computed as follows:  
\[A_{t}=Q^{\pi}(s_{t},a_{t})-V^{\pi}(s_{t}), \tag{162}\]  
where \(Q^{\pi}(s_{t},a_{t})\) is the action-value function, and \(V^{\pi}(s_{t})\) is the state-value function. PPO employs a surrogate objective function to balance exploration and exploitation while avoiding large policy updates, which could destabilize learning.  
In addition to the clipping mechanism, the total loss function in PPO combines the policy loss \(L^{\text{CLIP}}\), the value function loss \(L^{\text{VF}}\), and an entropy term \(L^{\text{ENT}}\) to encourage policy exploration:  
\[L(\theta)=L^{\text{CLIP}}(\theta)-c_{1}L^{\text{VF}}(\theta)+c_{2}L^{\text{ ENT}}(\theta), \tag{163}\]  
where \(L^{\text{VF}}\) is the squared error loss for value function approximation:  
\[L^{\text{VF}}(\theta)=\mathbb{E}_{t}\Big{[}(V_{\theta}(s_{t})-R_{t})^{2} \Big{]}, \tag{164}\]  
and \(L^{\text{ENT}}\) is the entropy loss:  
\[L^{\text{ENT}}(\theta)=\mathbb{E}_{t}\Bigg{[}-\sum_{a}\pi_{\theta}(a|s_{t}) \log\pi_{\theta}(a|s_{t})\Bigg{]}. \tag{165}\]  
The coefficients \(c_{1}\) and \(c_{2}\) control the relative importance of the value loss and entropy loss, respectively. A step-by-step pseudo code of PPO is presented in Algorithm 9.  
Figure 10: Block diagram of the proposed autonomous navigation framework integrating global path planning and deep reinforcement learning (DRL) with landmark generators [40].  
PPO has been applied to various domains, including robotic control, game playing, and autonomous navigation. Its robustness and simplicity make it a preferred choice for continuous and discrete action space problems. By preventing large policy changes through the clipping mechanism, PPO ensures stable learning and improved convergence properties. Some of the recent applications of this algorithm in robotic path planning and navigation are reviewed in the following sections.
## 3 Results and Discussions
### Findings from DOE1, 2 and 3
DOE-7, DOE-8, and DOE-9 are designed for conical=shaped plastic parts. The best results are 12.70 s, 19.65 s, and 15.96 s, which are obtained in cases 7.1, 8.3, and 9.7, respectively. It is notable here that these cooling times are quite shorter than those in the cylindrical case. As such, designers may consider replacing cylindrical bodies, such as bottle caps, bowls, plastic containers, etc., with conical-shaped ones whenever possible. Moreover, using draft angle in the part design can also help reduce the cooling times, too. DOEs 4-9 show significantly lower values of variances compared to the previous DOEs (1, 2, and 3). As we have considered cooling times only, the variances for DOE 4-9 are 6.13 s, 5.13 s, 1.94 s, 4.98 s, 1.16 s, and 1.30 s. This indicates that the datasets are closer to each other in the latter DOEs compared to the previous ones, hence the rationale behind planning DOEs 4-9 on the basis of whether DOEs 1-3 are correct. It should be noted that the channel design configuration that provides the minimum cooling time for a one-mm thick cylindrical plastic part is exactly the same for the one-mm thick conical-shaped part, too. This happens again for the 3.5 mm and six-mm thick parts also, indicating that  
This study provides a basis and guidelines for mold makers to support their design conformal cooling channels for their injection molds. Due to design constraints in injection molds, such as the position of inlet and outlet ports, the gate, runner, ejector pins, etc., the best design solution may not be feasible in practical industrial cases. For this reason, the top three design cases are enlisted in Figure 11 for each size and shape of plastic parts, in order to provide more options to mold designers in the industry. Figure 10: Temperature distribution on plastic parts for optimal design cases in DOE 4, 5, 6, 7, 8 and 9. Figure 9: Cooling time variation for six-mm thick plastic parts (both cylindrical and conical). In addition to the structural stability of the mold and cooling time and production cycle time of the process, the plastic part quality is also important for the plastic injection molding industry. Hence, warping in the final plastic part is a crucial factor to determine the design of the cooling channels in the mold. A detailed study on the effect of warping can be found in the literature [46]. As discussed earlier, the traditional models have straight drilled holes and buffers in some cases to enhance the cooling inside the mold, which cannot reach very close to the mold walls. On the contrary, \"conformal\" cooling channels 'conform' to the shape of the mold wall, and are capable of being very near the wall. As a result, the cooling becomes very uniform with such models, and the warping also minimizes. In this study, warping was not included in the design consideration, as it would elongate the DOEs beyond its scope. In the future, we would incorporate the warping and demonstrate new DOE cases with the study.
## 7 Proportional--Integral--Derivative (PID) Controller Enhanced with Deep Learning (DL)  
### Overview of PID Control  
The proportional--integral--derivative (PID) controller is one of the most widely used control strategies in engineering due to its simplicity and effectiveness in a broad range of applications [20]. It works by adjusting the control input based on three terms: the proportional term, which responds to the current error; the integral term, which accounts for the accumulation of past errors; and the derivative term, which anticipates future errors based on the rate of change [21]. This combination allows the PID controller to maintain stability and performance in systems, making it particularly useful in applications like vibration control, process control, and robotics.  
Figure 16: Voltage Response of PZT Sensor in the Cantilever Beam with Deep Learning Controller and without Controller.  
Figure 15: Voltage Response of PZT Sensor after Vibration Control with Deep Learning Controller (under Random Disturbance).  
However, the effectiveness of a PID controller heavily relies on tuning its parameters to suit the specific dynamics of the system being controlled. Tuning these parameters manually or through traditional methods can be time-consuming and may not result in optimal performance, especially in complex or dynamic environments. This is where deep learning techniques come into play, offering the potential to enhance PID control by automating the tuning process and improving overall system response.
# Dynamic Depth Learning in Stacked AutoEncoders
## 4 Dynamic Depth for Stacked AutoEncoders  
In this paper, we propose a new approach, Dynamic Depth for Stacked AutoEncoders (DDSAE), to dynamically learn the depth of a stacked AutoEncoder. In particular, we intend to learn dynamically the relevance of each layer, \(l\). This is done through an iterative update of the layer relevance weights at the end of each batch \(b\). The cornerstone of the approach is the formulation of a cost function that is apart from the loss function of the network. Similar to spectral dimensional reduction approaches (PCA, MDS, Isomap, etc.) [34], the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Type of AE** & **Technique** & **Supervision** & \begin{tabular}{c} **Width** \\ **Learning** \\ \end{tabular} & \
\begin{tabular}{c} **Depth** \\ **Learning** \\ \end{tabular} &
\begin{tabular}{c} **Hidden Layer’s** \\ **Number** \\ \end{tabular} \\ \hline
[28] & AE & Genetic & Supervised &
\begin{tabular}{c} Pruning and \\ adding \\ \end{tabular} & NA & Single \\ \hline
[26] & Variational AE & Evolutionary & Unsupervised & Adding & Adding & Multiple (Restricted) \\ \hline
[27] & Stacked AE & Genetic & Unsupervised & \
\begin{tabular}{c} Chromosome of \\ fixed length \\ \end{tabular} &
\begin{tabular}{c} Chromosome of \\ fixed length \\ \end{tabular} & Multiple \\ \hline
[18] & Convolutional & \begin{tabular}{c} Genetic \\ Variational AE \\ \end{tabular} & Supervised & \
\begin{tabular}{c} Chromosome of \\ variable length \\ \end{tabular} &
\begin{tabular}{c} Chromosome of \\ variable length \\ \end{tabular} & Multiple \\ \hline
[29] & Denoising AE & \
\begin{tabular}{c} Discriminative \\ and generative \\ \end{tabular} & Supervised &
\begin{tabular}{c} Adding and \\ merging \\ \end{tabular} & NA & Single \\ \hline
[30] & Denoising AE & \
\begin{tabular}{c} Discriminative \\ and generative \\ \end{tabular} & Supervised &
\begin{tabular}{c} Pruning and \\ adding \\ \end{tabular} & NA & Single \\ \hline
[31] & Stacked Sparse & \
\begin{tabular}{c} Generative \\ Denoising AE \\ \end{tabular} &
\begin{tabular}{c} Unsupervised \\ \end{tabular} & Pruning & NA & Multiple \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related works.  
devised cost function is based on faithful preservation of the inner product between the input instance and the mapped features at each layer. More specifically, computing the difference between the inner products of the input instances and the mapped features allows preserving the moment and the argument of pairwise input instances, and thus conserves the structure of the data. Figure 3 illustrates the proposed approach during the training process. The latter is composed of three main steps that are performed iteratively. In fact, a batch b is first trained to learn the mapping of each layer \(l\). Second, the relevance weights with respect to each layer are learned. Finally, layer pruning is performed. In the following, these training steps are described in detail.
# Kalman Filter and Wavelet Cross-Correlation for VHF Broadband Interferometer Lightning Mapping
## 2 Materials and Methods
### Broadband Interferometer (ITF)
#### 2.1.2 Time Difference of Arrival (TDOA)  
To calculate the TDOA and determine the direction of the lightning VHF radiation source, the cross-correlation has been traditionally used for estimating the similarities between two crossed baselines (consisting of three antennae). This process is achieved by using sliding overlapped windows. Besides, the generalized cross-correlation technique is one of the most classical time delay estimation methods.  
Figure 1: The procedure of lightning mapping.  
Figure 2 illustrates the basic geometry of orthogonal baselines for two-dimensional lightning mapping. From Figure 2, the given two baselines (BC and BD) separated by distance \(d\) (15 m in this study), the TDOAs at each baseline can be calculated as,  
\[\tau_{d1}=\frac{\Delta\phi_{1}}{2\pi f} \tag{1}\]  
\[\tau_{d2}=\frac{\Delta\phi_{2}}{2\pi f} \tag{2}\]  
where \(\tau_{d1}\) and \(\tau_{d2}\) are the time differences of the baselines BC and BD, respectively. \(\Delta\phi=2\pi f\tau_{d}\) is the phase difference of a distant signal (with frequency \(f\) in Hz) arriving at two antennae. \(\Delta\phi\) can be found implicitly by performing the cross-correlation procedure.
## 3 Expert Systems for Features Classification  
Expert systems implementation to automate decision-making process are applied to interpret complex data or correlation features with some degree of uncertainty. Although diverse types of expert systems are used to resolve problems with different origins, in the field of IMs fault diagnosis the classification expert systems prevail. Classification expert systems generate discriminant results (decisions made regarding features), precluding the generation of ambiguous results.  
As shown in [7; 8; 19], the support vector machine (SVM) expert system and artificial neural network (ANN) expert system are the most commonly used expert systems to solve features classification problems, due to their high learning coefficient of the problem. With a failure representative features database, obtained via fault diagnostic methods, these expert systems analyze and interpret the data to assess the presence or absence of an IM fault.  
SVM and ANN expert systems have an input interface to the system where fault features are inserted. With these features, the expert system deduces a solution for the fault diagnosis problem. The internal part of the system is then adjusted to perform the interpretation of input data, and an output of one or more results is finally generated. Every solution is related to its respective input data.  
However, in a different way about procedural programs, an expert system is not an execution of a sequence of commands that finally generate a result. Both SVM and ANN systems need to develop a previous training of the expert system to \"learn\" the mechanics of the problem to find the optimal solution. Adjusting the properties and parameters of the expert system to obtain a good training and good failure rates is a highly complex task, which in many cases is carried on by a manual trial and error procedure.
# A Sparsity-Invariant Model via Unifying Depth Prediction and Completion
## 5 Experiment
### Ablation Studies  
We conducted a series of ablation experiments to validate the effectiveness of the dual-branch architecture consisting of prediction and completion branches as well as the multi-scale local plane guidance module and loss functions. Additionally, for better visualization, we also show the line chart in Figure 6 to display the data from the corresponding ablation experiments in Tables 1-3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **500** & **200** & **100** & **50** & **5** & **0** \\ \hline DP branch & - & - & - & - & - & **0.465** \\ \hline DC branch & 0.125 & **0.160** & 0.199 & 0.245 & 0.416 & 0.516 \\ \hline Two branches & **0.122** & 0.162 & **0.197** & **0.239** & **0.393** & 0.470 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance obtained by different architectures (RMSE).  
Figure 5: The visualization results obtained by our full model when the input depth points vary from 500 to 200, 100, 50 and 0 (shown in (**c**–h**)). (**a**): color image, (**b**): the intermediate depth map obtained by the prediction branch, (**i**): the ground truth depth map. The depth map was visualized using the “jet” colormap.  
Figure 6: The visualization of ablation studies results. (**a**) The performance of different architectures, (**b**) The performance of the models with/without MLPGM, (**c**) The performance of different losses
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
## 4 Conclusions  
The aim of this work was to conduct a comprehensive investigation of epoxy mortar's mechanical properties and interface bonding performance using different fillers. Ordinary Portland cement (OPC)-based mortar without epoxy resin was used as a cement mortar. The research results show that epoxy steel slag (ESS) has the best mechanical properties as compared to other fillers. Based on the experimental results, is concluded that the steel slag has the highest compressive strength and tensile strength and lower water absorption  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ESS** & **ES** & **C** & **EM** & **EP** \\ \hline Al & Al & Fe & Si & Fe \\ \hline Si & Si & Si & Al & - \\ \hline K & S & Al & Cl & - \\ \hline Ca & Cl & K & - & - \\ \hline Ti & K & Na & - & - \\ \hline Cr & Ca & S & - & - \\ \hline Mn & Fe & - & - & - \\ \hline Fe & - & - & - & - \\ \hline Weight \% & 39.93 & 58.9 & 11.93 & 75.24 & 40.22 \\ \hline  & 25.56 & 25.78 & 46.96 & 16.33 & 32.72 \\ \hline  & 0.35 & 0.57 & 25.9 & 2.44 & 25.9 \\ \hline  & 0.27 & 0.4 & 1.63 & 0.35 & 0.85 \\ \hline  & 3.49 & 1.62 & 1.32 & 0.53 & 0.31 \\ \hline  & 7.73 & 5.75 & 9.19 & 0.38 & - \\ \hline  & 0.25 & 0.35 & 3.34 & 4.73 & - \\ \hline  & 4.54 & 0.34 & 0.97 & - & - \\ \hline  & 0.73 & 0.39 & 0.25 & - & - \\ \hline  & 0.93 & 4.5 & 0.65 & - & - \\ \hline  & 3.98 & 1.42 & - & - & - \\ \hline  & 12.24 & - & - & - & - \\ \hline Total & 100 & 100 & 100 & 100 & 100 \\ \hline Atomic \% & 57.34 & 70.17 & 19.4 & 83.32 & 55.03 \\ \hline  & 27.55 & 23.06 & 57.34 & 13.58 & 33.6 \\ \hline  & 0.26 & 0.35 & 10.62 & 0.81 & 10.62 \\ \hline  & 0.19 & 0.23 & 1.31 & 0.08 & 0.58 \\ \hline  & 2.23 & 0.86 & 0.46 & 0.25 & 0.18 \\ \hline  & 4.75 & 2.93 & 6.39 & 0.19 & - \\ \hline  & 0.11 & 0.15 & 2.42 & 1.78 & - \\ \hline  & 1.96 & 0.14 & 0.49 & - & - \\ \hline  & 0.26 & 0.14 & 0.21 & - & - \\ \hline  & 0.31 & 1.6 & 0.39 & - & - \\ \hline  & 1.25 & - & - & - & - \\ \hline  & 3.78 & - & - & - & - \\ \hline Total & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._capacity compared to the marble dust, sand, and PVC waste epoxy mortars. It could be confirmed that steel slag waste can be used as a potential filler for epoxy resin mortar to obtain better compressive and tensile strength. The analysis shows that certain peaks in the FTIR spectrum are associated with increased strength and durability. Furthermore, SEM analysis revealed that as the porosity decreased in the epoxy steel slag specimens, the strength increased. In cement mortar (C) and epoxy PVC waste, the porosity was found to be greater and strength was found to be weaker in SEM analysis. The SEM-EDX and XRD analysis revealed the formation of Ca, Mg, and other phases in the epoxy resin-based mortar microstructure, resulting in reduced porosity and enhanced compressive and tensile strengths. In addition, utilization of waste products as fillers in epoxy composites can save primary resources and achieve the required properties of epoxy composites in a much more environmentally friendly way.
# Investigation of the Static Characteristics of a Geogrid-Reinforced Embankment
## 3 Monitoring Data Analysis  
### Analysis of Displacement-Monitoring Data  
Figure 8(a-1-c-2) show the data of continuous daily monitoring at monitoring sites J1-J49 in the first, second, and third months, respectively. After completion of construction, horizontal and vertical displacements at each monitoring site were measured daily for 3 months. The single change value of each monitoring point is the average value of the 30-day monitoring value, the cumulative change value is the cumulative value of the 30-day monitoring value, and the rate of change is the ratio of the single change to the cumulative change value. It can be seen from the first displacement-monitoring dataset that the maximum single change value of horizontal displacement is close to 0.0055 m, and the maximum cumulative change value is about 0.004 m, with a relatively small change rate. The single change value and cumulative change value of different monitoring points differ greatly, which is caused by different compaction degrees in different parts of the same layer. From the vertical displacement, the difference between the maximum single change value and the cumulative change value is small, and the difference between the single change value and the cumulative change value of the two adjacent monitoring points is also small.  
The displacement-monitoring data of the first month can be combined with that of the second and third months. The single change value decreases, and the cumulative change value is larger, the horizontal displacement of the cumulative value is smaller, and the vertical displacement of the total accumulative value increases. The single change value decreases, the cumulative change value increases, the cumulative value of horizontal displacement decreases, and the total cumulative value increases over time. The three change values in the first month are the largest, and the change values in the second months and third months decrease. This is because the horizontal fill of the remaining space is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Name of the Material** & **Normal Stiffness Modulus (kPa)** & **Tangential Stiffness Modulus (kN/m2)** & **Cohesion (kPa)** & **Friction Angle** (c) & **Type** & **Thickness (m)** \\ \hline Geogrid 170 & - & - & - & - & 1D & \(5\times 10^{-3}\) \\ Geogrid 130 & - & - & - & - & 1D & \(5\times 10^{-3}\) \\ Geogrid 90 & - & - & - & - & 1D & \(5\times 10^{-3}\) \\ Interface contact & \(1\times 10^{9}\) & \(3\times 10^{-3}\) & 15 & 30 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geogrid section and contact characteristic parameter table.  
Figure 7: (a) Schematic diagram of filling monitoring points; (b) geogrid monitoring points.  
smaller, as under the action of gravity, the time needed for compaction is short, and the vertical space bigger, with most of the space under gravity populated. It takes a long time for the fill to be compacted again. After a comprehensive analysis, each displacement-monitoring value is within the allowable deformation range.
###### Abstract  
In this paper, an innovative method is put forward for estimating the dynamic mechanical behaviors of reinforced concrete (RC) column members by applying the random forest algorithm. Firstly, the development of dynamic modified coefficient (_DMC_) predictive models and the realization of the proposed method were elaborated. Then, due to the lack of dynamic loading tests on RC column members, a numerical model of RC columns considering the dynamic modification on flexural, shear and bond-slip behaviors was developed on the OpenSees platform, and the model accuracy and the effectiveness were verified with the available test results. Moreover, by comparing the simulated results of the hysteretic curve using numerical models with different complexities, the influences of dynamic modification and the deformation sub-element were investigated. Furthermore, a numerical experiment database was established to obtain the training data for developing the _DMC_ predictive models of critical mechanical behavior parameters, including the yielding bearing capacity, ultimate bearing capacity and displacement ductility. Finally, the results of feature importance for different input parameters were studied, and the model accuracy was evaluated using the test set and available experimental data. It was revealed that the predictive models developed using the random forest algorithm can be employed to reliably estimate the dynamic mechanical behaviors of RC column members.  
dynamic mechanical behavior; reinforced concrete column; random forest algorithm; dynamic modified coefficient; predictive model +
Footnote †: journal: Applied sciences