esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These algorithms aim to minimize control errors and enhance the performance of CFDL and PFDL-based MFAC controllers across various applications. MFC, on the other hand, relies on intelligent PID (iPID) laws, which mimic classical PID behavior but operate without a plant model, using only system data. The iP and iPID algorithms, as described in [8], exhibit behavior akin to PI and PID controllers. Given the suitability of DDC methods for nonlinear systems, this paper applies the proposed algorithms to three such systems: a BLDC motor with nonlinear control elements, a non-minimum phase nonlinear system, and a nonlinear system with time-varying parameters. The analysis aims to evaluate the effectiveness of gradient and non-gradient approaches in these contexts. Since DDC-type approaches are suitable for nonlinear systems, this paper proposes the application of the algorithms for obtaining the initial values for CFDL and PFDL, respectively the analysis between the gradient and non-gradient based methods, using three such systems. The first is represented by a BLDC motor, which has a control loop with nonlinear elements, the second is represented by a non-minimum phase nonlinear system, and the third is a nonlinear system with a time-varying parameter. The main contributions of this paper are as follows. * Determining the initial value of PPD for the MFAC-CFDL law by obtaining the starting value for the time-varying parameter used in the control law equation. * Determining the initial value of PG for the MFAC-FFDL law by obtaining the starting values for the time-varying parameter used in the control law equation. * Testing and validation of algorithms for initial values of time-varying parameters in relation to preliminary laws that provide stable responses of control systems
* Comparative analysis of the performance of systems controlled by gradient and non-gradient based methods applied to nonlinear systems. The structure of the paper comprises a presentation of the two Model Free algorithms which are described in Section 2, followed by the presentation of the nonlinear systems considered to be controlled, in Section 3. In Section 4, the algorithms for determining the optimized values for the PPD and PG are presented, with the results obtained by applying them to the plants mentioned in Section 3. In the same section, the comparative results of the control using the analyzed Model Free methods are also presented. In Section 5, the conclusions resulting from the comparative analysis and the usage of algorithms for determining the initial values for the PPD and PG are presented.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 6 Approximate Complexity Discussion
### Latency  
In order to evaluate the latency of the discussed amplitude shaping algorithms, we use the concepts of \"degree of serialism\" and \"parallelization factor\" as defined in [58]. Degree of serialism is the number of loop iterations that are completed for shaping/deshaping operations. We stress that this quantity neglects the computational complexity of these iterations, and thus, the latency of the operations within each sequential processing step. Therefore, the degree of serialism can only serve as a rough indicator for latency. On the other hand, parallelization factor is the number of simultaneously possible executions of a process to complete shaping/deshaping operations.  
AC, which can be employed to realize CCDM, is by nature a highly serial algorithm, and AC-CCDM has a serialism of \(k\) for matching and \(n\) for dematching [52]. SR-DM, which is an alternative to AC-CCDM in the binary-output case [58], has a serialism of \(\min(n_{1},n-n_{1})\) and \(1\) for shaping and deshaping, respectively (In the SR-DM context, \([n_{1},n_{2}]\) is the composition of binary sequences at the output of the matcher).  
In BL-DM [57] and PDM [51], a binary-output matcher is used for each of the \(\log_{2}n_{a}=m-1\) amplitude bit levels to enable parallelization, and thus, the parallelization factor is \(\log_{2}n_{a}\). As another attempt, PA-DM uses a binary-output matcher for \(n_{a}-1\) of the \(n_{a}\) amplitudes [58], and thus, the parallelization factor is \(n_{a}-1\). A more detailed discussion on improving the parallelization of DM algorithms was provided in Section 4.1.  
The shaping and deshaping algorithms of ESS [22] and (Algorithm 1 in [23]) have a serialism of \(k\) and \(n\), respectively. On the other hand SM [23] operates based on the D&C principle as in [59], and therefore has a serialism of \(\log_{2}n\) for deshaping. Table 3 summarizes the serialism of discussed shaping schemes.
# Time Series Clustering Model based on DTW for Classifying Car Parks
## 2 Data and methodology
### Comparative Analysis of Experiments  
In this section, 9 UCR datasets ([[http://www.timeseriesclassification.com/dataset.php](http://www.timeseriesclassification.com/dataset.php)]([http://www.timeseriesclassification.com/dataset.php](http://www.timeseriesclassification.com/dataset.php))) given in Table 1 are employed to evaluate the performance of the proposed method.  
In Table 1, \(k\) is the number of clusters, _train_ is the number of objects in training dataset, _test_ is the number of objects in test dataset, and _total_ is the sum of _train_ and _test_.  
Since the training data of the 9 public datasets is relatively small, in order to compare the accuracy of the models more generally, we re-divide each dataset into training data (80%) and test data (20%). Subsequently, we compare the performance of 4 methods on these 9 datasets, and results are given in Table 2.  
Table 2 shows the purity of these 4 methods. In order to make the results more intuitively and obviously, the best results of the training data and test data are in bold, respectively, which can be concluded as two aspects:  
(1) No matter which measurement method ED and DTW is chosen, the proposed DBPAM clustering method performs better results than others.  
(2) Clustering methods based on DTW show higher purity than those based on ED.  
(3) The performance of the proposed DTW +DBPAM model is better than ED+PAM, ED+DBPAM and DTW +PAM.  
In a word, the proposed DTW +DBPAM model is effective for classifying the time series data. We adopt it to classify the car parks in the next section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \(k\) & _train_ & _test_ & _total_ \\ \hline EthanolLevel & 4 & 804 & 200 & 1004 \\ SyntheticControl & 6 & 480 & 120 & 600 \\ Strawbeerry & 2 & 787 & 196 & 983 \\ CBF & 3 & 744 & 186 & 930 \\ Beef & 5 & 48 & 12 & 60 \\ Coffee & 2 & 45 & 11 & 56 \\ SmoothSubspace & 3 & 240 & 60 & 300 \\ MoteStrain & 2 & 1018 & 254 & 1272 \\ SonyAIBORobtsurface1 & 2 & 497 & 124 & 621 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review of raw 9 UCR public datasets.
# Prediction of Corroded Pipeline Failure Pressure Based on Empirical Knowledge and Machine Learning
## 1 Introduction
This approach specifically targets structure systems such as subsea oil and gas pipelines, integrating the remaining useful life (RUL) of key components as a critical metric. Furthermore, combining empirical knowledge with data-driven models can significantly boost the predictive capability of these models. [PERSON] et al. proposed a method that integrates empirical formulas to guide the training of artificial neural networks, thereby leveraging prior knowledge to improve model performance [14]. However, empirical formulas are often derived under specific conditions and may not fully account for all influencing factors, leading to limited robustness. Consequently, while such formulas can offer valuable insights, they may impose constraints on model training if used directly, potentially limiting their effectiveness in broader applications. This paper proposes a simple but effective approach of integrating an empirical formula into the loss function to guide the optimization of a neural network model for predicting the failure pressure of corroded pipelines. The design of this loss function balances the instructive nature of the empirical formula and the powerful fitting capability of the neural network, thereby enhancing the model's predictive accuracy for failure pressure. Empirical formulas are typically designed under specific corrosion defect conditions,and their predictive performance can significantly deteriorate when applied to pipelines with different defect conditions. To address this limitation, the proposed loss function incorporates an additional defect factor term predicted by the neural network. This term compensates for the errors caused by varying defect conditions, thus endowing the neural network model optimized by this loss function with stronger adaptability to a wide range of defect scenarios. Our proposed method does not involve complex feature design or selection processes. Despite this simplification, it has been validated to achieve improved performance across various publicly available datasets.
## 5 Automatic Monitoring and Control Method for Middle Rock Pillars
### Alarm Feedback  
The stability of the middle rock pillar must be monitored in real-time to avoid possible risks. It is necessary to display the monitored stable state of the middle rock pillar at the construction site through alarm indicator lights. After remote input, calculation and processing of the displacement data monitored on-site, the judgment conclusion was obtained. It is necessary to input the conclusion back to the site to increase the feedback of the stable state of the middle rock pillar. Alarm indicator lights were arranged on the working steel frame placed by the monitoring device, and the method in Table 5 was used as the output indication information.  
When \(V\) and \(H\) conformed to the above relationship, the corresponding COLOR information code color was fed back to the indicator remotely. When COLOR was \(-1\), the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instructions** & **Indicated Value** & **Calculation Range** & **Alarm Indicator Status** \\ \hline COLOR & COLOR = \(-1\) & \(V\leq-7.0\) or \(H\leq-1.21\) & Red light on and beep \\ COLOR & COLOR = 0 & \(\frac{2}{3}V<-7.0\leq\) & Yellow light on \\  & & \(V\) or \(\frac{2}{3}H<-1.21\leq H\) & Yellow light on \\ COLOR & COLOR = 1 & \(\frac{2}{3}V<-7.0\leq\) & Yellow light flashing \\ COLOR & COLOR = 2 & \(-7.0\leq\frac{2}{3}V\) and \(-1.21\leq\frac{2}{3}H\) & Green light on \\ \hline \hline \end{tabular}
\end{table}
Table 5: Alarm feedback output form.  
field displacement value exceeded the limit value, and the red light was on and beeped. When COLOR was 0, the field displacement value was greater than the limit value of two thirds, and the alarm yellow light was always on. When COLOR was 1, the field displacement value was less than two thirds of the limit value and greater than one third of the limit value, and the alarm yellow light flashed. A COLOR of 2 indicated that the field displacement value was less than one third of the limit value, and the alarm green light was always on.  
In general, the automatic monitoring and control method carried out three-dimensional dynamic deformation monitoring of dangerous parts such as the tunnel contour section and middle rock pillar during the construction process. The real-time monitoring data upload processor controlled the stability and fracture condition of the monitored parts through the criteria, and the dynamic observation of the change in the face over a long distance can also be realized through the camera. It can cope with the occurrence of unexpected adverse events to a certain extent, and its control process is shown in Figure 12.  
Figure 12: Workflow of automatic monitoring and control of middle rock pillar.
## 5 Tailoring the Sensitivity of LSPR-Based Sensors Using Simulation Models
### Sensitivity of a Spheroid Au Nanoparticle Embedded in TiO\({}_{2}\)
As seen in Figure 21g-i, the sensitivity is improved with the AR increase even if the TiO\({}_{2}\) capping layer is surrounding the nanoparticle. For instance, prolate or oblate spheroids with an AR of 2 and a 4 nm TiO\({}_{2}\) capping layer (Figure 21h,i) have a higher sensitivity (57 nm/RIU and 74 nm/RIU) than a gold sphere without a TiO\({}_{2}\) capping layer (52 nm/RIU, Figure 21g). These sensitivity simulation results, summarized in Table 3, are the consequence of an interaction of light with single nanoparticles with a well-described shape and size. Under these circumstances, the extinction spectrum redshifts when the surrounding refractive index increases, thus giving the foundation of optical LSPR sensing for refractive index changes. Unfortunately, even with well-defined arrays of Ag or Au nanoparticles, a continuous distribution of their size and aspect ratio is obtained [233]. Furthermore, since the nanoparticles must be supported by a substrate, the sensing volume also changes. This must be kept in mind when analysing LSPR sensitivity results from an ensemble of nanoparticles, since the expected redshift of the LSPR band can result in an overall blueshift. Figure 21: Au nanoparticles with similar volumes, without or with a layer of 4 or 8 nm TiO\({}_{2}\), with different surrounding refractive indices (\(n_{s}\) = 1.0, 1.3, and 1.5) and different shapes: (**a**) sphere, AR = 1, (**b**) oblate spheroid with AR = 2, and (**c**) prolate spheroid with AR = 2. Optical extinction efficiency coefficient of the (**d**) Au spheres, (**e**) oblate spheroid, and (**f**) prolate spheroid. Plots of \(\Delta\lambda\) against \(\Delta n_{s}\) (**g**-**i**) using data taken from (**d**–**f**), respectively, to estimate the sensitivity (RIS). Simulated using nanoDDSCAT+ tool, available at nanoHuB.org [213]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Au Nanoparticle Aspect** & **TiO\({}_{2}\) Layer Thickness on Au** & \\
**Ratio and Shape** & **Nanoparticle (nm)** & **RIS (nm.RIU\({}^{-1}\))** \\ \hline
**1 (sphere)** & - & 52 \\
**1 (sphere)** & 4 & 33 \\
**1 (sphere)** & 8 & 21 \\ \hline
**2 (prolate)** & - & 158 \\
**2 (prolate)** & 4 & 74 \\
**2 (prolate)** & 8 & 46 \\ \hline
**2 (oblate)** & & 106 \\
**2 (oblate)** & 4 & 57 \\
**2 (oblate)** & 8 & 39 \\ \hline
**3 (prolate)** & - & 305 \\ \hline
**4 (prolate)** & - & 414 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Estimated RIS results for all the Au nanoparticle shapes and the TiO\({}_{2}\) overlayer thicknesses.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 2 Endodontic Biofilm Formation  
The formation of oral biofilms has a dynamic nature as a linear process that commences when free-floating bacterial cells attach to the tooth surface [15]. This attachment is followed by embedding in a self-produced matrix of extracellular polymeric substances and exhibits an altered phenotype with respect to growth rate and gene transcription of the biofilm bacteria compared with their planktonic counterparts [16]. These various phases of microbial interactions with the surface require the production of extracellular polymers that assist in initial adhesion, maintenance of the biofilm three-dimensional structure, and detachment from matrix-enclosed aggregates [17]. Microorganisms in biofilms are more resistant to elimination than those in planktonic form [18; 19].  
Most endodontic infections originate from plaque on the tooth surface and gingival crevice/periodontal pocket [20]. In the anaerobic microenvironment of endodontically infected teeth, the microorganisms colonize root canal space, bind to the root canal wall, and grow as habitat-adapted multi-species endodontic biofilms (Figure 1) [16]. An extraradicular biofilm formation could also be observed from the external root surface [21]. Regular scanning electron microscopy (SEM, Helios Nanolab 650, FEI, Eindhoven, The Netherlands) shows bacterial aggregates on the biofilm surface. A focused ion beam scanning electron microscope (FIB-SEM, Helios Nanolab 650, FEI, Eindhoven, The Netherlands) has been introduced as a useful micromachining tool for biological samples [22]. The FIB-SEM is capable of milling to remove sections for viewing of features below the readily observable surface layer [23]. Dental plaque biofilm cell layers and the abundance of extracellular polymeric substance matrix associated with bacterial cells are shown under the FIB-SEM after sequential milling (Figure 2). It is known that certain bacteria can attach to type I collagen in dentin through the expression of surface adhesins [24]. Dentinal tubules are the tunnels for bacterial invasion from the main root canal towards the dentin periphery to induce deep and persistent dentin infection [25]. Bacteria hiding in the dentin tubules are difficult to reach and eliminate by traditional disinfecting strategies, such as instrumentation and irrigation.  
Bioceramic materials have been used in a variety of endodontic applications, including vital pulp therapy, sealer-based obturation, root-end fillings, regenerative endodontics, etc. Regardless of whether used alone or together with other materials, bioceramics play an important role in adapting to the surrounding tissues and materials [14]. Innovations have been continually developed in bioceramic materials with the goal of not only being biocompatible, but also acting as a synergistic antimicrobial component against established bioceramics and the potential future formation of new endodontic biofilms [10].
# Dual-Mic Speech Enhancement Based on TF-GSC with Leakage Suppression and Signal Recovery
## 3 Proposed TF-GSC with Leakage Suppression and Signal Recovery
### Signal Recovery  
Once the desired signal is attenuated by the FBF with steering error, the following modules such as the ANC and the postfilter cannot restore it, while they can suppress the residual interference in the FBF output. In this regard, the proposed signal recovery module adds a certain amount of the appropriate microphone signal to the FBF output. To determine which microphone signal should be utilized to recover the attenuated desired signal, we evaluated which microphone signal is closer to \(Y_{FBF}\) and \(Y_{U}\), respectively. Specifically, we assessed the cosine similarities between \(X_{1}\), \(X_{2}\) and \(Y_{FBF}\), \(Y_{U}\) given by:  
\[\begin{split} S_{\mathbf{x}_{p}\mathbf{x}_{FBF}}(n)=\lambda S_{ \mathbf{x}_{p}\mathbf{x}_{FBF}}(n-1)+(1-\lambda)\frac{\text{Re}(\mathbf{X}_{ p}(n)\bullet\mathbf{Y}_{FBF}(n))}{\|\mathbf{X}_{p}(n)\|\|\mathbf{Y}_{FBF}(n)\|}\\ S_{\mathbf{x}_{p}\mathbf{x}_{U}}(n)=\lambda S_{\mathbf{x}_{p} \mathbf{x}_{U}}(n-1)+(1-\lambda)\frac{\text{Re}(\mathbf{X}_{p}(n)\bullet \mathbf{Y}_{U}(n))}{\|\mathbf{X}_{p}(n)\|\|\mathbf{Y}_{U}(n)\|}\end{split} \tag{19}\]  
in which:  
\[\begin{split}\mathbf{X}_{p}(n)=[X_{p}(n,1),\dots,X_{p}(n,K)]^{T},p=1,2\\ \mathbf{Y}_{FBF}(n)=[Y_{FBF}(n,1),\dots,Y_{FBF}(n,K)]^{T}\\ \mathbf{Y}_{U}(n)=[Y_{U}(n,1),\dots,Y_{U}(n,K)]^{T}\end{split} \tag{20}\]  
where \(\lambda\) is a smoothing parameter, \(\bullet\) is an inner product operation, \(K\) is the number of frequency bins, and \(\frac{\text{Re}(\mathbf{X}_{p}(n)\bullet\mathbf{Y}_{FBF}(n))}{\|\mathbf{X}_{ p}(n)\|\|\mathbf{Y}_{FBF}(n)\|}\) and \(\frac{\text{Re}(\mathbf{X}_{p}(n)\bullet\mathbf{Y}_{U}(n))}{\|\mathbf{X}_{p}( n)\|\|\mathbf{Y}_{U}(n)\|}\) are the cosine values of the Euclidean angles between \(\mathbf{X}_{p}(n)\) and \(\mathbf{Y}_{FBF}(n)\) and \(\mathbf{Y}_{U}(n)\), respectively [33, 34]. Using these similarities, the two similarity differences \(SD_{\mathbf{x}_{12}\mathbf{Y}_{FBF}}(n)\) and \(SD_{\mathbf{x}_{21}\mathbf{Y}_{U}}(n)\) can be computed:  
\[\begin{split} SD_{\mathbf{x}_{12}\mathbf{Y}_{FBF}}(n)=S_{\mathbf{ x}_{1}\mathbf{Y}_{FBF}}(n)-S_{\mathbf{x}_{2}\mathbf{Y}_{FBF}}(n),\\ SD_{\mathbf{x}_{21}\mathbf{Y}_{U}}(n)=S_{\mathbf{x}_{2}\mathbf{Y}_{ U}}(n)-S_{\mathbf{x}_{1}\mathbf{Y}_{U}}(n).\end{split}\] (2 in which \(G_{X}(n,k)\) determines how much of the microphone signal is added to restore the attenuated desired signal. Since adding the microphone signal in low SNR environments may be harmful, \(G_{X}(n,k)\) is designed to have higher values in higher SNRs:  
\[G_{X}(n,k)=\sqrt{\frac{\mid Y_{FBF}(n,k)\mid^{2}}{\beta\mid\bar{Y}_{U}(n,k)\mid^{ 2}+\mid Y_{FBF}(n,k)\mid^{2}}} \tag{24}\]  
where \(\beta>1\) is a tuning parameter.
## 2 Methods
### Reinforcement Learning
#### 2.2.2 Reward Function  
The formulation of the reward function plays a crucial role in the learning process. It is used to indicate the quality of the action taken by the agent after it steps to the next state. In supervised learning, the correct answer is known and fed to the neural network through backpropagation which with using an appropriate optimization method, tunes the network parameters to the direction of minimizing the error. However, in reinforcement learning, the agent receives a reward signal from the environment based on the next state which determines the quality of the taken action. Positive rewards encourage the agent to accumulate as much reward as possible whereas negative rewards incentivize the agent to reach the targeted state quickly to avoid accumulating penalties. We tested three different reward functions, the first one takes the following form:  
\[r_{t}=-k(|x_{s}-x_{us}|) \tag{3}\]  
The second one is as follows:  
\[r_{t}=-k(|x_{s}|) \tag{4}\]  
The third reward function is:  
\[r_{t}=-k_{1}(\hat{x}_{s})^{2}-k_{2}(|u|) \tag{5}\]  
During the learning process, the third reward function performed the best. The neural networks struggled to converge with the first reward function due to low and close numerical values. The second reward function performed better; however, the actor network was not able to eliminate the steady-state error of the sprung mass \(x_{s}\). The reason for this is that the system can reach zero \(\hat{x}_{s}\), therefore obtaining the maximum reward in this case without reaching the desired \(x_{s}\). This problem was solved in the third reward function by adding a small penalty on the force, which will encourage the actor to produce zero forces when \(\dot{x}_{s}\) is zero.  
In the third reward function, the vehicle body velocity is used as the indicator of the quality for the action taken; the value is squared for a gradual feedback and to benefit from the optimization properties of a convex, thus allowing the controller to know that it is improving. The signal is amplified because the range of the suspension travel being studied is within a few centimeters, and the negative sign is applied to have the reward increased as \(\dot{x}_{s}\) decreases, which therefore motivates the controller to reach the desired state as fast as possible. \(k_{1}\) and \(k_{2}\) are chosen to be 1000 and 0.1, respectively. \(k_{1}\) is chosen to have a high value to motivate the controller to reach zero velocity, while \(k_{2}\) is chosen to have a much smaller value as higher values impose limitations on the working range of controller force, which therefore will have a negative impact on the response. Other values might improve or worsen the learning process.