esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Formulation
### Causal MDP Formulation  
In this section, we introduce causal factors that influence the state variables within the framework. Actions in causal MDPs are depicted as interventions. At each state \(s\), we construct the reward graph \(G_{R}(s)\). The variables representing rewards are \(R\) and states are denoted by \(S\). The agent can modify variables \(X_{I}\), but it is not allowed to intervene on the parent variables of \(R\) (represented as \(Z^{R}=Pa_{R}\)) or the parent variables of \(S\) (representedas \(Z^{S}=Pa_{S}\)). As described in Section 3.3, an intervention applied to an action of size \(m\) is symbolized as \(do(X_{\text{sub}}=x)\), where \(|X_{\text{sub}}|=m\) and \(x=[x_{1},\dots,x_{m}]\).  
At each state \(\mathbf{s}\), the causal graph \(G_{R}(s)\) includes the variables \(X^{R}=[X_{I},Z_{R},R]\). It is important to note that the identity of variables in the causal graphs remains consistent across states, although the underlying distributions may vary. Detailed explanations of these notations are provided in Figure 2. Utilizing this causal knowledge, we define the transition probability function as \(P(z^{\prime}\,|\,z,a,s)\), and the reformulation of the reward function becomes:  
\[R(s,a)=\sum_{z\in Z}R(s,Z=z)P(Z=z|s,a) \tag{16}\]  
Consider the function called \(R(s,Z=z)\), which tells us the expected reward when we have a certain state and parent pair. Now, we will introduce another function called the Q-value function, denoted as \(q_{t}^{\pi}:S\times Z\to R\). This function is all about giving us the total rewards we can expect under a policy \(\pi\), starting from a given state \(s_{t}=s\) and a parent state \(z_{t}=z\) and continuing until the episode ends:  
\[q_{t}^{\pi}(s,z)=R(s,z)+E\Bigg{[}\sum_{\tau=t+1}^{T_{\tau}}R(s_{\tau},\pi(s_{ \tau},\tau))|s_{t}=s,z_{t}=z\Bigg{]} \tag{17}\]  
According to the Bayesian Law of total probability formula, \(Q_{t}^{\pi}(s,a)\) can be expressed as the sum over all possible states \(z\) in \(Z\) of the conditional probability of \(Z\) being \(z\) given the current state \(s\) and action \(a\), denoted by \(P(Z=z|s,a)\), multiplied by the expected reward \(q_{t}^{\pi}(s,z)\). Expressing \(Q_{t}^{\pi}(s,a)\) as \(\sum_{z\in Z}P(Z=z\,|\,s,a)q_{t}^{\pi}(s,z)\) is a consequence of considering an MDP enhanced with dynamic causal graphs \(G^{R}\) and \(G^{S}\). This formulation can be captured by a tuple \(M_{\text{C}}=\langle S,A,P,R,T,G^{R},G^{S}\rangle\).
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 1 Introduction
It may reduce the breast cancer mortality rate and improve prognosis by detecting the subtle signs of early malignancy that can be easily missed by a general radiologist or less experienced breast radiologist [4]. Usually, a small, node-negative tumor under 10 mm in diameter may be effectively treated in nearly 90% of cases. However, this percentage lowers to about 55% when local-regional nodal involvement is present and 18% when distant metastases are present [5]. Several innovative algorithms for digital mammography based on deep learning have been developed due to increased interest in using AI for medical imaging. The employment of AI-based systems as independent readers for mammography interpretation has been shown in several studies to boost radiologist productivity in terms of turnaround time, sensitivity, and specificity [6; 7; 8; 9; 10]. Most radiologists are using the BI-RADS (_Breast Imaging-Reporting and Data System_) lexicon and reporting framework for breast imaging, including mammography, ultrasound, and MRI. The _American College of Radiology_ created it as a quality control and risk assessment tool [11]. Mammograms are more suspicious of cancer if the mass is not circumscribed, has a high density, has a non-conforming shape, or is associated with macrocalcifications such as those found in the amorphous, coarse heterogeneous, fine-pleomorphic, or fine-linear branching forms in a grouped, linear, or segmental pattern [11]. Expert radiologists were never surpassed by the AI algorithms; however, an AI algorithm integrated with the judgment of radiologists in a single-reader screening module improves the overall system's accuracy. Numerous AI algorithms can be used to construct a robust system for detecting and classifying breast cancer. In the following sections, the authors highlight a selection of the commonly used AI schemes and P-DCNN models. Both machine learning and deep learning are used in a variety of medical research areas, such as diagnosis, prognosis, and decision support systems [12; 13].
## Preparation of \"Cauliflower-Like\" ZnO Micron-Sized Particles  
**[PERSON], [PERSON] and [PERSON] ***  
Department of Chemistry, The Institute of Nanotechnology and Advanced Materials, Bar Ilan University, Ramat Gan 52900, Israel; E-Mails: [EMAIL_ADDRESS] (T.G.); [PERSON] (J.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +972-3-531-8861; Fax: +972-3-635-5208.  
_Received: 27 August 2013; in revised form: 4 November 2013 / Accepted: 7 November 2013 / Published: 14 November 2013_  
**Abstract:** Porous polydivinyl benzene (PDVB) microspheres of narrow size distribution were formed by a single-step swelling process of template uniform polystyrene microspheres with divinyl benzene (DVB), followed by polymerization of the DVB within the swollen template microspheres. The PDVB porous particles were then formed by dissolution of the template polystyrene polymer. Unique \"cauliflower-like\" ZnO microparticles were prepared by the entrapping of the ZnO precursor ZnCl\({}_{2}\) in the PDVB porous microspheres under vacuum, followed by calcination of the obtained ZnCl\({}_{2}\)-PDVB microspheres in an air atmosphere. The morphology, crystallinity and fluorescence properties of those ZnO microparticles were characterized. This \"cauliflower-like\" shape ZnO particles is in contrast to a previous study demonstrated the preparation of spherical shaped porous ZnO and C-ZnO microparticles by a similar method, using zinc acetate (ZnAc) as a precursor. Two diverted synthesis mechanisms for those two different ZnO microparticles structures are proposed, based on studies of the distribution of each of the ZnO precursors within the PDVB microspheres.  
**Keywords:** ZnO micrometer-sized particles; PDVB microspheres; fluorescence
## 2 Materials and Methods
### Analytical Methods  
The elemental concentrations of Cu, Ni, As, Cd, Pb, and Zn were determined at the Korea Military Academy using inductively coupled plasma-optical emission spectroscopy (ICP-OES, Perkin Elmer, Shelton, CT, USA). Powdered samples (3 g) were dissolved in a 1:3 mixture of HNO\({}_{3}\) to HCl. A reflux condenser and absorption container were attached to the mixture container and 15 mL of HNO\({}_{3}\) (0.5 M) was added to the absorption container over 16 h. The extraction was carried out by slowly increasing the temperature to 120 \({}^{\circ}\)C while circulating the coolant and heating it for 2 h at 120 \({}^{\circ}\)C. The temperature was then slowly lowered for 1 h to stop the coolant circulation. Subsequently, 10 mL of 0.5/L HNO\({}_{3}\) was poured into the absorption container and allowed to flow. Samples from the mixture container were filtered into a 100 mL volumetric flask using Whatman No. 40 filter paper. The flask was then filled to the marked line using 0.5 M HNO\({}_{3}\). The samples were analyzed using ICP-OES. If the concentration exceeded the calibration range, the samples were diluted and reanalyzed to determine the exact concentration. The average values of three measurements were reported as results. The chemical properties of the soils are shown in Table 1.  
The Pb isotope analyses were carried out on 15 samples at Activation Laboratories Ltd. (ActLabs, Ancaster, ON, Canada) using high resolution inductively coupled plasma mass spectrometry (HR-ICP-MS). The Pb isotope ratios were calibrated using the USGS SRM BCR-2. The \({}^{208}\)Pb/\({}^{204}\)Pb, \({}^{207}\)Pb/\({}^{204}\)Pb, and \({}^{206}\)Pb/\({}^{204}\)Pb ratios are presented in Table 2. The Pb isotope values were calibrated using the common lead NIST standard 981. Figure 1: (**a**) Geological map of the Korean military shooting range in South Korea. (**b**) Specific soil sampling locations in the third (A, C, E) and sixth (B, D, F) shooting lanes. (**c**) Altitude profile of studied shooting range [22]. The mineralogy of selected soil samples was analyzed with X-ray diffraction (XRD). The XRD analyses were conducted at the Korea Institute of Geoscience and Mineral Resources (KIGAM) using an X'Pert MPD diffractometer (Philips, Eindhoven, The Netherlands). The X-ray patterns were obtained from 3\({}^{\circ}\) to 65\({}^{\circ}\) (20 range) at a scan rate of 2\({}^{\circ}\) per minute. The pH of the selected soils was determined following the standard method of APHA [27]. The soil samples were air-dried at room temperature, placed in a beaker
# Acoustic Detection of Vaccine Reactions in Hens for Assessing Anti-Inflammatory Product Efficacy
## 3 Experiments and Results
### Statistical Analysis of Centroid and VocalNum Features  
The data used for the statistical analysis were collected simultaneously in two different locations; therefore, each group of data can be considered independent. Furthermore, according to the Shapiro-Wilk test [43], the data did not come from populations with a normal distribution. Since the normality criterion was not met, [PERSON]'s test was performed to find significant evidence (for \(\alpha\) 0.05) that the variances were different between the two populations [44]. Finally, a \(t\)-test with [PERSON]'s correction (due to differences in variances) was performed, to determine whether or not the mean acoustic features between the groups differed. The statistical analysis was performed in Python [14] using the library Scipy [22] and Pingouin [45].  
Table 2 shows the mean difference between control and treatment by feature, block, interval, and statistical \(p\)-value. The centroid had the highest mean differences for all intervals in the reaction block. The maximum mean difference at night in the reaction block was 465.60 Hz, which also was the strongest significant difference, specifically 2.41 \(\times\) 10\({}^{-14}\). Furthermore, the greatest significant difference in vocalNum was found at night during the reaction block, with a \(p\) value of 1.43 \(\times\) 10\({}^{-6}\). Post-reaction vocalizations were more similar between groups with fewer differences, compared to the pre-reaction interval.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Block** & **Feature** & **Interval** & **\(\mu\) CR** & **\(\mu\) TX** & \(\sigma\) **CR** & \(\sigma\) **TX** \\ \hline \multirow{6}{*}{Pre-Reaction} & \multirow{2}{*}{Centroid} & all & 3630.90 & 3437.92 & 717.71 & 694.24 \\ \cline{3-6}  & & day & 3864.17 & 3669.21 & 647.28 & 637.98 \\ \cline{3-6}  & & night & 2908.63 & 2740.77 & 350.88 & 245.87 \\ \cline{2-6}  & & all & 91.69 & 61.26 & 104.59 & 76.09 \\ \cline{3-6}  & & day & 111.61 & 78.97 & 111.56 & 79.14 \\ \cline{3-6}  & & night & 29.8 & 7.8 & 35.6 & 24.1 \\ \hline \multirow{6}{*}{Reaction} & \multirow{2}{*}{Centroid} & all & 3894.43 & 3599.72 & 690.23 & 665.30 \\ \cline{3-6}  & & day & 4066.42 & 3805.35 & 670.014 & 574.35 \\ \cline{3-6}  & & night & 3286.44 & 2820.83 & 316.96 & 320.46 \\ \cline{3-6}  & & all & 74.59 & 60.18 & 140.46 & 162.27 \\ \cline{3-6}  & & day & 89.48 & 74.80 & 155.35 & 179.60 \\ \cline{3-6}  & & night & 21.97 & 4.84 & 25.83 & 9.96 \\ \hline \multirow{6}{*}{Post-Reaction} & \multirow{2}{*}{Centroid} & all & 3417.58 & 3402.27 & 667.64 & 551.10 \\ \cline{3-6}  & & day & 3645.99 & 3582.16 & 616.31 & 517.52 \\ \cline{3-6}  & & night & 2778.52 & 2862.59 & 275.39 & 151.22 \\ \cline{1-1} \cline{2-6}  & & all & 51.00 & 47.08 & 63.64 & 45.73 \\ \cline{1-1} \cline{2-6}  & & day & 56.04 & 49.37 & 70.48 & 43.83 \\ \cline{1-1} \cline{2-6}  & & night & 36.91 & 40.20 & 35.22 & 50.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and standard deviation of features centroid and vocalNum by interval and block.
## 2 The Design of the Control System
### Fuzzy Logic
#### 2.4.1 Fuzzy Logic Reasoning Framework Design  
In MPC, the model parameters mainly include the prediction time domain \(N_{p}\), the control time domain \(N_{c}\), the error weight matrix \(Q\), and the control weight matrix \(R\) in the objective function equation. The selection of the parameters directly affects the effectiveness of the MPC. To this end, weights that reflect the system's ability to follow the reference trajectory are controlled in the objective function to be optimized to reflect the influence of driving style and road environment. The driving style and road slope have a certain stability in the secular data, and to assist in reflecting the influence of driver behavior and road environment, the actual vehicle speed and acceleration rate of change are added as errors along with the input to the fuzzy logic. Therefore, a four-dimensional fuzzy controller was designed based on driving style, current road slope, current vehicle speed, and acceleration rate of change, as shown in Figure 5.  
The driving style is based on the above recognition results, the slope can be calculated in real time based on the vehicle speed and GPS information, while the acceleration rate of change and the vehicle speed can be obtained based on the vehicle's motion state, and then  
Figure 5: Schematic diagram of fuzzy logic reasoning.  
the error weighting factor \(Q\prime\) can be obtained after fuzzy inference, and the corresponding weighting factor \(Q\) can be obtained after defuzzification. Therefore, the adjustment of the error weight factor is carried out adaptively in real time and the results are fed into the model predictive control model in real time.
# Deep Transfer Learning-Based Fault Diagnosis Using Wavelet Transform for Limited Data
###### Abstract  
Although various deep learning techniques have been proposed to diagnose industrial faults, it is still challenging to obtain sufficient training samples to build the fault diagnosis model in practice. This paper presents a framework that combines wavelet transformation and transfer learning (TL) for fault diagnosis with limited target samples. The wavelet transform converts a time-series sample to a time-frequency representative image based on the extracted hidden time and frequency features of various faults. On the other hand, the TL technique leverages the existing neural networks, called GoogLeNet, which were trained using a sufficient source data set for different target tasks. Since the data distributions between the source and the target domains are considerably different in industrial practice, we partially retrain the pre-trained model of the source domain using intermediate samples that are conceptually related to the target domain. We use a reciprocating pump model to generate various combinations of faults with different severity levels and evaluate the effectiveness of the proposed method. The results show that the proposed method provides higher diagnostic accuracy than the support vector machine and the convolutional neural network under wide variations in the training data size and the fault severity. In particular, we show that the severity level of the fault condition heavily affects the diagnostic performance.  
transfer learning; wavelet; limited data; fault detection; fault diagnosis; machinery fault sciences
# Predictability of ClinCheck in Overbite Correction with Aligners: A Systematic Review
## 5 Conclusions  
* The correction of deep bite predicted by the ClinCheck software overestimates the correction achieved clinically.
* There is a concordance between the ClinCheck prediction and the actual outcomes for open bite correction.
* The application of posterior occlusal bite blocks and traditional attachments appears to exert minimal influence on the success of open bite closure.
* The lack of standardized protocols for staging and biomechanics likely contributes to the variability observed in deep bite treatment outcomes. Overengineering the final set up and incorporating planned overcorrections may be necessary to reduce the discrepancy between the virtual plans and the clinical results.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
###### Abstract  
This paper considers a hybrid relay network consisting of the source, the amplify-and-forward (AF) relay, the decode-and-forward (DF) relay, and the destination. In hybrid three-hop relay systems, the transmitted signal from source can be received at the destination after processing the signals through two relays. If the first relay amplifies and forwards the received signal, and the second relay decodes and forwards the received signal, the system model is considered to be an AF-DF relay system. The reverse case is considered for the DF-AF relay system. The AF-DF and DF-AF relay systems have different error rates and achievable throughput with respect to the channel conditions between two nodes. We propose optimal power allocation schemes for two different relays in order to maximize the achievable rate under a sum relay power constraint for given channel gains and transmit power from the source. By solving the optimization problem to maximize the achievable rate for each relay network, the transmit power values in closed form are derived. When the channel gains are the same, the optimal power allocation scheme for the AF-DF relay network proves that greater power should be allocated at the first relay to maximize the achievable rate. In the case of the DF-AF relay network, we derive an optimal power allocation scheme for the four possible cases. Under the same signal-to-noise ratio (SNR) condition, at the first hop we show that the achievable rate of the AF-DF relay network is greater than that of the DF-AF relay network when the channel gain between two relays is greater than that between the second relay and destination. Simulation results show that the proposed power allocation schemes provide a higher achievable rate than the equal power allocation scheme and the grid search schemes.  
**Keywords:** power allocation; hybrid relay network; amplify-and-forward (AF); decode-and-forward (DF); achievable rate +
Footnote †: journal: Applied Soft 2018, 8, 690; doi:10.3390/app8050690
## 4 Experiment and Analysis
### Analysis of Experiments
#### 4.2.4 Output Parameter Evaluation  
The steering angle was estimated from the roll angle and roll rate using the MRA Equation (19). The mean values of the regression coefficients \(a_{1}\) and \(a_{2}\) are shown in Figure 12, which can be defined by Equations (20) and (21), respectively as a function of motorcycle speed \(v\).  
\[a_{1}=0.011v^{3}-0.27v^{2}+2.5v-5 \tag{20}\]  
\[a_{2}=-0.044v^{2}+0.92v-6.4 \tag{21}\]  
The steering angle estimated from the regression model is compared with its measured data from the experiments as shown in Figure 13. Figure 13a,b show that the estimated steering angle matches closely with the measured steering angle at speeds 3 and 5 km/h respectively. These results validate that the regression model can be used for estimating the steering angle, using the roll angle and roll rate accurately.  
Figure 12: Regression coefficients for roll angle (\(a_{1}\)) and roll rate (\(a_{2}\)).  
Figure 13: Validation of estimated steering angle from the experimental measurements.
## 2 Related Work  
### Intent-Based Networking (IBN)  
Intent-based networking (IBN) has emerged as an innovative technology for automation and orchestration of networks that may change the current network architectures and technologies. It is an intelligent networking system that can convert, deploy, and configure the network resources according to operator intentions automatically. This system can overcome and control abnormal events or failures. Moreover, it can also monitor the network resources continuously and collects the statistics for performing life cycle management [7; 8; 9; 10]. Many industrial organizations have developed IBN-based systems but Cisco, Huawei, and IETF (Internet Engineering Task Force) are the main contributors to the development of intelligent IBN systems. The road-map of the IBN-based systems in the literature are presented in Figure 2.  
Figure 2: IBN system development road-map.  
Cisco introduced the first IBN system in 2017; it is a closed-loop system and consists of three main modules: translation, activation, and assurance [11]. It facilitates the users of the network to input higher-level configurations in the form of intents, and the system can automatically translate those configurations into a system understandable format. Finally, the translated configurations are used for the deployment of resources. It is an intelligent and self-organizing system where the networks are planned, designed, and operated automatically. The translation module of the IBN system provides an easy way for network operators to express their intent in a flexible and declarative way. However, the network operator can consider the business objectives rather than thinking about how to configure the network resources to achieve the desired goal. On the other hand, the activation module of the IBN system can convert the captured intents into network policies that can be further used for deploying the network resources. Furthermore, with the help of domain orchestrators, these policies can be deployed into virtual and physical infrastructure. The assurance module is for monitoring the deployed resources, and in case of failure, it performs automatic recovery.  
After the CISCO, Huawei [12] introduced a similar kind of system named the Intent-Driven Network (IDN). It is an autonomous driving network system which aims to develop a future generation network with lifecycle management support, intelligent operation and management, closed-loop, multi-service support, E2E slicing support, and automatic failure and recovery. It can efficiently deploy business intents into physical networks and facilitate users with better QoS and QoE. The IDN network has a smart brain layer named Network Cloud Engine (NCE) that performs intelligent network control and management. It performs the operations related to the translation of intents and activation dynamically. It can also monitor the health of the virtual and physical resources of the network in real-time. It is an intelligent system due to AI and big data analytics modules. Furthermore, the SDN-based controllers are also integrated with the NCE platform for better control.
## 3 Data and Methodology
### Methodology
#### 3.2.4 Ensembles: RF, AdaBoost, and XGBoost  
Predictions from previously developed individual base estimators can be integrated using ensemble strategies to improve robustness and generalization ability; they can often deliver better results compared to a single estimator. Even when the individual models in the ensembles are fairly simple, the power of ensembling can lead to the creation of strong ensemble models.