esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Fabrication Techniques  
### Conducting Polymers  
In the framework of OECTs, the optimization of the organic channel connecting the drain and the source is of paramount importance. This channel is made of an OSC, whose conductivity can be modulated in response to an external stimulus (typically, an electrochemical reaction occurring at the gate or directly at the channel, as described above). In their neutral state, OSCs typically exhibit low electrical conductivity. Upon chemical or electrochemical oxidation (\(p\)-doped OSC) or reduction (\(n\)-doped OSC, less frequent), mobile charge carriers are formed in the backbone of the OSC and counterions penetrate the polymer film to ensure electroneutrality. The OSC thus acquires its conducting properties. This process is ideally reversible, and the conductivity of an OSC can be modulated through a careful control of its oxidation state. We will see below that some OSCs are self-doped and are dedoped by application of a positive gate voltage (case illustrated in Figure 1, called \"_normally on_\"), whereas some other OSCs, less used nowadays, are intrinsically dedoped and are doped by application of a negative gate voltage (called \"_normally off_\").  
The development of an efficient OECT for biosensing applications, beyond the choice of the probe/target system, is intimately tied with the material used in the fabrication of the channel. Ideally, the choice of this material should follow several general guidelines. From a practical point of view, the OSC of choice should be easy to process, and exhibit low fabrication costs. For an implantable or wearable device, mechanical flexibility should also be considered. From a physical and chemical point of view, the polymer should exhibit high electrical conductivity. It should also be stable over time, and towards chemical or electrochemical modifications. Below are discussed the various organic conducting polymers which have been employed in OECTs since the emergence of this device. Their structures are given in Figure 3.
## 2 Materials and Methods
### First Scenario: Relative Energy Dissipation
Figure 5,d show the measured and predicted data for the testing phase; it is seen that the predictions and measurements were in good agreement. They had a relative error of \(\pm\)1.38%. The upstream, downstream and relative specific energy dissipations are obtained from Equations (10)-(12), respectively. \[E_{0}=H+\frac{3}{2}y_{c} \tag{10}\]  
\[E_{1}=y_{1}+\frac{q^{2}}{2 gy_{1}^{2}} \tag{11}\]  
\[\frac{\Delta E}{E_{0}}=\frac{E_{0}-E_{1}}{E_{0}} \tag{12}\]  
Figure 6 shows the relative energy dissipation versus the relative critical depth with three relative screen pore diameters. It is inferred from the figure that the values of relative energy dissipation are in good agreement with the results of [PERSON] [29] and indicate that the relative diameter of the pores of the screens has no effect on the relative energy dissipation. For the relative critical depth range of the present study, the range of the downstream Froude number of the simple vertical drop was between 3.5 and 3.9, which is typical for energy dissipation downstream of the simple vertical drop from a Type I stilling basin. Increased turbulence and two-phase mixing of water and air with horizontal screens compared to a stilling basin has reduced the relative energy dissipation. The vertical drop equipped with a horizontal screen has reduced the relative energy dissipation for all three relative diameters of the screens by 31% compared to the type I stilling basin. It is also clear from the graph that the laboratory data are very well matched with the predictions. Figure 5: _Cont._  
Figure 5: Comparison of the dependent and predicted energy dissipation values for the best model (model 3) in the first scenario. (**a**,**b**) Training; (**c**,**d**) testing.
## Materials and Methods
### Electrochemical Measurements  
The electrochemical properties were carried out using LIR 2032-type coin cells. The active material was mixed with binder acetylene black and carboxymethyl cellulose (CMC) (weight ratio of 65:20:15) in tetrahydrofuran (THF) to form a slurry. Then, the slurry was uniformly spin-coated on copper foils. The mass loading of the slurry on each copper foil (12 mm) is about 0.3-3.5 mg. The electrodes were assembled in an Ar-filled glove box using lithium foil as the counter electrode and a Celgard 2300 film as the separator. An electrolyte solution consisting of 1 M LiPF6 dissolved in ethylene (EC)/dimethyl carbonate (DMC) (1:1 by volume) was used to assemble the coin cells. Galvanostatic charge-discharge measurements were tested between 0.01 and 1.0 V at a rate of 0.05 C on the cell tester (LAND CT2001A, Wuhan, China). Cyclic voltammetry (CV) was conducted on an electrochemical workstation (Bio-logic VMP3) at a scan rate of 0.1 mVs\({}^{-1}\) in a potential range from 0.01 to 3.0 V. Electrochemical impedance spectroscopy (EIS) measurements were carried out on the same electrochemical workstation with frequencies ranging from 100 kHz to 10 MHz, with an alternating voltage of 5 mV. All electrochemical measurements were carried out at room temperature.
## 4 Liquefaction Mitigation Using Colloidal Silica Grout
### Dynamic Properties of CS-Soil Mixture
It is remarkable that the CS-treated samples need more cycles to reach 5% DA strain than the untreated samples under different effective vertical stresses, which was the case for both of the samples with 40% and 60% relative densities. Furthermore, the required cycles to reach liquefaction increased significantly with the increase of CS concentration. Taking a 5% DA shear strain as the label of onset of liquefaction, when the CS concentration ranges from 0% to 14.5%, the required cycles to initiate liquefaction remarkably increase from 2 to 17 at a cyclic stress ratio of 0.41, as shown in Figure 21.  
Several kinds of laboratory element tests have indicated that grouting CS can obviously improve the liquefaction resistance of loose sandy samples. CS gel forms in the gelation process and tends to bond sand particles together, which improve the strength of the soil structure and inhibits the generation of excess pore water pressure during the cyclic loading process. In the CS-treated ground improvement technique, CS concentration is a dominant factor that influences the strengthening effect. The higher the CS concentration is, the stronger the liquefaction resistance of the treated layers will be. In general, the investigated CS concentration in liquefaction mitigation varies from 5% to 20% in practical scenarios. Figure 19: Shear strain of the untreated and treated soil samples in an undrained cyclic torsional shear test [77]. Figure 20: Comparison of cyclic resistance for untreated and CS-treated sand [17].
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 2 Construction of the SV-DC Model
### Procedures of the EKF Algorithm
Error covariance update  
The final process of updating the error covariance can be performed using Equation (28),  
\[P(k|k)=\big{[}1-K_{g}(k)H\big{]}P(k|k-1) \tag{28}\]  
As the last stage of the EKF algorithm, it ensures that the estimate is optimum and matches the real uncertainty. The main idea of the extended Kalman filter (EKF) is to linearize the nonlinear system using a first-order Taylor expansion to approximate the state and observation equations, enabling the Kalman filter to be used for nonlinear systems. The entire process consists of four stages. First is the system prediction stage, where the dynamic model is used to predict the state at the next time step while also accounting for process noise and uncertainty. Next is the establishment of measurement equations, which combines the predicted state with the measurement model to reflect the actual observed values of the system while considering the impact of measurement noise. In the generation of the optimal estimate stage, the predicted values are combined with the observed values to adjust the state estimate, making it more accurate. This is the key step of the filtering process, as it integrates observational information with the predicted results. Finally, there is the update of the error covariance, which reflects the changes in uncertainty in the current estimate, ensuring that the estimation remains optimal and matches the actual uncertainty. The advantage of the EKF is that even for nonlinear systems, it can achieve a good estimation effect through linear approximations using the Jacobian matrix, thus maintaining high reliability and applicability when dealing with complex nonlinear problems.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 7 Performance Evaluation with Realistic Models  
This section aims to thoroughly evaluate the performance of the proposed algorithms in realistic scenarios. Some realistic models are first introduced, followed by performance comparisons and discussions.
## 4 Results and Discussion
### Experimental Setup
#### 4.1.3 Evaluation Metrics  
Precision, sensitivity, F1-score, and accuracy serve as evaluation metrics for our method. These criteria are defined as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{4}\]  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{5}\]  
\[\text{F1}-\text{score}=\frac{2\times Precision\times Sensitivity}{Precision+ Sensitivity} \tag{6}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
Precision is the percentage of actual arrhythmia rhythms predicted as arrhythmias. Sensitivity is the percentage of actual arrhythmia rhythms predicted correctly. F1 score is the harmonic mean of the precision and the recall. Accuracy measures the percentage of rhythms tested that were correctly predicted, regardless of whether they were normal or arrhythmic.
## 5 Test Results and Discussion  
In this section, the proposed methods are applied to the modified IEEE 39-bus system in view of the procedure as detailed explained in Section 3. The work is carried out in MATLAB R2023b. The computer used for simulation is equipped with an Intel i5-13400 2.50 GHz CPU at 3.40 GHz with 32 GB RAM. MATPOWER is used to calculate the DPF [37], and toolbox UQLab is adopted for BASPCE [38].
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 3 Materials and Methods
### Model Architecture  
Following the previous works [20; 21], we utilize a multi-level architecture of normalizing flow for proposed IDFlow. As shown in Figure 1, we use a three-level flow model for blur degradation modeling and a 1-level flow model for real-world noise modeling. In each level, there are 12 flow steps with a _Squeeze_ layer at the head and a _Split_ layer at the tail. The _Squeeze_ layer is a space-to-depth operator that converts a tensor with a size of \(2H\times 2W\times C\) into a tensor with size of \(H\times W\times 4C\). The _Split_ layer just splits tensor equally in the channel dimension. Each flow step consists of four different flow layers: conditional affine coupling (CAC), an affine injector (AI), invertible 1 \(\times\) 1 convolution (invConv1 \(\times\) 1), and Actnorm. More details of these flow layers can be found in [20; 21].  
Conditional Affine Coupling is an extended version of the affine coupling layer used in Glow [21]. The input feature \(\mathbf{h}^{n}\) is split equally in channel dimension into \((\mathbf{h}^{n}_{1},\mathbf{h}^{n}_{2})\) at first, the \(\mathbf{h}^{n}_{1}\) as well as condition feature \(\mathbf{u}\) is taken into a neural network to generate a scaling factor and a bias factor. The two factors are applied to normalize \(\mathbf{h}^{n}_{2}\). This layer can be expressed as  
\[\mathbf{h}^{n+1}_{2}=f^{n}_{\theta,s}(\mathbf{h}^{n}_{1};\mathbf{u})\cdot \mathbf{h}^{n}_{2}+f^{n}_{\theta,p}(\mathbf{h}^{n}_{1};\mathbf{u}),\mathbf{h} ^{n+1}_{1}=\mathbf{h}^{n}_{1},\mathbf{h}^{n+1}=(\mathbf{h}^{n+1}_{1},\mathbf{ h}^{n+1}_{2}), \tag{10}\]  
where \(f^{n}_{\theta,s}\) and \(f^{n}_{\theta,p}\) denote an arbitrary CNN network.  
The Affine Injector [20] takes condition feature \(\mathbf{u}\) to generate scaling and bias factors to normalize the input feature as \(\mathbf{h}^{n+1}=f^{n}_{\theta,s}(\mathbf{u})\cdot\mathbf{h}^{n}+f^{n}_{ \theta,p}(\mathbf{u})\).  
Invertible 1 \(\times\) 1 convolution scales feature with an invertible matrix as \(\mathbf{h}^{n+1}=W*\mathbf{h}^{n}\). Here we use the standard non-factorized formulation as in [21].  
Actnorm [21] normalizes input features in a channel dimension with a learnable scaling factor and bias factor.  
For the HR image encoder, a CNN-based network is utilized with no need to be invertible. The details of network architectures are shown in Figure 1. The RRDB denotes the residual-in-residual dense block used in [4]. We set the number of HR encoder output channel to be 64. The \(3\times 3\) convolution layer is used as default. Moreover, for the networks of \(f^{n}_{\theta,s}\) and \(f^{n}_{\theta,p}\), we use a structure of two \(3\times 3\) convolution layers with an ReLU activation layer.
## 5 Results and Validations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Schemes** & **InH Office** & **UMi Streen Canyon** \\ \hline Continuous phase shift RIS & \(\sim\)15.53 & \(\sim\)11.37 \\
1-bit discrete phase shift RIS & \(\sim\)1.8 & \(\sim\)0.537 \\
2-bit discrete phase shift RIS & \(\sim\)3.78 & \(\sim\)2.78 \\ Difference (in bps/Hz) between continuous and & & \\
1-bit discrete phase shift RIS & \(\sim\)13.73 & \(\sim\)10.84 \\ Difference (in bps/Hz) between continuous and & & \\
2-bit discrete phase shift RIS & \(\sim\)11.75 & \(\sim\)8.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of sum rate (\(\text{bps}/\text{Hz}\)) under continuous and discrete phase RIS at SNR of 90 dB and 28 GHz mmWave frequency. Figure 14: Comparison of the sum rate (\(\text{bps}/\text{Hz}\)) for two users in the UMi—street canyon at 28 GHz with continuous and discrete phase RIS.
## 2 ELM-AE and Shrinkage-Thresholding Algorithms  
### Extreme Learning Machine Autoencoder  
The ELM-AE [25] is trained with an ELM algorithm [10] in an unsupervised manner. It corresponds to a single hidden layer feedforward neural network, in which the outputs match the inputs. The ELM-AE has both a high training speed and a strong generalization ability due to the pseudo-random creation of neurons in the hidden layer and the application of a pseudoinverse matrix to calculate the weights of the output layer. To improve the performance of this method, the authors of [25] assigned random orthogonal weights and biases to the hidden layer. This approach was shown to have good generalization, and to minimize \(\|\mathbf{H}\mathbf{\beta}-\mathbf{X}\|_{2}^{2}\) and the square norm of the coefficients \(\|\mathbf{\beta}\|_{2}^{2}\). Given a set of \(N\) training samples \(\{\mathbf{x}_{i}\,|\,i\leq N\}\), where \(\mathbf{x}_{i}\in\mathbb{R}^{m}\) are both the input and output data, the output with \(L\) hidden neurons can be expressed as follows:  
\[f_{L}(\mathbf{x}_{j})=\sum_{i=1}^{L}\mathbf{\beta}_{i}g(\mathbf{w}_{i}\mathbf{x}_{j}+b_{i}), \quad j=1,\ldots N \tag{1}\]  
where \(\mathbf{w}_{i}\) and \(b_{i}\) represent the pseudorandom weights and biases of the hidden layer, \(g\) is an activation function, and \(\mathbf{\beta}_{i}\) represents the weights of the output layer. The previous overdetermined linear system can be compactly described as follows:  
\[\mathbf{H}\mathbf{\beta}=\mathbf{X}, \tag{2}\]  
where \(\mathbf{H}\in\mathbb{R}^{N\times L}\) is the hidden layer output matrix, \(\mathbf{\beta}\in\mathbb{R}^{L\times m}\) is the output layer weight matrix, and \(\mathbf{X}\in\mathbb{R}^{N\times m}\) is the input data matrix.  
Indeed, \(\mathbf{\beta}=\mathbf{H}^{\dagger}\mathbf{X}\) is the standard solution to the overdetermined problem in (2), where \(\mathbf{H}^{\dagger}=(\mathbf{H}^{T}\mathbf{H})^{-1}\mathbf{H}^{T}\) is the Moore-Penrose inverse of matrix \(\mathbf{H}\). To improve the performance of an ELM-AE, the authors of [25] determined the output weights \(\mathbf{\beta}\) by means of the equation \(\mathbf{\beta}=\left(\dfrac{\mathbf{I}}{\lambda}+\mathbf{H}^{T}\mathbf{H}\right)^{-1}\mathbf{H}^{T} \mathbf{X}\), which is the result of solving the following regularized optimization problem:  
\[\mathbf{\beta}=\text{argmin}\|\mathbf{H}\mathbf{\beta}-\mathbf{X}\|_{2}^{2}+\lambda\|\mathbf{\beta }\|_{2}^{2}, \tag{3}\]  
where \(\lambda\) is a tuning regularization parameter.
## Materials and Methods
### Adsorption Isotherns and Data Modeling
#### Improved Non-Linear Regression  
The experimental data (\(c_{\rm e}\)_vs._\(q_{\rm e}\)) were analyzed also with improved non-linear regression. To explain the potential need for this improvement let's contrast the situation with that of traditional least squares curve fitting. Traditional non-linear least square is appropriate for response models in which the y values are measured values of a parameterized function of the corresponding input value x. For each set value of x, the response y is measured multiple times, with some potential variability around the unknown function value. The values for multiple measurements of y at the input value x follow a vertical line in the y _vs_ x diagram. For the curve determination with non-linear regression, the square of the vertical residual between the measured y and the functional value y is summed, and minimized as a function of the parameters. In these models, the error is independent of the input value x.  
Adsorption isotherm curve fitting is distinct from this situation. In isotherm diagrams, the response value _vs_ input value x may be thought of via the \(q_{\rm e}\)_vs._\(c_{\rm e}\) diagrams. However, in fact, both \(q_{\rm e}\) and \(c_{\rm e}\) are measured/calculated as responses to the initial concentration \(c_{\rm o}\). If we have multiple measurement for one initial \(c_{\rm o}\), the line of multiple \(q_{\rm e}\) in the diagram will be the diagonal line due to the conservation of mass, as explained earlier. It is this constraint from the conservation of mass relationship, along with the structure of variability it induces (perfectly negative correlation between \(c_{\rm e}\) and \(q_{\rm e}\)), that can necessitate the need for improvements on the traditional least squares procedure. If we fit the curve through those data that are shown in the diagonal line, these is a unique crossing point of the diagonal with the curve. For precise curve determination that respects the mass constraint, the appropriate square of residuals between measured \(q_{\rm e}\) and curve \(q_{\rm e}\) at that crossing point should be determined. Minimizing the sum of squares of this error is what is sought to be achieved by the improved non-linear adsorption fitting program. Properties of this improved regression are discussed further in the results section.
# T-Spline Based Unifying Registration Procedure for Free-Form Surface Workpieces in Intelligent CMM
## 1 Introduction
A T-spline surface obtained using the Z-map fitting method has been proposed [27]. A previous study noted that a T-spline surface can be converted into NURBS patches, and T-spline surfaces can then be spliced into an overall surface [28]. The T-spline surface has also been fit using periodic parameterization and automatic interaction [26]. A new algorithm was proposed to transform a triangular mesh into a T-spline surface [29]. An adaptive T-spline surface fitting method based on quad-tree segmentation was proposed [30]. Another study transformed an irregular quadrilateral mesh into a T-spline surface and proposed a mesh generation method around a single point [31; 32]. Another study proposed a method for fitting the T-spline surface with curvature guidance [33]. The T-spline can be used to fit large datasets [34]. A previous study performed isogeometric analysis on an arbitrary T-spline [35; 36]. T-spline modeling technology is still being further developed, and an arbitrary order of T-spline has been analyzed in the parameter space [37]. To develop a more intelligent CMM, the current study designs a new visual inspection system. A new visual system is designed based on the characteristics of CMMs. A unifying accurate registration procedure using T-spline representation is proposed for various free-form surfaces. The practical application of the procedure is to automatically detect the positions and orientations of free-form workpieces in intelligent CMM. A discretization method of the T-spline surface formula model is proposed, where the maximum distance between adjacent points can be set. Position and orientation can be determined by the registration of the discretization of the T-spline surface formula model and the extracted point cloud, which reduces the storage space. Using the point cloud registration results as the initial solution, an overall high accuracy registration of the measured point cloud and the T-spline surface is proposed. This approach reduces human intervention and achieves free-form surface workpiece intelligent detection and error evaluation.
# Industrial IoT Monitoring: Technologies and Architecture Proposal
## 2 Technologies for Industrial IoT
### IWSN Reports
When a device wants to connect to the network, it usually sends a join request and a neighbour signal level report. Lastly, WirelessHART defines several alarm types: path down alarm; source route failed alarm; and graph route failed alarm. Similarly to what happens in WirelessHART, in ISA100.11a networks, the system manager controls all communication in the network. However, in this standard, WSN routing takes place at the DL layer, due to the use of 6 LoWPAN at the network layer. Network metrics in ISA100.11a are shared at the MAC layer, instead of being shared at the NWK layer. ISA100.11a defines two groups of network reports: (1) connectivity alert, and (2) neighbour discovery alert. Connectivity alerts comprise two types of reports: per-neighbour report and per-channel report. Per-neighbour reports contain neighbours' connection statistics. On the other hand, per-channel reports contain per-channel all-neighbours statistics, and convey them to the system manager. Finally, neighbour discovery alerts are sent periodically to the system manager with a list of overheard neighbours. The system manager makes new routing decisions based on these reports. Per-neighbour reports, per-channel reports, and neighbour discovery alert, are similar to WirelessHART's neighbour health list, device health, and neighbour signal level, respectively. Last but not least, in WIA-PA, route computation is also performed by the network gateway, similarly to WirelessHART and ISA100.11a. WIA-PA defines four types of reports: (1) device status report, (2) channel condition report, (3) neighbour report, and (4) path failure report. Device status reports include statistics about the condition of field devices and routers, such as the number of packets exchanged with neighbours, number of restarts, and uptime. Channel condition reports send statistics grouped by channel and neighbour to the network gateway. These statistics include link quality, packet loss rate, and number of retries. Neighbour reports, also received by the network gateway, group neighbour statistics and neighbour scheduling details, such as backoff counter and exponent, transmitted and received packets, and number of acknowledgments. Lastly, path failure reports are generated when a route path failure occurs. These reports are sent by a routing device to the network gateway, whenever the retransmission counter of a specific path exceeds a given threshold.