esa-sceva/satcom-chunk-collection huggingface dataset:
# On Analyzing Beamforming Implementation in O-RAN 5G
## 5 System Model and Simulation Results
The base station interfaces with each UE through two scenarios: the first is the BS interfaces with UE by one beam stream so that the total number of streams is \(N_{t}=K\), and the second scenario is that the BS interfaces with each UE with multi-beam stream \(N_{t}>K\); this can be achieved by allowing spatial multiplexing and multi-user MIMO. Additionally, to achieve more simplicity, we consider a Rayleigh fading channel between the BS and users with variance SNR up to 30 dB. The system diagram can be explained more simply in Figure 7.  
The channel state information beamforming method required to generate the beamforming weight vector in O-RU was based on CSI of the user \(k\), and in the case of high channel traffic, the ZF-BF allows obtaining a separate received signal at each user that minimizes the total power of the noise and, as a consequence, maximizes the signal-to-noise ratio (SNR). Obviously, the method also minimizes the inter-symbol interference (ISI) components in the output (it drives the ISI to zero in a theoretical noise-free cause interference). From Figure 8, the results show that the SINR is not sufficient when no precoding is applied, while the achievable rate achieved by the zero-forcing beamforming approaches is adjacent to the more complex digital system. Moreover, the zero-forcing beamforming can achieve a performance adjacent to the single-user scenario, which means it can reduce the interference from the multi-user environment. The simulations show the comparison in the implantation of ZF precoding in O-DU, where only four antennas served four active users, and the implantation of ZF precoding in O-RU with mMIMO channels reduces multi-user interference. The zero-forcing algorithm could achieve a high reduction of interferences in the channel interface so that the high transmission bitrate needed in the interface may meet low latency requirements by using the advantage of low latency in the front-haul interface, as shown in Figure 6. The second point to be analyzed is whether the channel information method itself also meets the low latency requirements. This is the second open issue that needs to be investigated before the deployment of beamforming in URLLC scenarios. In addition, the O-RAN has promised to provide flexibility in the network implementation, and by using the advantage of Split Option 7-2x with Category B, we could (and should) optimize the channel status information (CSI) in the front-haul. For this, several equalizers combined with machine learning technology should be investigated. At last, information from the user equipment could be used in machine learning algorithms in order to optimize (in both performance and simplicity) the functioning of the beamforming. Figure 7: Implantation of digital beamforming with ZF precoder in O-RAN.
## 2 Data and Methods
### Data-Driven Algorithms
Decision trees can be classified into classification trees and regression trees. In this paper, we established the ROP regression prediction model, so the regression decision tree is selected as the weak learner. The schematic diagram of the random forest algorithm is shown in Figure 3.  
Figure 2: SVM algorithm. When training the binary decision tree model, we need to consider how to select the cut variables and cut points and how to measure the goodness of a cut variable and cut point. The impurity of the cut nodes is generally used to measure the goodness. In [PERSON]'s work (Equations (3) and (4)) [28], the impurity is calculated as follows:  
\[G(x_{i},v_{i})=\frac{n_{l}}{N_{s}}H(X_{l})+\frac{n_{r}}{N_{s}}H(X_{r}) \tag{3}\]  
where \(x_{i}\) is a cut variable, \(v_{i}\) is a cut point, \(n_{l}\), \(n_{r}\), and \(N_{S}\) are the number of samples of the left child node, the number of samples of the right child node, and the number of samples of the parent node, \(X_{l}\), \(X_{r}\) is the set of samples of the left and right child nodes, and \(H(X)\) is the impurity function. The regression task generally uses the squared mean error (MSE) as the impurity function. In this study, the random forest algorithm was used to establish the ROP regression model, so MSE was chosen as the impurity function, and the impurity formula was calculated as follows:  
\[H(X_{m})=\frac{1}{N_{m}}\sum_{i\in N_{m}}(y-\overline{y})^{2} \tag{4}\]  
(3) Backpropagation (BP) neural network algorithm  
A backpropagation (BP) neural network is a kind of backpropagation algorithm according to error, shown in Figure 4. By using the steepest descent method and the learning rule of backpropagation, the weights and thresholds of the network are adjusted continuously so as to train a multilayer neural network with minimum errors. The training of the BP neural network consists of two processes: forward propagation and backward propagation. In the forward propagation, the training samples are input from the input layer, calculated step by step by each hidden layer, and passed to the output layer. Then, the error between the output results and the real results is calculated. If the error exceeds the given threshold, the backward propagation stage will be carried out. In the backward feedback phase, all neurons in each layer will correct the weights according to the error. The BP neural network model will repeat the above process until the error is less than the set threshold. Figure 3: The random forest algorithm.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HSTSN & hybrid satellite-terrestrial sensor network \\ DF & decode-and-forward \\ AF & amplify-and-forward \\ TDMA & time division multiple access \\ IoT & Internet of Things \\ mMTC & Massive Machine-type communications \\ ITU & International Telecommunications Union \\ NTN & Non-Terrestrial Networks \\ LOS & line-of-sight \\ QoS & quality of services \\ \end{tabular}  
Figure 10: The total capacity for the chosen relays verse numbers of potential relays for both AF and DF protocols.
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 3 Proposed Method
### Formulation
To achieve cross-view consensus, we aggregate view-specific co-association matrices through element-wise averaging to construct the unified consensus matrix as follows:  
\[\mathbf{M}=\frac{1}{V}\sum_{v=1}^{V}\mathbf{M}^{v} \tag{4}\]  
Since the unified consensus matrix provides the relationship between two samples, we use \(\mathbf{M}\) as pseudo-constraints instead of pseudo-labels to guide the feature learning process as follows:  
\[\min_{\mathbf{W}^{v}}\sum_{v=1}^{V}\left\|(\mathbf{X}^{v}\mathbf{W}^{v})(\mathbf{X}^{v}\mathbf{W}^ {v})^{\top}-\mathbf{M}\right\|_{F}^{2}+\alpha\|\mathbf{W}^{v}\|_{2,1}+\sigma\|\mathbf{W}^{ v}\|_{F}^{2} \tag{5}\]  
where \((\mathbf{X}^{v}\mathbf{W}^{v})\in\mathbb{R}^{n\times k}\) projects the data from the \(v\)-th view into a low-dimensional feature subspace, where the similarity between samples can be assessed using the linear kernel \((\mathbf{X}^{v}\mathbf{W}^{v})(\mathbf{X}^{v}\mathbf{W}^{v})^{\top}\). In addition, the composite regularization architecture incorporates hybrid regularization terms (i.e., \(L_{2,1}\)-norm and Frobenius norm) to achieve dual structural control on the projection matrix. Intuitively, the \(L_{2,1}\)-norm performs hard selection of discriminative features while the Frobenius norm softly regularizes retained features, thereby avoiding the over-sparsification problem while maintaining the benefits of dimensionality reduction. The trade-off between sparsity and generalization is explicitly controlled by \(\alpha\) and \(\sigma\).
## 3 System Model
### Optimal Number of Clusters
If the number of the constructed clusters is greater than the optimal number of clusters, the total routing traffics within each cluster will be reduced because of fewer members, however, more clusters will result in more than one-hop transmissions from the CHs to the BS also the CHs will receive data from fewer members this will reduce the local data aggregation being performed and increase the communications among the CHs.
# Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor
## 2 Problem Description  
The working principle of the CAPS used in the experiment is shown in Figure 1:  
Figure 1: Working principle of the Capacitive Angular Position Sensors (CAPS).  
Under the influence of excitation voltage [3], the sensor outputs two signals (\(U_{0}\) and \(V_{0}\)), which can be expressed as Equation (2):  
\[\begin{array}{l}U_{0}=k\cdot E\cdot\cos(\omega_{0}\cdot t)\cdot\sin(\theta),\\ V_{0}=k\cdot E\cdot\cos(\omega_{0}\cdot t)\cdot\cos(\theta).\end{array} \tag{2}\]  
where \(k\) is the gain coefficient, \(\omega_{0}\) and \(E\) are the frequency and amplitude of the excitation voltage, respectively, and \(\theta\) is the angle to be measured. Ideally, the output signals after envelop detection follow Equation (3):  
\[\begin{array}{l}U=\sin(\theta),\\ V=\cos(\theta).\end{array} \tag{3}\]  
With the presence of interference factors, output signals are defined by Equation (1). When the amplitudes of the interference terms are recognized, signal calibration can be performed based on Equation (4):  
\[\begin{array}{l}U=U_{f}=\big{(}U-\hat{b}_{1}\big{)}/\hat{a}_{1}=\sin\theta, \\ V=V_{f}=\big{(}V-\hat{b}_{2}\big{)}/(\hat{a}_{2}\cos\hat{\beta})+\big{(}U- \hat{b}_{1}\big{)}\tan\hat{\beta}/\hat{a}_{1}=\cos\theta.\end{array} \tag{4}\]  
where \(\beta\) is the identified phase shift, \(\hat{a}_{1}\) and \(\hat{a}_{2}\) are the identified amplitudes, \(\hat{b}_{1}\) and \(\hat{b}_{2}\) are the identified DC offsets.  
Our research objectives include studying the parameter identification algorithm, as well as integrating the parameter identification and angle calculation process into the digital circuit to realize self-calibration in hardware.
# Electrically Conductive Nanocomposite Fibers for Flexible and Structural Electronics
## 1 Introduction  
Smart textiles, also called smart clothing, represent an evolving new class of materials with a range of new features, such as flexibility, stretchability, and lightness that allow for many applications and designs previously not possible with the use of traditional electronic technology [1]. As clothes and textiles are in direct contact with about 90% of the skin's surface [2], smart textiles with noninvasive sensors can be used not only for aesthetic purposes, but above all to improve human living standards, health, and safety. For example, wearable electronic devices allow for the continuous monitoring of vital parameters during typical daily activities. This information can reduce the need for the patient to stay in the hospital while providing a great deal of the necessary information to determine the patient's health condition. Similar types of intelligent textiles equipped with sensors can be used in sportswear to monitor heart rate, breathing, body temperature, and other physiological parameters. Smart textiles can also be used in military, fashion, energy harvesting, and wireless data transmission [3; 4; 5; 6; 7; 8]. Given the many possible applications of smart textiles, it is highly reasonable to conduct research to develop new electronic components, materials, and manufacturing methods that can serve to broaden the applications of wearable electronics. It is particularly important to develop electrically conductive materials that meet the additional requirements of the textile industry, such as elasticity, stretchability strength, light weight, and low production cost. One of the materials groups that can fulfill the above assumptions are polymer composites with a conductive functional phase in the form of carbon structures, such as carbon nanotubes, graphite, or graphene [9]. Equally importantas the choice of conductive phase is the choice of a suitable polymer matrix, which is mainly responsible for the mechanical properties of the composite.  
Here, we introduce a manufacturing process of conductive, elastic, and small-diameter (0.2 mm) composite fibers of an unlimited length in an SBS (poly(styrene-butadiene-styrene)) matrix and CNT (carbon nanotubes), and/or graphite as a functional phase. In this study, the electrical behavior of the fabricated fibers was analyzed and described. The as-produced fibers may be sewn into the fabrics and used as a conductive path in textronics circuits or as a strain sensor, thanks to its piezoresistive properties. Thanks to its efficient processing properties, it is expected that similar composites may be suitable not only for the textile industry, but also, for example, as a filament for 3D printing.
# A New Period-Sequential Index Forecasting Algorithm for Time Series Data
###### Abstract  
A period-sequential index algorithm with sigma-pi neural network technology, which is called the (SPNN-PSI) method, is proposed for the prediction of time series datasets. Using the SPNN-PSI method, the cumulative electricity output (CEO) dataset, Volkswagen sales (VS) dataset, and electric motors exports (EME) dataset are tested. The results show that, in contrast to the moving average (MA), exponential smoothing (ES), and autoregressive integrated moving average (ARIMA) methods, the proposed SPNN-PSI method shows satisfactory forecasting quality due to lower error, and is more suitable for the prediction of time series datasets. It is also concluded that: There is a trend that the higher the correlation coefficient value of the reference historical datasets, the higher the prediction quality of SPNN-PSI method, and a higher value (\(>\)0.4) of correlation coefficient for SPNN-PSI method can help to improve occurrence probability of higher forecasting accuracy, and produce more accurate forecasts for the big datasets.  
Keywords:forecasting; time series data; period-sequential index algorithm; neural networks +
Footnote †: journal: Applied Sciences
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background
### Paradigm Shift through Multiagent-Based Approaches  
The traffic assignment problem, as defined in Section 2.1, requires the \(o\)-\(d\) matrix as input. However, experience has shown that travel time can modify the entries in the \(o\)-\(d\) matrix. A user may be discouraged from going to a given supermarket if there is congestion. Therefore, the \(o\)-\(d\) matrix is calculated iteratively. In the transportation investment project context, the traffic assignment problem is included in a four-step model [138] that follows several iterations until the convergence. In this model, the first three steps, which are trip generation, trip distribution, and mode choice, calculate the \(o\)-\(d\) matrix based on the socioeconomic data of the zones and the distance matrix between the zones. The last step is the traffic, which provides the new distance matrix. This matrix is used again for the first three stages, and so on. Agent-based models of users allow an activity-based approach [139]. Rather than assign the traffic according to the \(o\)-\(d\) matrix, each transportation user is an agent that aims to increase its utility according to a collection of activities. Time spent on transport is a decreasing function that reduces the agent's utility. The agent-based model enables agents either to learn [140] or to co-evolve in parallel [93], not only to optimize their routes and approach a kind of equilibrium but also to adapt their departure times according to traffic conditions. A noticeable description in [93] details the utility function and the co-evolution principle used in MatSim software version 0.8. Moreover, other optimizations can be launched, such as the location of stop stations and traffic-light plans [136]. However, to reach the convergence, the multi-agent learning algorithms are limited to a specific equilibrium, such as Nash, correlated, or coarse equilibrium [141; 142].
# Comparison of the Properties and Structure of Montmorillonite Modified with Polyvinylpyrrolidone
## 3 Results and Discussion
### Surface Morphology and Composition  
Since the sample MMT/PVP = 1:12 in all characteristics and structure is the closest to MMT/PVP-top, SEM images of these samples are shown in Figure 11. SEM images of the original substances are also presented. The original MMT is in the form of agglomerates 3-20 \(\upmu\)m in size, consisting of polydisperse particles 150-300 nm in size with a layered structure (Figure 11a). The particles of the original PVP have sizes from 10 to 50 \(\upmu\)m and different shapes (Figure 11b). The sample MMT/PVP = 1:12 (Figure 11c) has a completely exfoliated structure, in which individual sheets of MMT covered with PVP are evenly distributed. The MMT/PVP-top sample (Figure 11d) has a similar structure to the MMT/PVP = 1:12 sample. However, there are single non-exfoliated MMT packets. This may indicate the incomplete exfoliation of MMT in this sample and the presence of a small amount of intercalated MMT packets that positively affect the heat resistance of this sample compared to the completely exfoliated MMT/PVP = 1:12.  
EDX analysis (Table 3) confirms approximately the same amount of MMT in the samples MMT/PVP-top and MMT/PVP = 1:12. The samples contain almost equal amounts of elements Si and Al, which are absent in the original PVP.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{4}{c}{**Elemental Content, wt\%**} \\ \cline{2-5}  & **MMT** & **MMT/PVP = 1:12** &
\begin{tabular}{c} **MMT/PVP-Top** \\ **(MMT/PVP = 1:2)** \\ \end{tabular} & **PVP** \\ \hline C & 1.85 & 42.31 & 41.15 & 42.99 \\ \hline O & 45.51 & 30.09 & 30.76 & 29.18 \\ \hline Si & 31.83 & 4.35 & 4.18 & - \\ \hline N & - & 20.88 & 21.97 & 27.83 \\ \hline Al & 10.23 & 1.73 & 1.36 & - \\ \hline Fe & 2.38 & 0.23 & 0.25 & - \\ \hline Na & 0.70 & 0.11 & 0.08 & - \\ \hline Mg & 0.88 & 0.07 & - & - \\ \hline K & 6.62 & 0.23 & 0.25 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: EDX elemental compositions of modified MMT.  
Figure 10: DSC curves of MMT/PVP-top at the MMT:PVP ratio 1:2.
## 3 Material Removal Model of Abrasive Flow
### Material Removal Model in Contact Length  
The material removal depth in per contact length is defined as removal depth of workpiece surface contacting with abrasive particle, and known as linear removal rate [51; 52; 53]. As shown in Figure 5, an element of abrasive flow is cut out of boundary layer on the workpiece surface, in which \(dx\), \(dy\), and \(dz\) are the width, height and length, respectively. Because different positions have different curvatures, there is no a certain worn volume in the abrasive flow processing.  
Cutting depth of abrasive particle is less than pressed depth on the workpiece surface during processing [54]. For the cutting process, the number of abrasive particles is very great, so the remove depth cannot simply be expressed by cutting depth. When many abrasive particles participate in cutting, the cutting depth will have an average distribution. In the model in Figure 4, \(dy\) is the cutting depth of abrasive flow. Consequently, referring to Equation (6), the number of abrasive particles in the area \(dxdz\) of micro-element is approximately \(dxdzN_{a}\). When a micro-element flows from \(z_{0}\) to \(z_{0}+ds\), the normal pressure \(F_{n}\) of the abrasive flow on the abrasive particle is a certain value. According to micro-cutting wear mechanism, the material removal depth is described by:  
\[D_{l}=\frac{dy}{dz}=N_{a}\cdot\frac{K}{K_{v}}\cdot\frac{F_{n}}{H} \tag{7}\]  
where \(K_{v}\) is the material removal coefficient. Therefore, the nonlinear material removal amount is proportional to the force of abrasive flow on the particle, and is related to the curvature of workpiece surface.
# Design of a Computed Tomography Automation Architecture
## 3 Feedback Techniques
### Artifact Reduction  
The presence of artifacts in CT data can disrupt inspection processes and reducing them is an ongoing research focus. Artifacts refer to something seen in the image that is not actually there, showing up most notably during 3D visualization. [PERSON] and [PERSON] [16] focused on reducing artifacts due to the metal pieces in electronic devices. They used a triaxial rotation mechanism to allow for the adjustment of their samples. An initial single-axis rotation scan was performed as a baseline, and the total area of all-metal regions was counted in pixels for each projection. Since decreased metal area means metals are overlapping, one- or two-axis angles were changed so that the metal area increased. This comes from the idea that transmitted X-ray intensities are only saturated in a certain direction because of overlapping metals, and there could be other directions that contain regions that are not saturated. When plotting the area of metal regions vs. projection number, there existed local minima where metals were presumed to be overlapping. Optimum angles of orientation were chosen using the triaxial rotation mechanism so that these metal regions overlapped as little as possible. The maximum rotation used for the non-primary axis was 30\({}^{\circ}\) from their initial angle. Additionally, they connected the combinations of the three axes using spline interpolation and completed a control function for the triaxial rotation. A forward projection calculation was conducted based on this control function, and it was expected that the metal area on the transmitted image would decrease. For 3D reconstruction, another algorithm should be used since there are multiple axes used in scanning. They proposed an effective reconstruction algorithm based on the Feldkamp-Davis-Kress (FDK) algorithm modified for multi-axis scanning. Note that these experiments were performed only in simulation. Figure 4 represents the method used by [PERSON] and [PERSON] [16] in their study and is an example of a partial architecture that has been modified by introducing a feedback loop that serves as an added capability to the generic CT procedure in Figure 2.
## 2 Data and Methods
### Model Evaluation  
Models were calibrated with 70% of the presence/absence data, while the remaining 30% were utilized to evaluate the predictive performance of each model [90]. This evaluation involved a 10-fold bootstrap replication, employing both threshold-independent metrics, such as area under a receiver operating characteristic (ROC) curve (AUC) [91], and threshold-dependent metrics, such as the true skill statistic (TSS) [92].  
The AUC metric evaluates a model's ability to distinguish between sites where a species is present and those where it is absent. It serves as an indicator of how effectively the models prioritize areas based on their suitability as habitat for a particular species [93]. A model is deemed excellent when the AUC is greater than 0.9, good for values between 0.8 and 0.9, acceptable for values between 0.7 and 0.8, poor for values between 0.6 and 0.7, and invalid for values between 0.5 and 0.6 [90].  
The TSS is calculated as sensitivity (the proportion of observed presence to predicted presence, or true positive rate) plus specificity (the proportion of observed absence to predicted absence, or true negative rate) minus one [91]. TSS is not sensitive to prevalence, while keeping all the advantages of Kappa, such as considering omission and commission errors [92]. The TSS value ranges from \(-1\) to 1, where +1 indicates perfect agreement between observations and predictions, and values of 0 or less implies results not better than random grouping [94]. The overall workflow adopted in this study is outlined in Figure 2 below.  
Figure 2: An overview of the methodological framework applied in the predictive modeling of Acacia _docurrens_ distribution.